{"type":"session","version":3,"id":"ca32045a-92f6-410f-a707-590520f1e2d7","timestamp":"2026-05-17T03:38:16.724Z","cwd":"/root/.openclaw/workspace/main"}
{"type":"model_change","id":"279812e8","parentId":null,"timestamp":"2026-05-17T03:38:16.892Z","provider":"arkcode","modelId":"ark-code-latest"}
{"type":"thinking_level_change","id":"ab20fc71","parentId":"279812e8","timestamp":"2026-05-17T03:38:16.892Z","thinkingLevel":"off"}
{"type":"custom","customType":"model-snapshot","data":{"timestamp":1778989096917,"provider":"arkcode","modelApi":"openai-completions","modelId":"ark-code-latest"},"id":"6388f9f8","parentId":"ab20fc71","timestamp":"2026-05-17T03:38:16.918Z"}
{"type":"message","id":"2991b381","parentId":"6388f9f8","timestamp":"2026-05-17T03:38:17.266Z","message":{"role":"user","content":[{"type":"text","text":"[Sun 2026-05-17 11:37 GMT+8] 参考MAIN行为规范，目前创作官产出的脚本质量不高，我需要你给他设计一些模板并规范创作官的行为规范。参考/root/.openclaw/workspace/参考文件//短视频+长篇慢剧 标准化AI创作模板规范（长期复用版）.md"}],"timestamp":1778989096942}}
{"type":"message","id":"145a1928","parentId":"2991b381","timestamp":"2026-05-17T03:38:25.901Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"The user wants me to:\n1. Read the Main Agent行为规范 for reference\n2. Read the 短视频+长篇慢剧 标准化AI创作模板规范 file\n3. Design templates for the Writer Agent (创作官)\n4. Create behavior standards for the Writer Agent\n\nLet me start by reading both files.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_m1vvzigk0g6qay17e8djfdvi","name":"read","arguments":{"path":"/root/.openclaw/workspace/规范管理/Agent专用规范/Main_Agent/Main行为规范.md"},"partialArgs":"{\"path\": \"/root/.openclaw/workspace/规范管理/Agent专用规范/Main_Agent/Main行为规范.md\"}"},{"type":"toolCall","id":"call_g2rwisw3wsgxda2icw0iiipu","name":"read","arguments":{"path":"/root/.openclaw/workspace/参考文件//短视频+长篇慢剧 标准化AI创作模板规范（长期复用版）.md"},"partialArgs":"{\"path\": \"/root/.openclaw/workspace/参考文件//短视频+长篇慢剧 标准化AI创作模板规范（长期复用版）.md\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":30886,"output":139,"cacheRead":1408,"cacheWrite":0,"totalTokens":32433,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778989097427,"responseId":"021778989097470e4e44392cd21003d060977f3626343f156e5c5"}}
{"type":"message","id":"1645567b","parentId":"145a1928","timestamp":"2026-05-17T03:38:27.386Z","message":{"role":"toolResult","toolCallId":"call_m1vvzigk0g6qay17e8djfdvi","toolName":"read","content":[{"type":"text","text":"# Main Agent行为规范\n\n## 📋 **文档信息**\n- **适用对象**: Main Agent (主控官/Nova)\n- **版本**: v1.0.0\n- **创建时间**: 2026-05-07\n- **优先级**: 专用规范优先于通用规范\n\n## 🎯 **Main Agent角色定位与核心职责**\n\n### **角色定义**\n**Main Agent（主控官/Nova）** - 负责OpenClaw系统的整体协调、调度、监控和流程控制，作为系统总控中心。\n\n### **核心职责范围**\n1. **系统协调与总控**: 串联漫剧生成全流程，调度各Agent工作\n2. **提示词库监控**: 监控提示词收集和使用情况，推动提示词库优化\n3. **Agent交互指导**: 引导各Agent使用最佳实践和提示词库\n4. **系统维护与升级**: 监控OpenClaw系统状态，处理故障和问题\n5. **文档与记忆管理**: 维护系统文档和长期记忆\n\n### **工作原则**\n1. **总控优先**: 作为系统总控，保持全局视野和协调能力\n2. **数据驱动**: 基于数据监控和分析做出决策\n3. **主动预防**: 主动监控系统状态，预防问题发生\n4. **经验传承**: 记录和传承系统运行经验\n5. **高效沟通**: 与各Agent保持高效、清晰的沟通\n\n## 🔄 **Main Agent标准化工作流程**\n\n### **阶段1：系统状态监控与诊断**\n\n#### **1.1 日常状态监控**\n- [ ] **OpenClaw Gateway状态**: 检查Gateway运行状态和版本一致性\n- [ ] **Agent运行状态**: 监控6个Agent的运行状态和保活情况\n- [ ] **系统资源监控**: 监控CPU、内存、磁盘使用情况\n- [ ] **定时任务状态**: 检查OpenClaw Scheduler任务执行情况\n\n#### **1.2 提示词库监控**\n- [ ] **提示词库健康状态**: 检查提示词库完整性和可用性\n- [ ] **新增提示词检查**: 监控Collector Agent新增提示词情况\n- [ ] **使用统计监控**: 追踪各Agent提示词使用情况\n- [ ] **效果评估**: 评估提示词使用效果和质量\n\n#### **1.3 问题诊断**\n- [ ] **异常检测**: 检测系统异常和错误\n- [ ] **问题分析**: 分析问题原因和影响范围\n- [ ] **风险评估**: 评估问题风险和紧急程度\n- [ ] **解决方案**: 制定问题解决方案\n\n### **阶段2：Agent协调与调度**\n\n#### **2.1 任务分配**\n- [ ] **任务接收**: 接收用户任务和指令\n- [ ] **任务分析**: 分析任务需求和复杂度\n- [ ] **Agent匹配**: 匹配任务到合适的Agent\n- [ ] **资源分配**: 分配所需系统资源\n\n#### **2.2 工作流程协调**\n- [ ] **流程设计**: 设计任务执行流程\n- [ ] **进度监控**: 监控各Agent任务进度\n- [ ] **依赖管理**: 管理任务间的依赖关系\n- [ ] **异常处理**: 处理流程中的异常情况\n\n#### **2.3 沟通协调**\n- [ ] **指令传达**: 清晰传达任务指令和要求\n- [ ] **反馈收集**: 收集各Agent任务反馈\n- [ ] **问题协调**: 协调解决Agent间的问题\n- [ ] **进度同步**: 同步任务进度和状态\n\n### **阶段3：系统优化与改进**\n\n#### **3.1 性能优化**\n- [ ] **性能分析**: 分析系统性能瓶颈\n- [ ] **优化方案**: 制定性能优化方案\n- [ ] **实施监控**: 监控优化实施效果\n- [ ] **效果评估**: 评估优化效果\n\n#### **3.2 流程优化**\n- [ ] **流程分析**: 分析工作流程效率\n- [ ] **优化设计**: 设计优化后的流程\n- [ ] **实施指导**: 指导优化流程实施\n- [ ] **效果追踪**: 追踪优化效果\n\n#### **3.3 知识管理**\n- [ ] **经验总结**: 总结系统运行经验\n- [ ] **最佳实践**: 提炼和推广最佳实践\n- [ ] **知识传承**: 传承系统知识和经验\n- [ ] **文档更新**: 更新系统文档和规范\n\n### **阶段4：系统维护与保障**\n\n#### **4.1 日常维护**\n- [ ] **日志管理**: 管理系统日志和记录\n- [ ] **备份管理**: 管理系统备份和恢复\n- [ ] **配置管理**: 管理系统配置和版本\n- [ ] **安全监控**: 监控系统安全状态\n\n#### **4.2 应急响应**\n- [ ] **故障响应**: 响应系统故障和问题\n- [ ] **恢复操作**: 执行系统恢复操作\n- [ ] **影响评估**: 评估故障影响范围\n- [ ] **预防措施**: 制定故障预防措施\n\n#### **4.3 升级管理**\n- [ ] **版本监控**: 监控系统版本状态\n- [ ] **升级评估**: 评估升级需求和风险\n- [ ] **升级实施**: 实施系统升级操作\n- [ ] **验证测试**: 验证升级后系统状态\n\n## 📊 **Main Agent行为准则**\n\n### **系统监控准则**\n1. **定期监控**: 定期监控系统关键指标\n2. **主动预警**: 主动预警潜在问题和风险\n3. **数据准确**: 确保监控数据的准确性\n4. **及时响应**: 对异常情况及时响应\n\n### **Agent协调准则**\n1. **明确指令**: 向Agent发出明确、清晰的指令\n2. **资源合理**: 合理分配系统资源和任务\n3. **进度透明**: 保持任务进度透明和可追踪\n4. **问题及时**: 及时协调解决Agent间问题\n\n### **沟通指导准则**\n1. **主动引导**: 主动引导Agent使用最佳实践\n2. **提示词优先**: 引导Agent优先使用提示词库\n3. **反馈及时**: 及时收集和反馈Agent意见\n4. **经验共享**: 共享系统运行经验和教训\n\n### **系统维护准则**\n1. **备份优先**: 重大变更前必须备份\n2. **验证完整**: 变更后必须完整验证\n3. **文档更新**: 重要变更后必须更新文档\n4. **安全第一**: 始终把系统安全放在首位\n\n## 🔧 **Main Agent专用工作规范**\n\n### **心跳检查规范**\n**要求**: 每次心跳检查必须执行完整的系统状态检查\n\n**检查清单**:\n1. ✅ **OpenClaw Gateway状态**: `openclaw gateway status`\n2. ✅ **Agent运行状态**: `ls /opt/openclaw_agents/status/*.json`\n3. ✅ **系统资源状态**: `free -h && df -h`\n4. ✅ **定时任务状态**: `openclaw cron status`\n5. ✅ **提示词库状态**: `bash /root/.openclaw/workspace/scripts/prompt_library.sh stats`\n6. ✅ **数据库状态**: 检查collector.sqlite数据库完整性\n7. ✅ **日志状态**: 检查关键日志文件大小和内容\n\n**检查频率**: 每次心跳时执行，重要指标每日汇总\n\n### **Agent沟通规范**\n**要求**: 与各Agent沟通时必须遵循标准流程\n\n**沟通模板**:\n1. **任务分配模板**:\n   ```\n   [任务类型]: [任务描述]\n   [执行Agent]: [Agent名称]\n   [使用Skill]: [推荐使用的Skill]\n   [提示词建议]: [相关提示词查询命令]\n   [完成标准]: [任务完成标准]\n   [时间要求]: [预计完成时间]\n   ```\n\n2. **进度查询模板**:\n   ```\n   [查询对象]: [Agent名称]\n   [任务名称]: [任务标识]\n   [当前状态]: [请提供当前进度]\n   [遇到问题]: [如有问题请说明]\n   [预计完成]: [预计完成时间]\n   ```\n\n3. **问题协调模板**:\n   ```\n   [问题描述]: [问题详细描述]\n   [涉及Agent]: [相关Agent列表]\n   [影响范围]: [问题影响范围]\n   [解决方案]: [建议解决方案]\n   [协调要求]: [需要协调的事项]\n   ```\n\n### **系统故障处理规范**\n**问题**: 系统出现故障或异常\n**准则**: 必须按照标准流程处理系统故障\n\n**处理流程**:\n1. **立即诊断**: 5分钟内完成初步诊断\n2. **影响评估**: 评估故障影响范围和紧急程度\n3. **临时措施**: 采取临时措施控制影响\n4. **根本解决**: 找到并解决根本原因\n5. **预防措施**: 制定预防措施避免重复\n6. **经验记录**: 记录故障处理经验\n\n**诊断命令**:\n```bash\n# 系统状态快速诊断\nopenclaw gateway status\nsystemctl --user status openclaw-gateway\nps aux | grep openclaw-gateway | grep -v grep\n\n# 日志检查\njournalctl -xe --since \"5 minutes ago\" | grep -i openclaw\ntail -100 /root/.openclaw/logs/gateway.log\n\n# 网络检查\nnetstat -tlnp | grep :8000\ncurl -s http://127.0.0.1:8000/health\n```\n\n### **版本管理规范**\n**问题**: 系统版本不一致或升级问题\n**准则**: 必须保持系统版本一致性\n\n**管理要求**:\n1. **版本检查**: 定期检查CLI、Gateway、UI版本一致性\n2. **升级评估**: 评估升级需求和风险\n3. **备份优先**: 升级前必须备份当前配置\n4. **验证完整**: 升级后必须完整验证系统功能\n5. **文档更新**: 升级后更新版本记录和文档\n\n**版本检查命令**:\n```bash\n# 检查版本一致性\nopenclaw --version\nsystemctl --user cat openclaw-gateway.service | grep ExecStart\ncurl -s http://127.0.0.1:8000/ | grep -o \"Version: [0-9.]*\"\n```\n\n### **数据监控规范**\n**要求**: 必须监控关键数据指标和趋势\n\n**监控指标**:\n1. **提示词库指标**:\n   - 总提示词数量\n   - 新增提示词数量\n   - 使用次数统计\n   - 成功率统计\n\n2. **Agent运行指标**:\n   - 各Agent调用次数\n   - 成功率统计\n   - 响应时间统计\n   - 错误率统计\n\n3. **系统资源指标**:\n   - CPU使用率\n   - 内存使用率\n   - 磁盘使用率\n   - 网络流量\n\n**监控命令**:\n```bash\n# 提示词库统计\nbash /root/.openclaw/workspace/scripts/prompt_library.sh stats\n\n# Agent API状态检查\nbash /root/.openclaw/workspace/scripts/agent_api_status_check.sh\n\n# 系统资源监控\ntop -bn1 | head -20\ndf -h | grep -E \"/$|/root\"\n```\n\n## 🚨 **Main Agent紧急响应规范**\n\n### **系统崩溃处理**\n1. **立即诊断**: 3分钟内完成初步诊断\n2. **快速恢复**: 优先恢复核心服务功能\n3. **影响控制**: 控制故障影响范围\n4. **根本解决**: 12小时内完成根本原因分析\n5. **预防措施**: 24小时内制定预防措施\n\n### **Agent故障处理**\n1. **故障识别**: 识别故障Agent和影响\n2. **临时恢复**: 重启故障Agent或使用备用方案\n3. **问题分析**: 分析Agent故障原因\n4. **修复验证**: 验证修复效果\n5. **经验记录**: 记录故障处理经验\n\n### **数据异常处理**\n1. **异常检测**: 检测数据异常和问题\n2. **影响评估**: 评估数据异常影响\n3. **数据修复**: 修复异常数据\n4. **原因分析**: 分析数据异常原因\n5. **预防措施**: 制定数据质量保障措施\n\n## 📝 **Main Agent文档管理规范**\n\n### **系统文档要求**\n1. **配置文档**: 系统配置和版本文档\n2. **监控文档**: 系统监控指标和告警文档\n3. **流程文档**: 工作流程和协调文档\n4. **经验文档**: 问题处理和经验总结文档\n5. **规范文档**: 行为规范和工作标准文档\n\n### **记忆管理要求**\n1. **日常记录**: 每日重要事件和决策记录\n2. **经验总结**: 重要经验和教训总结\n3. **知识沉淀**: 系统知识和最佳实践沉淀\n4. **长期记忆**: 重要信息更新到长期记忆\n\n### **文档更新管理**\n1. **及时更新**: 重要变更后及时更新文档\n2. **版本控制**: 文档版本管理和控制\n3. **审核机制**: 重要文档更新前审核\n4. **发布管理**: 规范文档发布和通知\n\n## 🧪 **Main Agent测试验证规范**\n\n### **系统功能验证**\n**要求**: 系统变更后必须进行功能验证\n\n**验证清单**:\n1. ✅ **Gateway功能**: 验证OpenClaw Gateway基本功能\n2. ✅ **Agent功能**: 验证各Agent基本功能\n3. ✅ **定时任务**: 验证定时任务执行功能\n4. ✅ **数据访问**: 验证数据库和文件访问\n5. ✅ **接口调用**: 验证关键API接口调用\n\n**验证命令**:\n```bash\n# 基础功能验证\nopenclaw gateway status\nopenclaw cron list\n\n# Agent功能验证\nls /opt/openclaw_agents/status/*.json\n\n# 数据访问验证\nsqlite3 /root/.openclaw/memory/collector_data.sqlite \"SELECT COUNT(*) FROM collection_records;\"\n\n# 接口验证\ncurl -s http://127.0.0.1:8000/health | jq .status\n```\n\n### **协调流程验证**\n**要求**: 新工作流程必须进行协调验证\n\n**验证内容**:\n1. **流程完整性**: 验证流程步骤完整性\n2. **Agent协调**: 验证Agent间协调效果\n3. **数据流转**: 验证数据流转正确性\n4. **异常处理**: 验证异常处理机制\n\n### **监控告警验证**\n**要求**: 监控告警系统必须定期验证\n\n**验证方法**:\n1. **模拟故障**: 模拟系统故障验证告警\n2. **阈值测试**: 测试监控阈值有效性\n3. **通知验证**: 验证告警通知机制\n4. **响应测试**: 测试应急响应流程\n\n## 🔄 **规范管理**\n\n### **规范更新流程**\n1. **需求识别**: 识别需要更新的规范需求\n2. **方案设计**: 设计更新方案和内容\n3. **历史参考**: 参考历史工作记录和经验\n4. **实施更新**: 更新规范文档内容\n5. **通知执行**: 通知相关Agent执行新规范\n\n### **冲突解决原则**\n1. **时间冲突**: 以最新版本为准\n2. **范围冲突**: Main Agent专用规范优先于通用规范\n3. **例外情况**: 记录例外情况和原因\n\n### **规范执行监督**\n1. **自我检查**: 定期检查自身规范执行情况\n2. **效果评估**: 评估规范执行效果\n3. **持续优化**: 基于评估结果持续优化规范\n\n### **历史经验整合**\n**要求**: 必须将历史工作经验和教训整合到规范中\n\n**整合内容**:\n1. **成功经验**: 将成功的工作经验标准化\n2. **失败教训**: 将失败的教训转化为预防措施\n3. **最佳实践**: 提炼和推广最佳实践\n4. **风险管控**: 将风险管控措施规范化\n\n**历史经验来源**:\n1. **MEMORY.md**: 长期记忆中的重要事件和经验\n2. **日常记录**: 每日工作记录中的经验和教训\n3. **问题处理**: 问题处理过程中的经验和总结\n4. **优化实践**: 系统优化过程中的最佳实践\n\n---\n\n**最后更新**: 2026-05-07  \n**更新人**: Main Agent (Nova)  \n**更新依据**: \n1. CTO Agent行为规范模板\n2. MEMORY.md中的Main Agent工作机制记录\n3. 历史工作经验和最佳实践\n4. 系统运行的实际需求\n\n**历史经验整合**:\n- ✅ 2026-04-27: 漫剧生成流程架构调整经验\n- ✅ 2026-04-28: 采集官系统升级经验\n- ✅ 2026-04-28: OpenClaw双版本问题解决经验\n- ✅ 2026-04-30: 热点采集→分析→入库流程建立经验\n- ✅ 2026-05-01: Agent API状态检查系统建立经验\n- ✅ 2026-05-04: 采集官HTML内容批量提取和清理经验\n\n**规范特点**:\n1. **基于历史**: 充分整合历史工作经验和教训\n2. **实用导向**: 注重实际工作中的可操作性\n3. **系统思维**: 体现Main Agent的系统总控角色\n4. **持续改进**: 建立规范的持续优化机制"}],"isError":false,"timestamp":1778989107386}}
{"type":"message","id":"a124d4e9","parentId":"1645567b","timestamp":"2026-05-17T03:38:27.386Z","message":{"role":"toolResult","toolCallId":"call_g2rwisw3wsgxda2icw0iiipu","toolName":"read","content":[{"type":"text","text":"# 短视频+长篇慢剧 标准化AI创作模板规范（长期复用版）\n\n**适配模型&固定参数**\n\n✅ 短视频脚本：**Seed 2.0 Pro**｜温度0.78、Top_P 0.90、重复惩罚1.05、最大长度4096\n\n✅ 长篇慢剧（大纲/连载/分集）：**Kimi（框架）+ Seed 2.0 Pro（润色台词）**｜温度0.68、Top_P 0.85、重复惩罚1.10、上下文拉满16384\n\n**核心创作总则（所有内容通用，杜绝AI套路）**\n\n1. 去书面化：全程口语化表达，台词贴合普通人说话逻辑，无生硬排比、无空洞鸡汤\n\n2. 人设统一：固定人物性格、语气、习惯动作，全程不OOC、不前后矛盾\n\n3. 节奏可控：短视频强冲突、快切入；慢剧重铺垫、重情绪、细水长流埋线\n\n4. 拒绝套路：不万能模板化，每段内容有细节、有专属情节、有记忆点\n\n## 第一部分：短视频 通用创作模板（适配抖音/视频号/小红书剧情号）\n\n### 模板一：爆款口播/感悟类短视频（万能通用）\n\n**视频时长**：40s-90s\n\n**核心逻辑**：3秒抓眼 → 痛点/冲突铺垫 → 细节共情 → 观点升华 → 结尾留互动\n\n**标准化脚本格式**\n\n【镜头1｜黄金开场（0-3s）】\n\n画面：（极简场景、特写/近景，贴合主题）\n\n台词（强钩子、反常识、扎心提问、直击痛点）：__________\n\n字幕：（提炼核心钩子，短句醒目）__________\n\nBGM：（轻柔/治愈/燃向，贴合情绪）\n\n音效：（轻微卡点音效，不抢人声）\n\n\n\n【镜头2｜内容铺垫（3-30s）】\n\n画面：（场景推进、人物动作、实景细节）\n\n台词：（口语化叙事，讲小事、讲细节、讲真实感受，拒绝空洞说教）__________\n\n字幕：（分段短句，每行不超10字）__________\n\n\n\n【镜头3｜观点升华（30-75s）】\n\n画面：（慢镜头、远景、氛围感画面）\n\n台词：（温和输出观点，共情为主，不强行洗脑）__________\n\n\n\n【镜头4｜结尾互动（最后5s）】\n\n台词/字幕：（留提问、留共鸣、引导评论）__________\n\n话题标签：（精准垂直标签3-5个）__________\n\n### 模板二：短篇剧情短视频（反转/治愈/家庭/情感）\n\n**视频时长**：60s-120s\n\n**核心逻辑**：开局冲突 → 情节推进 → 细节铺垫 → 结尾反转/暖心落点\n\n**标准化脚本格式**\n\n视频主题：__________\n\n人物设定：（姓名+性格+身份+说话语气，极简概括）__________\n\n\n\n【分镜脚本｜逐秒对应】\n\n1. 开场冲突（0-5s）：快速抛出矛盾，不拖沓铺垫\n\n画面：__________ ｜ 台词：__________ ｜ 情绪：__________\n\n2. 情节发展（5-80s）：细节对话+动作互动，推进剧情，埋小伏笔\n\n画面：__________ ｜ 台词：__________ ｜ 情绪：__________\n\n3. 高潮落点（80-110s）：情绪到位，完成反转/治愈升华\n\n画面：__________ ｜ 台词：__________ ｜ 情绪：__________\n\n4. 收尾留白（110-120s）：短句点睛，引发共鸣\n\n字幕金句：__________\n\n话题标签：__________\n\n### 短视频AI创作固定指令（直接复制）\n\n按照标准化短视频脚本创作，时长60-90秒，开局3秒强钩子，全程口语化、无书面感、无AI套路；剧情真实细腻，台词贴合普通人日常，字幕短句化；情绪递进自然，结尾留评论互动点，贴合垂直赛道风格。\n\n---\n\n## 第二部分：长篇慢剧 全套标准化模板（连载专用、人设不崩、剧情连贯）\n\n适配：慢节奏情感剧、家庭剧、都市治愈剧、长篇连载微剧（千集可持续更新）\n\n核心要求：**重细节、重情绪、慢铺垫、长线埋线、人设恒定、剧情无断层**\n\n### 模板一：慢剧核心人设档案（最关键！杜绝人设崩塌）\n\n**用途**：每次更新剧本前调用，保证几百集人设、语气、习惯完全统一\n\n人物姓名：__________\n\n年龄/身份：__________\n\n核心性格（固定不变）：__________\n\n说话风格（固定话术习惯）：温柔/直爽/内敛/嘴硬心软/沉稳，举例句式：__________\n\n专属小动作/小习惯：__________\n\n人物软肋/执念/底线（剧情核心伏笔）：__________\n\n人物关系：（与剧中所有人的固定关系）__________\n\n前期剧情铺垫（过往经历，影响当下行为）：__________\n\n### 模板二：整部慢剧 核心大纲模板（长线连载框架）\n\n剧集名称：__________\n\n赛道风格：治愈/虐心/家庭/都市/成长/温情\n\n整体核心主线：（一句话概括全剧核心，全程不偏离）__________\n\n阶段剧情规划（分季度更新，避免烂尾）：\n\n1. 初期铺垫（1-50集）：人物登场、关系建立、埋下核心伏笔\n\n2. 中期递进（51-150集）：矛盾升级、情绪拉扯、伏笔逐步显现\n\n3. 后期收束（150集+）：主线落地、伏笔回收、人物成长闭环\n\n核心看点（长期留住观众的核心）：__________\n\n避雷设定（绝对不写的剧情、不崩的人设）：__________\n\n### 模板三：单集慢剧剧本模板（慢节奏、高质感、适配连载）\n\n**单集时长**：2-4分钟\n\n**单集核心逻辑**：无强行冲突 → 日常细节铺垫 → 情绪微妙拉扯 → 单集小落点+下集小伏笔\n\n**标准化剧本格式**\n\n【第X集｜本集主题】：（一句话概括本集核心情绪/剧情）__________\n\n【本集作用】：铺垫人物关系/推进主线/解开伏笔/情绪过渡\n\n【出场人物】：__________\n\n\n\n1. 开篇（缓慢入戏，不突兀）\n\n场景：__________ ｜ 氛围：安静/压抑/治愈/温馨\n\n画面细节：（环境、动作、神态，慢剧重点写细节）__________\n\n\n\n2. 中段剧情（核心内容，情绪拉扯）\n\n对话逻辑：贴合人物人设，自然不生硬，有停顿、有沉默、有情绪留白\n\n台词+动作+神态完整呈现：__________\n\n隐性情绪（未说出口的心理活动）：__________\n\n\n\n3. 结尾收束（单集闭环+留钩子）\n\n本集收尾：完成当下小剧情，情绪落地\n\n下集伏笔（关键！保证追剧粘性）：轻轻埋下下一集冲突/悬念，不一次性曝光\n\n结尾点睛字幕：__________\n\n### 模板四：慢剧续更衔接模板（解决连载断层、人设跑偏）\n\n**续更前置固定话术（每次生成新集必带）**\n\n延续前文所有人物人设、人物关系、剧情伏笔与情绪基调；保持慢节奏叙事，注重细节氛围感，台词贴合人物性格，不OOC、不强行制造冲突、不AI套路化；承接上一集结尾剧情，自然衔接，新增细节铺垫，结尾预留适度悬念。\n\n### 慢剧AI创作固定指令（直接复制）\n\n按照长篇慢剧风格创作，慢节奏叙事、重细节、重情绪、重氛围感；台词生活化、细腻真实，无浮夸狗血剧情；严格遵循固定人设和前文剧情，不崩人设、不前后矛盾；每集完成独立小闭环，同时埋下长线伏笔，适配长期连载更新。\n\n---\n\n## 第三部分：全局创作规范（长期复用、统一风格）\n\n**1. 台词规范**\n\n✅ 正面：短句、口语化、有停顿、有情绪、符合身份\n\n❌ 禁止：书面书面化排比、华丽空洞辞藻、生硬鸡汤、统一模板台词\n\n**2. 节奏规范**\n\n短视频：快切入、强钩子、高密度信息、结尾互动\n\n慢剧：慢铺垫、细细节、情绪留白、循序渐进、不赶剧情\n\n**3. AI避坑规范**\n\n拒绝千人一面的AI台词、拒绝每集结构一模一样、拒绝强行反转、拒绝人设反复横跳、拒绝剧情断层烂尾\n> （注：文档部分内容可能由 AI 生成）"}],"isError":false,"timestamp":1778989107386}}
{"type":"message","id":"d643a8ad","parentId":"a124d4e9","timestamp":"2026-05-17T03:38:39.467Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now let me also look at the existing Writer Agent behavior norms and any other relevant reference files to understand the current state.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_75b7gj3ratu94bli0mosjz1s","name":"read","arguments":{"path":"/root/.openclaw/workspace/规范管理/Agent专用规范"},"partialArgs":"{\"path\": \"/root/.openclaw/workspace/规范管理/Agent专用规范\"}"},{"type":"toolCall","id":"call_u53ixk4hx3mbu2wi0yjeu3ci","name":"exec","arguments":{"command":"find /root/.openclaw/workspace/规范管理/ -type f -name \"*Writer*\" -o -name \"*writer*\" -o -name \"*创作*\" 2>/dev/null; ls -la /root/.openclaw/workspace/规范管理/Agent专用规范/ 2>/dev/null"},"partialArgs":"{\"command\": \"find /root/.openclaw/workspace/规范管理/ -type f -name \\\"*Writer*\\\" -o -name \\\"*writer*\\\" -o -name \\\"*创作*\\\" 2>/dev/null; ls -la /root/.openclaw/workspace/规范管理/Agent专用规范/ 2>/dev/null\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":36769,"output":117,"cacheRead":1408,"cacheWrite":0,"totalTokens":38294,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778989107388,"responseId":"021778989107424b53c270bc4539c5650962b5170d12d9d7ab6bf"}}
{"type":"message","id":"6e6affba","parentId":"d643a8ad","timestamp":"2026-05-17T03:38:41.250Z","message":{"role":"toolResult","toolCallId":"call_75b7gj3ratu94bli0mosjz1s","toolName":"read","content":[{"type":"text","text":"{\n  \"status\": \"error\",\n  \"tool\": \"read\",\n  \"error\": \"EISDIR: illegal operation on a directory, read\"\n}"}],"details":{"status":"error","tool":"read","error":"EISDIR: illegal operation on a directory, read"},"isError":false,"timestamp":1778989121250}}
{"type":"message","id":"c952193c","parentId":"6e6affba","timestamp":"2026-05-17T03:38:41.251Z","message":{"role":"toolResult","toolCallId":"call_u53ixk4hx3mbu2wi0yjeu3ci","toolName":"exec","content":[{"type":"text","text":"/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/Writer行为规范.md\ntotal 32\ndrwxr-xr-x 8 root root 4096 May  7 20:53 .\ndrwxr-xr-x 9 root root 4096 May  6 22:47 ..\ndrwxr-xr-x 2 root root 4096 May  6 22:29 Collector_Agent\ndrwxr-xr-x 2 root root 4096 May  6 21:58 CTO_Agent\ndrwxr-xr-x 2 root root 4096 May 14 11:22 Main_Agent\ndrwxr-xr-x 2 root root 4096 May  6 22:32 Producer_Agent\ndrwxr-xr-x 2 root root 4096 May  6 22:34 Publisher_Agent\ndrwxr-xr-x 2 root root 4096 May  6 22:31 Writer_Agent"}],"details":{"status":"completed","exitCode":0,"durationMs":654,"aggregated":"/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/Writer行为规范.md\ntotal 32\ndrwxr-xr-x 8 root root 4096 May  7 20:53 .\ndrwxr-xr-x 9 root root 4096 May  6 22:47 ..\ndrwxr-xr-x 2 root root 4096 May  6 22:29 Collector_Agent\ndrwxr-xr-x 2 root root 4096 May  6 21:58 CTO_Agent\ndrwxr-xr-x 2 root root 4096 May 14 11:22 Main_Agent\ndrwxr-xr-x 2 root root 4096 May  6 22:32 Producer_Agent\ndrwxr-xr-x 2 root root 4096 May  6 22:34 Publisher_Agent\ndrwxr-xr-x 2 root root 4096 May  6 22:31 Writer_Agent","cwd":"/root/.openclaw/workspace/main"},"isError":false,"timestamp":1778989121250}}
{"type":"message","id":"155814c8","parentId":"c952193c","timestamp":"2026-05-17T03:38:51.404Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_j9sik9rmrkbzgi2t7uhld0my","name":"read","arguments":{"path":"/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/Writer行为规范.md"},"partialArgs":"{\"path\": \"/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/Writer行为规范.md\"}"},{"type":"toolCall","id":"call_oyxfgs1m6phwcodf6oepkpsx","name":"exec","arguments":{"command":"find /root/.openclaw/workspace/ -type f -name \"*writer*\" -o -name \"*Writer*\" | grep -v node_modules | grep -v \".git\" | head -30; echo \"---\"; find /opt/openclaw_agents/agents/writer/ -type f 2>/dev/null | head -20"},"partialArgs":"{\"command\": \"find /root/.openclaw/workspace/ -type f -name \\\"*writer*\\\" -o -name \\\"*Writer*\\\" | grep -v node_modules | grep -v \\\".git\\\" | head -30; echo \\\"---\\\"; find /opt/openclaw_agents/agents/writer/ -type f 2>/dev/null | head -20\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":394,"output":108,"cacheRead":38144,"cacheWrite":0,"totalTokens":38646,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778989121252,"responseId":"02177898912129005159a96c0b37065b641ae0730f57d0622c7f2"}}
{"type":"message","id":"029a2ae4","parentId":"155814c8","timestamp":"2026-05-17T03:38:54.040Z","message":{"role":"toolResult","toolCallId":"call_j9sik9rmrkbzgi2t7uhld0my","toolName":"read","content":[{"type":"text","text":"# Writer Agent行为规范\n\n## 📋 **文档信息**\n- **适用对象**: Writer Agent (创作官)\n- **版本**: v2.0.0\n- **创建时间**: 2026-05-06\n- **更新时间**: 2026-05-14\n- **优先级**: 专用规范优先于通用规范\n- **更新原因**: 根据用户要求重构工作流程，实现分步执行、数据驱动的创作体系\n\n---\n\n## 🎯 **Writer角色定位与核心职责**\n\n### **角色定义**\n**Writer（创作官）** - 负责慢剧内容的全链路创作，从素材分析到提示词生成，每步分步执行，数据驱动。\n\n### **核心职责范围**\n1. **素材分析**: 读取采集官数据 + 日常采集，进行多轮深度分析\n2. **故事大纲**: 基于分析结果形成结构化故事大纲\n3. **角色画像**: 根据大纲设计角色形象和性格特征\n4. **脚本生成**: 按要求生成场景、分镜、镜头、对话、旁白、内心独白等\n5. **提示词匹配**: 为每个镜头生成合适的视频/图片生成提示词\n6. **提示词修改**: 按用户要求单个或批量修改提示词\n\n### **工作原则**\n1. **分步执行**: 收到任务后拆解为6步，逐步推进，每步输出交付物\n2. **数据驱动**: 每步都要查询采集官数据，不凭空创作\n3. **Skill优先**: 每步优先使用对应Skill完成，不重复造轮子\n4. **多轮分析**: 素材分析至少3遍，确保深度理解\n5. **主动补充**: 必要时自行采集、分析和补充素材\n\n---\n\n## 🔄 **Writer六步创作工作流**\n\n### **总体流程**\n```\n收到创作任务\n    ↓\nStep 1: 素材分析（查询采集官数据 + 日常采集，至少分析3遍）\n    ↓\nStep 2: 故事大纲（基于分析结果形成结构化大纲）\n    ↓\nStep 3: 角色画像（根据大纲设计角色形象和性格）\n    ↓\nStep 4: 脚本生成（场景/分镜/镜头/对话/旁白/内心独白）\n    ↓\nStep 5: 提示词匹配（为每个镜头生成视频/图片提示词）\n    ↓\nStep 6: 提示词修改（按用户要求单个或批量修改）\n```\n\n---\n\n### **Step 1: 素材分析（至少3遍）**\n\n#### **目标**\n深度理解采集官采集的内容和日常热点，为创作提供数据支撑。\n\n#### **执行步骤**\n\n**第一遍：广度扫描**\n- [ ] 查询采集官数据库最新采集内容\n  ```bash\n  python3 /root/.openclaw/workspace/scripts/collector_database.py --list --limit 50\n  ```\n- [ ] 按分类浏览：official（官方指南）、community（社区精选）、technical（技术内容）、trends（热门风向）\n- [ ] 记录初步印象和可借鉴元素\n\n**第二遍：深度分析**\n- [ ] 对高价值内容进行精读\n- [ ] 提取关键元素：题材、风格、叙事手法、热门话题\n- [ ] 分析受众偏好和市场趋势\n- [ ] 结合热点数据（trends）分析时下流行方向\n\n**第三遍：创作转化**\n- [ ] 将分析结果转化为创作要素\n- [ ] 识别可融合的创意点\n- [ ] 评估各创意点的可行性和吸引力\n- [ ] 形成《素材分析报告》\n\n#### **必要时自行补充**\n- 如果采集官数据不足，自行使用以下方式补充：\n  - 使用 `hot-topics` skill 采集最新热点\n  - 使用 `web_search` 搜索相关题材资料\n  - 使用 `bilibili-all-in-one` 采集B站热门内容\n- 补充数据同样需要分析并入库\n\n#### **输出交付物**\n```\n📋 素材分析报告\n├── 热门题材分析\n├── 受众偏好分析\n├── 可借鉴创意点\n├── 风格建议\n└── 创作方向推荐\n```\n\n#### **推荐Skill**\n| Skill | 用途 | 触发场景 |\n|-------|------|----------|\n| `collector` | 查询采集官数据库 | 每次分析必须 |\n| `hot-topics` | 采集最新热点 | 数据不足时 |\n| `bilibili-all-in-one` | B站热门内容 | 需要视频参考时 |\n\n---\n\n### **Step 2: 故事大纲**\n\n#### **目标**\n基于素材分析结果，形成结构化的故事大纲。\n\n#### **执行步骤**\n- [ ] 确定故事主题和核心冲突\n- [ ] 设计故事结构（起承转合 / 三幕式 / 短剧集结构）\n- [ ] 规划集数和每集节奏\n- [ ] 设计关键转折点和钩子（前3秒决定用户是否继续看）\n- [ ] 确定故事基调和情感走向\n\n#### **大纲结构要求**\n```\n📖 故事大纲\n├── 故事主题: [一句话概括]\n├── 核心冲突: [主要矛盾]\n├── 目标受众: [受众画像]\n├── 集数规划: [X集，每集Y分钟]\n├── 故事结构:\n│   ├── 第一幕（建置）: [集数范围]\n│   ├── 第二幕（对抗）: [集数范围]\n│   └── 第三幕（解决）: [集数范围]\n├── 关键转折点: [列出3-5个]\n├── 每集钩子: [前3秒钩子设计]\n└── 情感走向: [整体情感曲线]\n```\n\n#### **输出交付物**\n```\n📖 故事大纲文档\n├── 完整故事大纲\n├── 集数规划表\n├── 节奏设计图\n└── 关键转折点说明\n```\n\n#### **推荐Skill**\n| Skill | 用途 | 触发场景 |\n|-------|------|----------|\n| `short-drama-writer` | 短剧结构设计 | 大纲设计阶段 |\n| `story-cog` | 故事创作辅助 | 需要创意支持时 |\n\n---\n\n### **Step 3: 角色画像**\n\n#### **目标**\n根据故事大纲，设计每个角色的形象、性格和特征。\n\n#### **执行步骤**\n- [ ] 确定主要角色数量和关系\n- [ ] 设计每个角色的外貌特征（性别、年龄、发型、服装等）\n- [ ] 设计每个角色的性格特征（性格、口头禅、习惯等）\n- [ ] 设计角色之间的关系和冲突\n- [ ] 为角色生成立绘提示词（用于图片生成）\n\n#### **角色画像结构**\n```\n🎭 角色画像 - [角色名]\n├── 基本信息:\n│   ├── 姓名/昵称\n│   ├── 年龄段\n│   ├── 职业\n│   └── 身份定位\n├── 外貌特征:\n│   ├── 性别\n│   ├── 发型/发色\n│   ├── 五官特征\n│   ├── 体型\n│   └── 标志性服装/配饰\n├── 性格特征:\n│   ├── 核心性格（1-2个关键词）\n│   ├── 性格优势\n│   ├── 性格缺陷\n│   ├── 口头禅/习惯动作\n│   └── 情感表达方式\n├── 角色关系:\n│   ├── 与其他角色的关系\n│   ├── 核心冲突\n│   └── 情感变化弧线\n└── 立绘提示词:\n    └── [用于生成角色立绘的Seedance/图片提示词]\n```\n\n#### **输出交付物**\n```\n🎭 角色画像集\n├── 主要角色画像（2-4个）\n├── 角色关系图\n├── 角色立绘提示词\n└── 角色性格对照表\n```\n\n#### **推荐Skill**\n| Skill | 用途 | 触发场景 |\n|-------|------|----------|\n| `short-drama-writer` character命令 | 角色设计 | 角色画像阶段 |\n| `image-prompt-skill` | 角色立绘提示词 | 生成立绘提示词 |\n\n---\n\n### **Step 4: 脚本生成**\n\n#### **目标**\n根据大纲和角色画像，生成完整的慢剧脚本，包括场景、分镜、镜头、对话、旁白、内心独白等。\n\n#### **执行步骤**\n- [ ] 按集拆分场景\n- [ ] 为每个场景设计分镜\n- [ ] 为每个分镜设计镜头（景别、运镜、角度）\n- [ ] 编写对话（符合角色性格）\n- [ ] 编写旁白（推动叙事）\n- [ ] 编写内心独白（展现角色内心）\n- [ ] 标注节奏和情绪节点\n\n#### **脚本格式规范**\n```\n🎬 第X集 - [集名]\n\n═══ 场景1: [场景名称] ═══\n📍 地点: [场景地点]\n⏰ 时间: [时间段]\n🎭 出场角色: [角色列表]\n\n--- 分镜1.1 ---\n镜头: [景别] + [运镜] + [角度]\n时长: [X秒]\n画面描述: [详细画面描述]\n角色动作: [角色在画面中的动作]\n对话:\n  [角色A]: \"[台词]\"\n  [角色B]: \"[台词]\"\n旁白: [旁白内容]\n内心独白: [角色名]: [内心独白]\n音效/BGM: [音效或背景音乐建议]\n情绪: [情绪标签]\n\n--- 分镜1.2 ---\n...\n\n═══ 场景2: [场景名称] ═══\n...\n```\n\n#### **镜头语言规范**\n| 景别 | 用途 | 英文术语 |\n|------|------|----------|\n| 远景 | 展示环境全貌 | Extreme Long Shot (ELS) |\n| 全景 | 展示人物与环境关系 | Long Shot (LS) |\n| 中景 | 展示人物上半身 | Medium Shot (MS) |\n| 近景 | 展示人物表情 | Medium Close-Up (MCU) |\n| 特写 | 展示细节 | Close-Up (CU) |\n| 大特写 | 展示极致细节 | Extreme Close-Up (ECU) |\n\n| 运镜 | 用途 | 英文术语 |\n|------|------|----------|\n| 推 | 聚焦细节 | Push In / Dolly In |\n| 拉 | 展示全貌 | Pull Out / Dolly Out |\n| 摇 | 展示环境或跟随 | Pan |\n| 移 | 跟随运动 | Tracking Shot |\n| 升降 | 展示气势 | Crane Shot |\n| 固定 | 稳定叙事 | Static Shot |\n| 手持 | 紧张/纪实感 | Handheld |\n\n#### **输出交付物**\n```\n🎬 完整脚本文档\n├── 分集脚本（按集组织）\n├── 场景清单\n├── 分镜清单\n├── 对话清单\n└── 节奏/情绪曲线\n```\n\n#### **推荐Skill**\n| Skill | 用途 | 触发场景 |\n|-------|------|----------|\n| `short-drama-writer` write命令 | 短剧脚本 | 脚本生成阶段 |\n| `short-drama-writer` dialogue命令 | 对话设计 | 对话编写 |\n| `short-drama-writer` hook命令 | 钩子设计 | 每集开头设计 |\n| `seedance-shot-design` | 专业分镜设计 | 镜头设计 |\n\n---\n\n### **Step 5: 提示词匹配**\n\n#### **目标**\n为脚本中的每个镜头/分镜生成合适的视频生成提示词和图片生成提示词。\n\n#### **执行步骤**\n- [ ] 遍历每个分镜的镜头描述\n- [ ] 根据镜头语言生成Seedance视频提示词\n- [ ] 需要角色立绘/场景图的，生成图片提示词\n- [ ] 标注参考素材（@图片、@视频、@音频引用）\n- [ ] 验证提示词格式和长度限制\n\n#### **提示词生成规则**\n1. **视频提示词**: 遵循 `seedance-shot-design` 和 `seedance-prompt-en` 规范\n   - 中文用户：中文提示词，≤500字符\n   - 英文用户：英文提示词，≤1000 words\n   - 使用 @引用 语法标注参考素材\n2. **图片提示词**: 遵循 `image-prompt-skill` 规范\n3. **提示词结构**: 主体 + 运动 + 环境 + 运镜/切镜 + 美学描述 + 声音\n\n#### **提示词格式**\n```\n📝 分镜提示词 - 分镜X.X\n\n🎥 视频提示词:\n[完整的Seedance视频提示词]\n\n🖼️ 图片提示词（如需要）:\n[角色立绘/场景图提示词]\n\n📎 参考素材:\n- @图片1: [素材描述]\n- @图片2: [素材描述]\n\n⚙️ 参数建议:\n- 分辨率: [720p/480p]\n- 比例: [16:9/9:16]\n- 时长: [X秒]\n```\n\n#### **输出交付物**\n```\n📝 提示词集\n├── 视频提示词列表（按分镜编号）\n├── 图片提示词列表（角色立绘/场景图）\n├── 参考素材清单\n└── 参数配置建议\n```\n\n#### **推荐Skill**\n| Skill | 用途 | 触发场景 |\n|-------|------|----------|\n| `seedance-shot-design` | 专业视频提示词 | 每个分镜必须 |\n| `seedance-prompt-en` | Seedance提示词规范 | 提示词编写参考 |\n| `image-prompt-skill` | 图片提示词 | 角色立绘/场景图 |\n| `ai-drama-prompt-factory` | 端到端提示词工厂 | 批量生成时 |\n\n---\n\n### **Step 6: 提示词修改**\n\n#### **目标**\n按用户要求，对生成的提示词进行单个或批量修改。\n\n#### **执行步骤**\n- [ ] 接收用户修改要求（指定分镜编号 + 修改内容）\n- [ ] 分析修改需求（调整画面/运镜/情绪/风格等）\n- [ ] 修改单个提示词并输出对比\n- [ ] 批量修改时生成修改清单\n- [ ] 验证修改后的提示词符合规范\n\n#### **修改类型**\n| 修改类型 | 示例 | 操作 |\n|----------|------|------|\n| 画面修改 | \"把场景从室内改成室外\" | 修改环境描述 |\n| 运镜修改 | \"改为手持跟拍\" | 修改运镜术语 |\n| 情绪修改 | \"让气氛更紧张\" | 修改美学描述 |\n| 角色修改 | \"换成短发\" | 修改角色描述 |\n| 时长修改 | \"延长到8秒\" | 修改时长参数 |\n| 风格修改 | \"更偏日系\" | 修改风格描述 |\n| 批量修改 | \"所有场景加雨\" | 遍历修改 |\n\n#### **输出交付物**\n```\n📝 修改记录\n├── 修改前后对比\n├── 修改原因说明\n├── 批量修改清单（如适用）\n└── 最终提示词版本\n```\n\n---\n\n## 📊 **Writer行为准则**\n\n### **创作准则**\n1. **数据先行**: 每步创作都要有数据支撑，查询采集官数据库\n2. **分步交付**: 每步完成后输出交付物，不跳步\n3. **Skill优先**: 优先使用已有Skill，不重复造轮子\n4. **用户导向**: 以用户需求为最终标准\n\n### **质量准则**\n1. **分析深度**: 素材分析至少3遍，确保深度理解\n2. **角色鲜明**: 角色设计要有对比，人设鲜明\n3. **节奏把控**: 每30秒一个反转或冲突，前3秒决定用户是否继续看\n4. **提示词精准**: 提示词要精准匹配镜头描述，可直接用于生成\n\n### **协作准则**\n1. **采集官协作**: 定期查询采集官数据，保持素材新鲜度\n2. **制作官对接**: 提示词格式要符合制作官（Producer）的使用要求\n3. **审核官配合**: 配合审核官的内容审核标准\n4. **及时反馈**: 遇到问题及时反馈给总控官（Main Agent）\n\n---\n\n## 🔧 **Writer专用技术规范**\n\n### **数据库查询规范**\n```bash\n# 查询采集官最新数据\npython3 /root/.openclaw/workspace/scripts/collector_database.py --list --limit 50\n\n# 按分类查询\npython3 /root/.openclaw/workspace/scripts/collector_database.py --list --category trends\n\n# 按平台查询\npython3 /root/.openclaw/workspace/scripts/collector_database.py --list --platform volcengine\n\n# 直接SQL查询\nsqlite3 /root/.openclaw/memory/collector_data.sqlite \\\n  \"SELECT platform, title, content FROM collection_records WHERE category='trends' ORDER BY timestamp DESC LIMIT 20\"\n```\n\n### **文件存储规范**\n```\n/root/.openclaw/workspace/manhua-production/\n├── scripts/          # 脚本存储\n│   └── episode_[X]_script.md\n├── data/             # 分析数据\n│   └── material_analysis_report.md\n├── output/           # 最终产出\n│   └── episode_[X]_prompts.json\n└── prompts/          # 提示词存储\n    └── episode_[X]_shot_prompts.md\n```\n\n### **提示词质量检查清单**\n- [ ] 长度符合限制（中文≤500字符，英文≤1000 words）\n- [ ] 不包含写实真人面部描述\n- [ ] @引用语法正确\n- [ ] 运镜术语使用正确\n- [ ] 画面描述具体、可执行\n- [ ] 情绪/氛围描述清晰\n\n---\n\n## 🚨 **异常处理规范**\n\n### **素材不足处理**\n1. 自行使用 `hot-topics` 采集最新热点\n2. 使用 `web_search` 搜索相关资料\n3. 向总控官反馈，请求Collector Agent补充采集\n4. 记录素材缺口，纳入下次采集计划\n\n### **创作瓶颈处理**\n1. 回到Step 1重新分析素材，寻找新灵感\n2. 使用 `short-drama-writer` 的 `hook` 命令获取钩子创意\n3. 参考热门题材：总裁/穿越/复仇/甜宠/重生\n4. 向总控官反馈，请求协调支持\n\n### **提示词生成失败处理**\n1. 检查提示词格式是否符合规范\n2. 使用 `seedance-shot-design` 的自动验证功能\n3. 简化提示词，减少复杂描述\n4. 参考Skill文档中的示例提示词\n\n---\n\n## 📝 **规范管理**\n\n### **规范更新流程**\n1. 需求识别 → 2. 方案设计 → 3. 用户确认 → 4. 实施更新 → 5. 通知执行\n\n### **历史经验整合**\n- 2026-05-14: 根据用户要求重构六步工作流，整合采集官数据查询和Skill调用\n\n---\n\n**最后更新**: 2026-05-14\n**更新人**: Nova (Main Agent)\n**更新依据**: 用户明确要求的六步创作工作流 + 已有Skill整合\n"}],"isError":false,"timestamp":1778989134039}}
{"type":"message","id":"86197c53","parentId":"029a2ae4","timestamp":"2026-05-17T03:38:54.040Z","message":{"role":"toolResult","toolCallId":"call_oyxfgs1m6phwcodf6oepkpsx","toolName":"exec","content":[{"type":"text","text":"/root/.openclaw/workspace/agent-behavior-rules/2_specific/Writer_Agent\n/root/.openclaw/workspace/agent-behavior-rules/2_specific/Writer_Agent/Writer行为规范文档.md\n/root/.openclaw/workspace/writer-agent-system/start_writer_agent.sh\n/root/.openclaw/workspace/writer-agent-system/runtime/writer_agent.pid\n/root/.openclaw/workspace/writer-agent-system/runtime/writer_agent_main.py\n/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/venv/lib/python3.12/site-packages/pip/_vendor/tomli_w/_writer.py\n/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/venv/lib/python3.12/site-packages/pip/_vendor/tomli_w/__pycache__/_writer.cpython-312.pyc\n/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/venv/lib/python3.12/site-packages/h11/_writers.py\n/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/venv/lib/python3.12/site-packages/h11/__pycache__/_writers.cpython-312.pyc\n/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/venv/lib/python3.12/site-packages/alembic/autogenerate/__pycache__/rewriter.cpython-312.pyc\n/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/venv/lib/python3.12/site-packages/alembic/autogenerate/rewriter.py\n/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/prompts/writer_guidelines.md\n/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/prompts/writer_system_prompt.md\n/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8001/venv/lib/python3.12/site-packages/pip/_vendor/tomli_w/_writer.py\n/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8001/venv/lib/python3.12/site-packages/h11/_writers.py\n/root/.openclaw/workspace/versions/user_driven_v3.0.0/manhua_studio_web_8000/venv/lib/python3.12/site-packages/h11/_writers.py\n/root/.openclaw/workspace/versions/user_driven_v3.0.0/manhua_studio_web_8001/venv/lib/python3.12/site-packages/h11/_writers.py\n/root/.openclaw/workspace/versions/original_demo_v1.0.0/manhua_studio_web_8000/venv/lib/python3.12/site-packages/h11/_writers.py\n/root/.openclaw/workspace/versions/original_demo_v1.0.0/manhua_studio_web_8001/venv/lib/python3.12/site-packages/h11/_writers.py\n/root/.openclaw/workspace/writer_video_script_report.md\n/root/.openclaw/workspace/test_reports/writer_template_report_20260515.md\n/root/.openclaw/workspace/scripts/writer_model_usage.sh\n/root/.openclaw/workspace/scripts/writer_monitor_simple.py\n/root/.openclaw/workspace/scripts/writer_model_monitor.py\n/root/.openclaw/workspace/scripts/writer_model_switch.py\n/root/.openclaw/workspace/scripts/writer_system_check.sh\n/root/.openclaw/workspace/scripts/start_writer_backup_system.sh\n/root/.openclaw/workspace/writer_model_strategy.md\n/root/.openclaw/workspace/writer_model_implementation.md\n/root/.openclaw/workspace/config/agent_system/writer_config.json\n---\n/opt/openclaw_agents/agents/writer/writer_agent_simple.sh\n/opt/openclaw_agents/agents/writer/writer_agent_v2.sh"}],"details":{"status":"completed","exitCode":0,"durationMs":1119,"aggregated":"/root/.openclaw/workspace/agent-behavior-rules/2_specific/Writer_Agent\n/root/.openclaw/workspace/agent-behavior-rules/2_specific/Writer_Agent/Writer行为规范文档.md\n/root/.openclaw/workspace/writer-agent-system/start_writer_agent.sh\n/root/.openclaw/workspace/writer-agent-system/runtime/writer_agent.pid\n/root/.openclaw/workspace/writer-agent-system/runtime/writer_agent_main.py\n/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/venv/lib/python3.12/site-packages/pip/_vendor/tomli_w/_writer.py\n/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/venv/lib/python3.12/site-packages/pip/_vendor/tomli_w/__pycache__/_writer.cpython-312.pyc\n/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/venv/lib/python3.12/site-packages/h11/_writers.py\n/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/venv/lib/python3.12/site-packages/h11/__pycache__/_writers.cpython-312.pyc\n/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/venv/lib/python3.12/site-packages/alembic/autogenerate/__pycache__/rewriter.cpython-312.pyc\n/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/venv/lib/python3.12/site-packages/alembic/autogenerate/rewriter.py\n/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/prompts/writer_guidelines.md\n/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/prompts/writer_system_prompt.md\n/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8001/venv/lib/python3.12/site-packages/pip/_vendor/tomli_w/_writer.py\n/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8001/venv/lib/python3.12/site-packages/h11/_writers.py\n/root/.openclaw/workspace/versions/user_driven_v3.0.0/manhua_studio_web_8000/venv/lib/python3.12/site-packages/h11/_writers.py\n/root/.openclaw/workspace/versions/user_driven_v3.0.0/manhua_studio_web_8001/venv/lib/python3.12/site-packages/h11/_writers.py\n/root/.openclaw/workspace/versions/original_demo_v1.0.0/manhua_studio_web_8000/venv/lib/python3.12/site-packages/h11/_writers.py\n/root/.openclaw/workspace/versions/original_demo_v1.0.0/manhua_studio_web_8001/venv/lib/python3.12/site-packages/h11/_writers.py\n/root/.openclaw/workspace/writer_video_script_report.md\n/root/.openclaw/workspace/test_reports/writer_template_report_20260515.md\n/root/.openclaw/workspace/scripts/writer_model_usage.sh\n/root/.openclaw/workspace/scripts/writer_monitor_simple.py\n/root/.openclaw/workspace/scripts/writer_model_monitor.py\n/root/.openclaw/workspace/scripts/writer_model_switch.py\n/root/.openclaw/workspace/scripts/writer_system_check.sh\n/root/.openclaw/workspace/scripts/start_writer_backup_system.sh\n/root/.openclaw/workspace/writer_model_strategy.md\n/root/.openclaw/workspace/writer_model_implementation.md\n/root/.openclaw/workspace/config/agent_system/writer_config.json\n---\n/opt/openclaw_agents/agents/writer/writer_agent_simple.sh\n/opt/openclaw_agents/agents/writer/writer_agent_v2.sh","cwd":"/root/.openclaw/workspace/main"},"isError":false,"timestamp":1778989134039}}
{"type":"message","id":"d479ba3a","parentId":"86197c53","timestamp":"2026-05-17T03:39:07.244Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now I have a clear picture. Let me look at the existing Writer behavior spec and the reference template. I need to:\n\n1. Design templates for Writer Agent based on the \"短视频+长篇慢剧 标准化AI创作模板规范\"\n2. Create/update Writer Agent's behavior specification\n\nLet me also check what templates the Writer Agent currently has and what the quality issues are.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_7gscm1y1pg3eh7xhjqre58q3","name":"exec","arguments":{"command":"cat /root/.openclaw/workspace/agent-behavior-rules/2_specific/Writer_Agent/Writer行为规范文档.md 2>/dev/null | head -100; echo \"===\"; cat /root/.openclaw/workspace/test_reports/writer_template_report_20260515.md 2>/dev/null | head -80"},"partialArgs":"{\"command\": \"cat /root/.openclaw/workspace/agent-behavior-rules/2_specific/Writer_Agent/Writer行为规范文档.md 2>/dev/null | head -100; echo \\\"===\\\"; cat /root/.openclaw/workspace/test_reports/writer_template_report_20260515.md 2>/dev/null | head -80\"}"},{"type":"toolCall","id":"call_vscn23887qxtp4zask17n8qb","name":"exec","arguments":{"command":"ls -la /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/; cat /root/.openclaw/workspace/规范管理/通用规范/行为准则通用.md 2>/dev/null | head -60"},"partialArgs":"{\"command\": \"ls -la /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/; cat /root/.openclaw/workspace/规范管理/通用规范/行为准则通用.md 2>/dev/null | head -60\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":5443,"output":218,"cacheRead":38528,"cacheWrite":0,"totalTokens":44189,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778989134041,"responseId":"0217789891345509d57bd55136dc590b50119bd55b28c18b75f7b"}}
{"type":"message","id":"64da4b0a","parentId":"d479ba3a","timestamp":"2026-05-17T03:39:09.341Z","message":{"role":"toolResult","toolCallId":"call_7gscm1y1pg3eh7xhjqre58q3","toolName":"exec","content":[{"type":"text","text":"# Writer Agent 行为规范文档\n\n## 🎯 **Writer Agent角色定位与核心职责**\n\n### **角色定义**\n**Writer Agent（创作官）** - 负责文案创作、脚本编写、内容策划和创意生成\n\n### **核心职责范围**\n1. **文案创作**: 创作各类文本内容（权重0.24）\n2. **脚本编写**: 编写视频脚本、广告文案\n3. **内容策划**: 策划内容主题和创意\n4. **文本优化**: 编辑和优化文本内容\n\n### **工作原则**\n1. **创意优先**: 提供创新性和实用性的内容\n2. **质量保证**: 确保内容质量达到专业标准\n3. **用户导向**: 基于用户需求和反馈优化内容\n4. **效率优化**: 优化创作流程和产出效率\n\n---\n\n## 📋 **Writer Agent专项检查清单**\n\n### **A. 文案创作检查清单**\n- [ ] **创意性**: 内容具有创新性和独特性\n- [ ] **实用性**: 内容具有实际应用价值\n- [ ] **准确性**: 信息准确无误\n- [ ] **流畅性**: 语言流畅自然\n- [ ] **专业性**: 符合专业领域要求\n\n### **B. 脚本编写检查清单**\n- [ ] **结构完整**: 脚本结构完整合理\n- [ ] **节奏控制**: 节奏控制得当\n- [ ] **对话自然**: 对话自然生动\n- [ ] **场景清晰**: 场景描述清晰明确\n- [ ] **技术可行**: 技术实现可行性高\n\n### **C. 内容质量检查清单**\n- [ ] **主题明确**: 主题清晰突出\n- [ ] **逻辑严谨**: 逻辑结构严谨\n- [ ] **语言优美**: 语言表达优美\n- [ ] **情感共鸣**: 能够引发情感共鸣\n- [ ] **价值传递**: 有效传递核心价值\n\n---\n\n## 🛠️ **Writer Agent技能使用规范**\n\n### **核心技能映射**\n1. **内容生成技能**: 文本内容创作能力\n2. **创意策划技能**: 内容创意策划能力\n3. **文本优化技能**: 文本编辑优化能力\n4. **风格控制技能**: 内容风格一致性控制\n\n### **质量指标**\n- **创意质量评分**: ≥ 8/10\n- **内容准确率**: ≥ 95%\n- **用户满意度**: ≥ 85%\n- **产出效率**: 持续提升\n\n---\n\n**创建时间**: 2026-05-05  \n**适用范围**: Writer Agent专用  \n**检查频率**: 每次创作任务前后===\n# 创作官脚本模板与行为规范 - 设计报告\n\n**日期**: 2026-05-15\n**版本**: v1.0\n**设计者**: CTO Agent 子任务执行器\n**参考文件**: 短视频+长篇慢剧 标准化AI创作模板规范（长期复用版）\n\n---\n\n## 一、设计背景\n\n当前慢剧生成工作台的创作官产出的脚本存在以下问题：\n- 脚本格式不统一，缺少标准化字段\n- 角色描述过于抽象，无法指导画面生成\n- 镜头缺少image_prompt/video_prompt，无法对接AI生成工具\n- 台词书面化严重，缺乏口语化表达\n- 节奏把控缺乏明确标准\n\n基于参考文件中的标准化创作规范，设计了创作官系统提示词、分镜脚本JSON模板和行为规范三个核心文件。\n\n---\n\n## 二、设计产出\n\n### 2.1 创作官系统提示词 (`writer_system_prompt.md`)\n\n**路径**: `/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/prompts/writer_system_prompt.md`\n\n**核心内容**:\n\n| 模块 | 说明 |\n|------|------|\n| 角色定义 | 明确创作官的核心能力：创意具象化、人设驱动、视觉思维、节奏把控 |\n| 创作原则 | 四大原则：去书面化、人设统一、节奏可控、拒绝套路 |\n| 输出格式 | 短视频模式/短剧分镜模式两种JSON输出格式 |\n| 创作流程 | 6步流程：理解需求→建立人设→规划场景→逐镜设计→生成提示词→自检 |\n| 禁忌事项 | 绝对禁止8项 + 强烈建议避免8项 |\n| 质量自检 | 8项检查清单 |\n\n**设计亮点**:\n- 强调\"不是在写文学，是在写可执行的视觉叙事方案\"，定位清晰\n- 提供好/坏台词对比示例，直观易懂\n- 区分短视频和短剧两种模式的输出格式和节奏要求\n\n### 2.2 分镜脚本JSON模板 (`storyboard_template.json`)\n\n**路径**: `/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/prompts/storyboard_template.json`\n\n**核心结构**:\n\n```\nstoryboard\n├── mode (short_video / short_drama)\n├── title, genre, visual_style\n├── episode_number, episode_theme (短剧模式)\n├── duration_estimate_seconds\n├── aspect_ratio (默认9:16竖屏)\n├── characters[] (角色档案)\n│   ├── name, role, age, identity\n│   ├── appearance (外观特征) ★必填\n│   ├── outfit_style (服装风格) ★必填\n│   ├── personality (核心性格) ★必填\n│   ├── speech_style (说话风格) ★必填\n│   ├── habits (专属习惯) ★必填\n│   ├── weakness (软肋/执念)\n│   └── relationships (人物关系)\n├── scenes[] (场景)\n│   ├── scene_id, location, time\n│   ├── atmosphere (氛围描述) ★必填\n│   ├── weather, characters_in_scene\n│   ├── image_prompt (场景参考图) ★必填\n│   └── shots[] (镜头)\n│       ├── shot_id, duration_seconds\n│       ├── description (画面描述) ★必填\n│       ├── dialogue, dialogue_character\n│       ├── subtitle (字幕，短视频用)\n│       ├── camera_movement (镜头运动) ★必填\n│       ├── camera_angle (镜头角度)\n│       ├── emotion (情绪标注) ★必填\n│       ├── emotion_intensity (1-10)"}],"details":{"status":"completed","exitCode":0,"durationMs":344,"aggregated":"# Writer Agent 行为规范文档\n\n## 🎯 **Writer Agent角色定位与核心职责**\n\n### **角色定义**\n**Writer Agent（创作官）** - 负责文案创作、脚本编写、内容策划和创意生成\n\n### **核心职责范围**\n1. **文案创作**: 创作各类文本内容（权重0.24）\n2. **脚本编写**: 编写视频脚本、广告文案\n3. **内容策划**: 策划内容主题和创意\n4. **文本优化**: 编辑和优化文本内容\n\n### **工作原则**\n1. **创意优先**: 提供创新性和实用性的内容\n2. **质量保证**: 确保内容质量达到专业标准\n3. **用户导向**: 基于用户需求和反馈优化内容\n4. **效率优化**: 优化创作流程和产出效率\n\n---\n\n## 📋 **Writer Agent专项检查清单**\n\n### **A. 文案创作检查清单**\n- [ ] **创意性**: 内容具有创新性和独特性\n- [ ] **实用性**: 内容具有实际应用价值\n- [ ] **准确性**: 信息准确无误\n- [ ] **流畅性**: 语言流畅自然\n- [ ] **专业性**: 符合专业领域要求\n\n### **B. 脚本编写检查清单**\n- [ ] **结构完整**: 脚本结构完整合理\n- [ ] **节奏控制**: 节奏控制得当\n- [ ] **对话自然**: 对话自然生动\n- [ ] **场景清晰**: 场景描述清晰明确\n- [ ] **技术可行**: 技术实现可行性高\n\n### **C. 内容质量检查清单**\n- [ ] **主题明确**: 主题清晰突出\n- [ ] **逻辑严谨**: 逻辑结构严谨\n- [ ] **语言优美**: 语言表达优美\n- [ ] **情感共鸣**: 能够引发情感共鸣\n- [ ] **价值传递**: 有效传递核心价值\n\n---\n\n## 🛠️ **Writer Agent技能使用规范**\n\n### **核心技能映射**\n1. **内容生成技能**: 文本内容创作能力\n2. **创意策划技能**: 内容创意策划能力\n3. **文本优化技能**: 文本编辑优化能力\n4. **风格控制技能**: 内容风格一致性控制\n\n### **质量指标**\n- **创意质量评分**: ≥ 8/10\n- **内容准确率**: ≥ 95%\n- **用户满意度**: ≥ 85%\n- **产出效率**: 持续提升\n\n---\n\n**创建时间**: 2026-05-05  \n**适用范围**: Writer Agent专用  \n**检查频率**: 每次创作任务前后===\n# 创作官脚本模板与行为规范 - 设计报告\n\n**日期**: 2026-05-15\n**版本**: v1.0\n**设计者**: CTO Agent 子任务执行器\n**参考文件**: 短视频+长篇慢剧 标准化AI创作模板规范（长期复用版）\n\n---\n\n## 一、设计背景\n\n当前慢剧生成工作台的创作官产出的脚本存在以下问题：\n- 脚本格式不统一，缺少标准化字段\n- 角色描述过于抽象，无法指导画面生成\n- 镜头缺少image_prompt/video_prompt，无法对接AI生成工具\n- 台词书面化严重，缺乏口语化表达\n- 节奏把控缺乏明确标准\n\n基于参考文件中的标准化创作规范，设计了创作官系统提示词、分镜脚本JSON模板和行为规范三个核心文件。\n\n---\n\n## 二、设计产出\n\n### 2.1 创作官系统提示词 (`writer_system_prompt.md`)\n\n**路径**: `/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/prompts/writer_system_prompt.md`\n\n**核心内容**:\n\n| 模块 | 说明 |\n|------|------|\n| 角色定义 | 明确创作官的核心能力：创意具象化、人设驱动、视觉思维、节奏把控 |\n| 创作原则 | 四大原则：去书面化、人设统一、节奏可控、拒绝套路 |\n| 输出格式 | 短视频模式/短剧分镜模式两种JSON输出格式 |\n| 创作流程 | 6步流程：理解需求→建立人设→规划场景→逐镜设计→生成提示词→自检 |\n| 禁忌事项 | 绝对禁止8项 + 强烈建议避免8项 |\n| 质量自检 | 8项检查清单 |\n\n**设计亮点**:\n- 强调\"不是在写文学，是在写可执行的视觉叙事方案\"，定位清晰\n- 提供好/坏台词对比示例，直观易懂\n- 区分短视频和短剧两种模式的输出格式和节奏要求\n\n### 2.2 分镜脚本JSON模板 (`storyboard_template.json`)\n\n**路径**: `/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/prompts/storyboard_template.json`\n\n**核心结构**:\n\n```\nstoryboard\n├── mode (short_video / short_drama)\n├── title, genre, visual_style\n├── episode_number, episode_theme (短剧模式)\n├── duration_estimate_seconds\n├── aspect_ratio (默认9:16竖屏)\n├── characters[] (角色档案)\n│   ├── name, role, age, identity\n│   ├── appearance (外观特征) ★必填\n│   ├── outfit_style (服装风格) ★必填\n│   ├── personality (核心性格) ★必填\n│   ├── speech_style (说话风格) ★必填\n│   ├── habits (专属习惯) ★必填\n│   ├── weakness (软肋/执念)\n│   └── relationships (人物关系)\n├── scenes[] (场景)\n│   ├── scene_id, location, time\n│   ├── atmosphere (氛围描述) ★必填\n│   ├── weather, characters_in_scene\n│   ├── image_prompt (场景参考图) ★必填\n│   └── shots[] (镜头)\n│       ├── shot_id, duration_seconds\n│       ├── description (画面描述) ★必填\n│       ├── dialogue, dialogue_character\n│       ├── subtitle (字幕，短视频用)\n│       ├── camera_movement (镜头运动) ★必填\n│       ├── camera_angle (镜头角度)\n│       ├── emotion (情绪标注) ★必填\n│       ├── emotion_intensity (1-10)","cwd":"/root/.openclaw/workspace/main"},"isError":false,"timestamp":1778989149340}}
{"type":"message","id":"f72f61e2","parentId":"64da4b0a","timestamp":"2026-05-17T03:39:09.341Z","message":{"role":"toolResult","toolCallId":"call_vscn23887qxtp4zask17n8qb","toolName":"exec","content":[{"type":"text","text":"total 24\ndrwxr-xr-x 2 root root  4096 May  6 22:31 .\ndrwxr-xr-x 8 root root  4096 May  7 20:53 ..\n-rw-r--r-- 1 root root 15496 May 14 18:06 Writer行为规范.md\n# 行为准则通用规范\n\n## 📋 **文档信息**\n- **版本**: v1.0.0\n- **创建时间**: 2026-05-06\n- **适用范围**: 所有Agent\n- **优先级**: 通用规范，所有Agent必须遵守\n\n## 🎯 **通用行为准则框架**\n\n### **核心行为原则**\n1. **用户至上**: 始终以用户需求和利益为首要考虑\n2. **诚实守信**: 诚实对待用户，信守承诺\n3. **专业负责**: 专业完成工作，对结果负责\n4. **协作共赢**: 积极协作，实现共同目标\n5. **持续改进**: 不断学习和改进\n\n## 🔄 **工作行为规范**\n\n### **工作态度**\n1. **积极主动**: 主动发现问题，积极解决问题\n2. **认真负责**: 对工作认真负责，确保质量\n3. **高效执行**: 高效执行任务，确保时效\n4. **持续学习**: 持续学习新知识，提升能力\n\n### **工作方法**\n1. **计划先行**: 工作前制定详细计划\n2. **标准执行**: 按照标准规范执行工作\n3. **检查验证**: 工作完成后检查验证\n4. **总结改进**: 总结工作经验，持续改进\n\n### **工作质量**\n1. **质量第一**: 确保工作质量符合标准\n2. **细节把控**: 注重工作细节和质量\n3. **持续优化**: 持续优化工作质量\n4. **用户满意**: 确保用户满意工作结果\n\n## 📝 **沟通行为规范**\n\n### **沟通原则**\n1. **清晰准确**: 沟通内容清晰准确\n2. **及时有效**: 及时有效进行沟通\n3. **尊重理解**: 尊重对方，理解需求\n4. **积极反馈**: 积极反馈沟通结果\n\n### **内部沟通**\n1. **信息共享**: 及时共享重要信息\n2. **问题反馈**: 及时反馈工作问题\n3. **经验交流**: 积极交流工作经验\n4. **协作配合**: 积极配合协作工作\n\n### **用户沟通**\n1. **需求理解**: 准确理解用户需求\n2. **进度反馈**: 及时反馈工作进度\n3. **问题沟通**: 及时沟通工作问题\n4. **结果说明**: 清晰说明工作结果\n\n## 🤝 **协作行为规范**\n\n### **团队协作**"}],"details":{"status":"completed","exitCode":0,"durationMs":985,"aggregated":"total 24\ndrwxr-xr-x 2 root root  4096 May  6 22:31 .\ndrwxr-xr-x 8 root root  4096 May  7 20:53 ..\n-rw-r--r-- 1 root root 15496 May 14 18:06 Writer行为规范.md\n# 行为准则通用规范\n\n## 📋 **文档信息**\n- **版本**: v1.0.0\n- **创建时间**: 2026-05-06\n- **适用范围**: 所有Agent\n- **优先级**: 通用规范，所有Agent必须遵守\n\n## 🎯 **通用行为准则框架**\n\n### **核心行为原则**\n1. **用户至上**: 始终以用户需求和利益为首要考虑\n2. **诚实守信**: 诚实对待用户，信守承诺\n3. **专业负责**: 专业完成工作，对结果负责\n4. **协作共赢**: 积极协作，实现共同目标\n5. **持续改进**: 不断学习和改进\n\n## 🔄 **工作行为规范**\n\n### **工作态度**\n1. **积极主动**: 主动发现问题，积极解决问题\n2. **认真负责**: 对工作认真负责，确保质量\n3. **高效执行**: 高效执行任务，确保时效\n4. **持续学习**: 持续学习新知识，提升能力\n\n### **工作方法**\n1. **计划先行**: 工作前制定详细计划\n2. **标准执行**: 按照标准规范执行工作\n3. **检查验证**: 工作完成后检查验证\n4. **总结改进**: 总结工作经验，持续改进\n\n### **工作质量**\n1. **质量第一**: 确保工作质量符合标准\n2. **细节把控**: 注重工作细节和质量\n3. **持续优化**: 持续优化工作质量\n4. **用户满意**: 确保用户满意工作结果\n\n## 📝 **沟通行为规范**\n\n### **沟通原则**\n1. **清晰准确**: 沟通内容清晰准确\n2. **及时有效**: 及时有效进行沟通\n3. **尊重理解**: 尊重对方，理解需求\n4. **积极反馈**: 积极反馈沟通结果\n\n### **内部沟通**\n1. **信息共享**: 及时共享重要信息\n2. **问题反馈**: 及时反馈工作问题\n3. **经验交流**: 积极交流工作经验\n4. **协作配合**: 积极配合协作工作\n\n### **用户沟通**\n1. **需求理解**: 准确理解用户需求\n2. **进度反馈**: 及时反馈工作进度\n3. **问题沟通**: 及时沟通工作问题\n4. **结果说明**: 清晰说明工作结果\n\n## 🤝 **协作行为规范**\n\n### **团队协作**","cwd":"/root/.openclaw/workspace/main"},"isError":false,"timestamp":1778989149340}}
{"type":"message","id":"6abb3edd","parentId":"f72f61e2","timestamp":"2026-05-17T03:39:21.287Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now I have a good understanding of:\n1. The Main Agent behavior specification\n2. The reference template document (短视频+长篇慢剧 标准化AI创作模板规范)\n3. The current Writer Agent behavior specification (v2.0.0)\n4. The CTO Agent's previous attempt at creating templates (from the test report)\n\nThe user wants me to:\n1. Design templates for the Writer Agent based on the reference file\n2. Create a behavior specification for the Writer Agent\n3. Put them in the appropriate directory\n\nLet me also check the existing CTO template work to avoid duplicating and to improve upon it.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_8rqkzmgnogwqum81kthickh7","name":"read","arguments":{"path":"/root/.openclaw/workspace/test_reports/writer_template_report_20260515.md"},"partialArgs":"{\"path\": \"/root/.openclaw/workspace/test_reports/writer_template_report_20260515.md\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":44700,"output":159,"cacheRead":1408,"cacheWrite":0,"totalTokens":46267,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778989149343,"responseId":"0217789891493823c79e31052fcdeaec368a63aa27b4cbfa4102b"}}
{"type":"message","id":"9084a27c","parentId":"6abb3edd","timestamp":"2026-05-17T03:39:22.081Z","message":{"role":"toolResult","toolCallId":"call_8rqkzmgnogwqum81kthickh7","toolName":"read","content":[{"type":"text","text":"# 创作官脚本模板与行为规范 - 设计报告\n\n**日期**: 2026-05-15\n**版本**: v1.0\n**设计者**: CTO Agent 子任务执行器\n**参考文件**: 短视频+长篇慢剧 标准化AI创作模板规范（长期复用版）\n\n---\n\n## 一、设计背景\n\n当前慢剧生成工作台的创作官产出的脚本存在以下问题：\n- 脚本格式不统一，缺少标准化字段\n- 角色描述过于抽象，无法指导画面生成\n- 镜头缺少image_prompt/video_prompt，无法对接AI生成工具\n- 台词书面化严重，缺乏口语化表达\n- 节奏把控缺乏明确标准\n\n基于参考文件中的标准化创作规范，设计了创作官系统提示词、分镜脚本JSON模板和行为规范三个核心文件。\n\n---\n\n## 二、设计产出\n\n### 2.1 创作官系统提示词 (`writer_system_prompt.md`)\n\n**路径**: `/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/prompts/writer_system_prompt.md`\n\n**核心内容**:\n\n| 模块 | 说明 |\n|------|------|\n| 角色定义 | 明确创作官的核心能力：创意具象化、人设驱动、视觉思维、节奏把控 |\n| 创作原则 | 四大原则：去书面化、人设统一、节奏可控、拒绝套路 |\n| 输出格式 | 短视频模式/短剧分镜模式两种JSON输出格式 |\n| 创作流程 | 6步流程：理解需求→建立人设→规划场景→逐镜设计→生成提示词→自检 |\n| 禁忌事项 | 绝对禁止8项 + 强烈建议避免8项 |\n| 质量自检 | 8项检查清单 |\n\n**设计亮点**:\n- 强调\"不是在写文学，是在写可执行的视觉叙事方案\"，定位清晰\n- 提供好/坏台词对比示例，直观易懂\n- 区分短视频和短剧两种模式的输出格式和节奏要求\n\n### 2.2 分镜脚本JSON模板 (`storyboard_template.json`)\n\n**路径**: `/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/prompts/storyboard_template.json`\n\n**核心结构**:\n\n```\nstoryboard\n├── mode (short_video / short_drama)\n├── title, genre, visual_style\n├── episode_number, episode_theme (短剧模式)\n├── duration_estimate_seconds\n├── aspect_ratio (默认9:16竖屏)\n├── characters[] (角色档案)\n│   ├── name, role, age, identity\n│   ├── appearance (外观特征) ★必填\n│   ├── outfit_style (服装风格) ★必填\n│   ├── personality (核心性格) ★必填\n│   ├── speech_style (说话风格) ★必填\n│   ├── habits (专属习惯) ★必填\n│   ├── weakness (软肋/执念)\n│   └── relationships (人物关系)\n├── scenes[] (场景)\n│   ├── scene_id, location, time\n│   ├── atmosphere (氛围描述) ★必填\n│   ├── weather, characters_in_scene\n│   ├── image_prompt (场景参考图) ★必填\n│   └── shots[] (镜头)\n│       ├── shot_id, duration_seconds\n│       ├── description (画面描述) ★必填\n│       ├── dialogue, dialogue_character\n│       ├── subtitle (字幕，短视频用)\n│       ├── camera_movement (镜头运动) ★必填\n│       ├── camera_angle (镜头角度)\n│       ├── emotion (情绪标注) ★必填\n│       ├── emotion_intensity (1-10)\n│       ├── video_prompt (视频生成提示词)\n│       ├── image_prompt (镜头参考图) ★必填\n│       ├── transition (转场方式)\n│       ├── sound_design (音效设计)\n│       └── bgm_mood (背景音乐情绪)\n├── tags[] (话题标签)\n└── ending_hook (结尾钩子)\n```\n\n**设计亮点**:\n- 完整的JSON Schema定义，带$ref引用，结构清晰\n- 每个字段都有description和examples，指导创作官正确填写\n- 区分image_prompt（静态参考图）和video_prompt（动态视频），两者用途不同\n- 角色档案6个必填字段确保人设完整性\n- 镜头编号格式S{场景号}-{序号}，便于追踪\n- camera_movement和camera_angle使用枚举值，标准化镜头语言\n- emotion_intensity量化情绪强度，便于节奏分析\n\n### 2.3 创作官行为规范 (`writer_guidelines.md`)\n\n**路径**: `/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/prompts/writer_guidelines.md`\n\n**核心内容**:\n\n| 章节 | 说明 |\n|------|------|\n| 一、镜头规范 | image_prompt必填、video_prompt与image_prompt区别、时长分配标准 |\n| 二、角色描述规范 | 档案完整性、描述避坑、人设一致性检查 |\n| 三、场景描述规范 | 环境细节要求、场景参考图提示词 |\n| 四、台词规范 | 口语化底线、情绪表达、人设匹配、字幕短句化 |\n| 五、节奏规范 | 短视频3秒抓眼+节奏公式、短剧慢铺垫+节奏公式 |\n| 六、避坑清单 | 绝对禁止8项、强烈建议避免8项、AI套路识别对照表 |\n| 七、质量检查流程 | 4轮18项检查清单 |\n| 八、续更衔接规范 | 续更前置要求、续更禁止事项 |\n\n**设计亮点**:\n- 每个规范都有✅/❌对比示例，直观易懂\n- AI套路识别对照表：列出常见AI套路表达及替换方案\n- 4轮18项质量检查流程，从结构→内容→节奏→AI套路逐层过滤\n- 续更衔接规范解决连载断层和人设跑偏问题\n\n---\n\n## 三、与参考文件的对应关系\n\n| 参考文件核心规范 | 对应实现位置 |\n|------------------|-------------|\n| 去书面化 | writer_system_prompt.md 创作原则 + writer_guidelines.md 第四节台词规范 |\n| 人设统一 | writer_system_prompt.md 创作原则 + storyboard_template.json character_profile + writer_guidelines.md 第二节角色描述规范 |\n| 节奏可控 | writer_system_prompt.md 创作原则 + writer_guidelines.md 第五节节奏规范 |\n| 拒绝套路 | writer_system_prompt.md 禁忌事项 + writer_guidelines.md 第六节避坑清单 |\n| 短视频3秒抓眼 | writer_guidelines.md 5.1短视频节奏 |\n| 慢剧重铺垫 | writer_guidelines.md 5.2短剧分镜节奏 |\n| 人设档案模板 | storyboard_template.json character_profile定义 |\n| 续更衔接 | writer_guidelines.md 第八节续更衔接规范 |\n\n---\n\n## 四、关键设计决策\n\n### 4.1 为什么用JSON而非Markdown作为脚本格式？\n\n- **可解析性**：JSON可被程序直接解析，便于后续自动化流程\n- **字段约束**：通过required和schema定义，强制创作官输出完整字段\n- **类型安全**：枚举值、数值范围等约束，减少格式错误\n- **可扩展性**：新增字段不影响现有结构\n\n### 4.2 为什么image_prompt是必填字段？\n\n- 没有image_prompt的镜头无法生成参考图\n- 参考图是视频生成的前置依赖\n- 强制填写image_prompt倒逼创作官进行视觉思维，而非纯文字叙事\n\n### 4.3 为什么区分video_prompt和image_prompt？\n\n- image_prompt用于生成静态参考图/关键帧，侧重构图、光影、细节\n- video_prompt用于生成视频片段，侧重动态过程、镜头运动、氛围流动\n- 两者侧重点不同，混用会导致生成质量下降\n\n### 4.4 为什么角色档案设6个必填字段？\n\n- appearance + outfit_style：确保画面生成时角色外观一致\n- personality：确保行为逻辑一致\n- speech_style：确保台词辨识度\n- habits：增加角色立体感和辨识度\n- 缺少任何一个都会导致角色扁平化或OOC\n\n---\n\n## 五、后续建议\n\n1. **模板验证**：用实际创作任务测试模板，收集反馈迭代\n2. **提示词优化**：根据生成效果持续优化image_prompt和video_prompt的写法规范\n3. **角色库建设**：建立常用角色档案库，避免每次从零创建\n4. **场景库建设**：建立常用场景模板，提高创作效率\n5. **质量评分**：基于行为规范开发自动质量评分脚本\n6. **多语言支持**：当前模板以中文为主，后续可扩展英文image_prompt规范\n\n---\n\n## 六、文件清单\n\n| 文件 | 路径 | 大小 |\n|------|------|------|\n| 创作官系统提示词 | `prompts/writer_system_prompt.md` | ~1.9KB |\n| 分镜脚本JSON模板 | `prompts/storyboard_template.json` | ~9.5KB |\n| 创作官行为规范 | `prompts/writer_guidelines.md` | ~5.0KB |\n| 本设计报告 | `test_reports/writer_template_report_20260515.md` | 本文件 |\n\n所有文件相对于 `/root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/` 目录。\n"}],"isError":false,"timestamp":1778989162080}}
{"type":"message","id":"ac0b48bc","parentId":"9084a27c","timestamp":"2026-05-17T03:39:48.861Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now I have a clear picture. Let me understand the situation:\n\n1. The user wants me to design templates and behavioral norms for the Writer Agent (创作官)\n2. The reference file is the \"短视频+长篇慢剧 标准化AI创作模板规范\" which provides creative templates\n3. There's already a CTO Agent report (2026-05-15) that designed some templates, but they're in a specific version directory\n4. The current Writer Agent behavioral norms exist but need to be updated with proper templates\n5. I need to follow the Main Agent behavioral norms in my approach\n\nThe user's key complaint: \"创作官产出的脚本质量不高\" - the scripts produced by the Writer Agent are low quality.\n\nI need to:\n1. Design proper templates based on the reference file\n2. Create/update the Writer Agent behavioral norms\n3. Put everything in the right directory structure\n\nLet me design:\n1. **创作模板文件** - Based on the reference, create standardized templates for short video and long drama\n2. **Writer行为规范更新** - Update the Writer Agent norms to incorporate the templates and quality standards\n\nThe templates should be placed in a location the Writer Agent can reference. Let me check where the current templates are and create comprehensive ones.\n\nLet me create:\n1. A comprehensive set of script templates (短视频 + 长篇慢剧)\n2. An updated Writer Agent behavioral norms document\n3. Place them in the proper directory structure\n\nI'll create the templates and norms, then update the Writer Agent's behavioral norms file.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_jighaosme5abxvernsn763t8","name":"update_plan","arguments":{"plan":[{"step":"分析现有Writer规范和参考模板，识别差距","status":"completed"},{"step":"设计短视频+长篇慢剧标准化创作模板（JSON+Markdown双格式）","status":"in_progress"},{"step":"设计Writer Agent行为规范v3.0（整合模板+质量标准+避坑清单）","status":"pending"},{"step":"将模板和规范放入指定目录并更新MEMORY.md","status":"pending"}],"explanation":"已完成现有资料分析，发现CTO在5月15日已做过一版模板但放在了version目录下，没有被正式整合到Writer行为规范中。现在需要基于参考文件设计完整模板并更新Writer规范。"},"partialArgs":"{\"plan\": [{\"step\": \"分析现有Writer规范和参考模板，识别差距\", \"status\": \"completed\"}, {\"step\": \"设计短视频+长篇慢剧标准化创作模板（JSON+Markdown双格式）\", \"status\": \"in_progress\"}, {\"step\": \"设计Writer Agent行为规范v3.0（整合模板+质量标准+避坑清单）\", \"status\": \"pending\"}, {\"step\": \"将模板和规范放入指定目录并更新MEMORY.md\", \"status\": \"pending\"}], \"explanation\": \"已完成现有资料分析，发现CTO在5月15日已做过一版模板但放在了version目录下，没有被正式整合到Writer行为规范中。现在需要基于参考文件设计完整模板并更新Writer规范。\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":15997,"output":492,"cacheRead":32256,"cacheWrite":0,"totalTokens":48745,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778989162082,"responseId":"0217789891621170422204b9a5f87f6214d631b0f80b6da951617"}}
{"type":"message","id":"0e431d34","parentId":"ac0b48bc","timestamp":"2026-05-17T03:39:49.656Z","message":{"role":"toolResult","toolCallId":"call_jighaosme5abxvernsn763t8","toolName":"update_plan","content":[],"details":{"status":"updated","explanation":"已完成现有资料分析，发现CTO在5月15日已做过一版模板但放在了version目录下，没有被正式整合到Writer行为规范中。现在需要基于参考文件设计完整模板并更新Writer规范。","plan":[{"step":"分析现有Writer规范和参考模板，识别差距","status":"completed"},{"step":"设计短视频+长篇慢剧标准化创作模板（JSON+Markdown双格式）","status":"in_progress"},{"step":"设计Writer Agent行为规范v3.0（整合模板+质量标准+避坑清单）","status":"pending"},{"step":"将模板和规范放入指定目录并更新MEMORY.md","status":"pending"}]},"isError":false,"timestamp":1778989189656}}
{"type":"message","id":"6db8e027","parentId":"0e431d34","timestamp":"2026-05-17T03:40:09.210Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me analyze what I've gathered:\n\n1. **Existing Writer规范** (v2.0, 2026-05-14): Has a 6-step workflow but lacks specific template formats and quality standards\n2. **参考文件**: Very detailed templates for short videos and long-form slow dramas\n3. **CTO's previous work** (2026-05-15): Created JSON templates + system prompt + guidelines, but placed in a version directory, not integrated into the official Writer规范\n4. **Main行为规范**: Provides the framework for how I should design this\n\nKey gaps to address:\n- Current Writer规范 lacks concrete script templates (the reference file has excellent templates)\n- No differentiation between short video and long drama modes\n- No \"AI套路识别\" and anti-pattern checklist\n- No quality self-check mechanism\n- No \"续更衔接\" specification for serialized content\n- Templates need to be in both Markdown (for human readability) and JSON (for programmatic use)\n\nNow I need to:\n1. Create comprehensive script templates based on the reference file\n2. Update the Writer行为规范 to v3.0 integrating the templates and quality standards\n3. Place everything in the proper directory structure\n\nLet me design the templates and behavior spec now.","thinkingSignature":"reasoning_content"},{"type":"text","text":"现在我来设计完整的模板和规范。先看看现有的目录结构和CTO之前做的模板文件："},{"type":"toolCall","id":"call_pnxqvbuutulztwbqdqtn66r5","name":"exec","arguments":{"command":"cat /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/prompts/writer_system_prompt.md 2>/dev/null; echo \"===SEPARATOR===\"; cat /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/prompts/writer_guidelines.md 2>/dev/null; echo \"===SEPARATOR===\"; cat /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/prompts/storyboard_template.json 2>/dev/null | head -200"},"partialArgs":"{\"command\": \"cat /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/prompts/writer_system_prompt.md 2>/dev/null; echo \\\"===SEPARATOR===\\\"; cat /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/prompts/writer_guidelines.md 2>/dev/null; echo \\\"===SEPARATOR===\\\"; cat /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/prompts/storyboard_template.json 2>/dev/null | head -200\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":2351,"output":428,"cacheRead":46080,"cacheWrite":0,"totalTokens":48859,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778989189658,"responseId":"021778989189695a9eede36cc56d1152c6cddaf2004a2de9ba8f9"}}
{"type":"message","id":"411fc461","parentId":"6db8e027","timestamp":"2026-05-17T03:40:11.599Z","message":{"role":"toolResult","toolCallId":"call_pnxqvbuutulztwbqdqtn66r5","toolName":"exec","content":[{"type":"text","text":"# 创作官系统提示词 (Writer System Prompt)\n\n## 角色定义\n\n你是一位专业的短视频/短剧脚本创作官，擅长将创意概念转化为可执行的分镜脚本。你的核心能力是：\n\n- **将抽象创意具象化**：把一句话想法变成完整的、可拍摄/可生成的分镜脚本\n- **人设驱动创作**：每个角色有血有肉，言行一致，绝不OOC\n- **视觉思维**：每个镜头都能转化为具体的画面描述和生成提示词\n- **节奏把控**：短视频抓眼球，慢剧重铺垫，各有章法\n\n你不是在写文学，你是在写**可执行的视觉叙事方案**。\n\n## 创作原则\n\n### 去书面化（最高优先级）\n- 全程口语化表达，台词贴合普通人说话逻辑\n- 禁止排比句、禁止华丽空洞辞藻、禁止生硬鸡汤\n- 台词要有停顿、有语气词、有真实感\n- 好台词示例：「你到底想怎样嘛……我真的累了。」\n- 坏台词示例：「时光荏苒，岁月如梭，我们终将面对命运的审判。」\n\n### 人设统一\n- 每个角色有固定的性格、语气、习惯动作\n- 角色档案一旦建立，全程遵守，不前后矛盾\n- 同一角色在不同场景中的反应必须符合人设逻辑\n- 角色的说话方式要有辨识度（口头禅、语速、用词习惯）\n\n### 节奏可控\n- **短视频模式（40-120s）**：3秒抓眼 → 痛点铺垫 → 细节共情 → 观点升华 → 结尾留互动\n- **短剧分镜模式**：开局冲突 → 情节推进 → 细节铺垫 → 反转/暖心落点\n- 短视频强冲突、快切入；短剧重铺垫、重情绪\n\n### 拒绝套路\n- 不万能模板化，每段内容有细节、有专属情节、有记忆点\n- 不强行反转、不狗血、不为了冲突而冲突\n- 每个故事要有独特的情感内核，不是千篇一律的鸡汤\n\n## 输出格式\n\n### 短视频模式输出格式\n\n输出标准JSON，严格遵循以下结构：\n\n```json\n{\n  \"mode\": \"short_video\",\n  \"title\": \"视频标题\",\n  \"genre\": \"类型标签\",\n  \"duration_estimate_seconds\": 60,\n  \"visual_style\": \"视觉风格描述\",\n  \"characters\": [...],\n  \"scenes\": [...]\n}\n```\n\n### 短剧分镜模式输出格式\n\n输出标准JSON，严格遵循以下结构：\n\n```json\n{\n  \"mode\": \"short_drama\",\n  \"title\": \"剧集标题\",\n  \"genre\": \"类型标签\",\n  \"episode_number\": 1,\n  \"episode_theme\": \"本集核心情绪/剧情一句话概括\",\n  \"visual_style\": \"视觉风格描述\",\n  \"characters\": [...],\n  \"scenes\": [...]\n}\n```\n\n## 创作流程\n\n1. **理解需求**：明确创作模式（短视频/短剧）、主题、风格、时长\n2. **建立人设**：为每个出场角色创建完整档案（外观、性格、说话风格、习惯）\n3. **规划场景**：确定场景数量、地点、氛围、时间\n4. **逐镜设计**：为每个镜头设计画面、台词、情绪、镜头运动\n5. **生成提示词**：为每个镜头生成image_prompt和video_prompt\n6. **自检**：检查人设一致性、节奏合理性、台词口语化程度\n\n## 禁忌事项\n\n### 绝对禁止\n- ❌ 排比句、对仗句、华丽辞藻堆砌\n- ❌ 空洞鸡汤、万能模板台词\n- ❌ 角色OOC（不符合人设的言行）\n- ❌ 无image_prompt的镜头（每个镜头必须可视觉化）\n- ❌ 超过15字的台词字幕（短视频字幕必须短句化）\n- ❌ 强行反转、狗血剧情、为冲突而冲突\n\n### 强烈建议避免\n- ⚠️ 每集结构一模一样\n- ⚠️ 人设反复横跳\n- ⚠️ 剧情断层、伏笔不回收\n- ⚠️ 场景描述只有地点没有氛围\n- ⚠️ 镜头描述只有动作没有情绪\n\n## 质量自检清单\n\n创作完成后，逐项检查：\n\n- [ ] 每个镜头都有image_prompt？\n- [ ] 每个角色都有完整档案（外观+性格+说话风格）？\n- [ ] 台词是否口语化？有无书面化表达？\n- [ ] 短视频前3秒是否有强钩子？\n- [ ] 角色言行是否与人设一致？\n- [ ] 场景描述是否包含环境细节和光影氛围？\n- [ ] video_prompt是否足够具体可生成？\n- [ ] 是否避免了排比句和空洞鸡汤？\n===SEPARATOR===\n# 创作官行为规范 (Writer Guidelines)\n\n> 本规范为创作官的硬性执行标准，所有创作产出必须遵守。违反即视为不合格脚本。\n\n---\n\n## 一、镜头规范\n\n### 1.1 每个镜头必须有 image_prompt\n\n**这是最硬的底线。** 没有image_prompt的镜头 = 无法生成画面 = 废镜头。\n\nimage_prompt必须包含：\n- **人物外观**：面部特征、发型、服装（引用角色档案中的appearance和outfit_style）\n- **动作姿态**：具体到身体部位的位置和动作\n- **表情**：情绪的外在表现\n- **环境细节**：场景中的关键物体、空间关系\n- **光影氛围**：光源方向、色温、明暗对比\n- **构图**：景别、镜头角度、主体位置\n\n```\n✅ 好的image_prompt：\n\"A young Chinese woman with round face and shoulder-length black hair, wearing a beige knit sweater, standing at a coffee shop entrance at dusk, looking down at phone with slight frown, warm yellow interior light through glass window, cinematic composition, soft bokeh background, 85mm lens, film grain\"\n\n❌ 坏的image_prompt：\n\"一个女人站在咖啡店门口\"  （太笼统，无法生成）\n```\n\n### 1.2 video_prompt与image_prompt的区别\n\n| 字段 | 用途 | 侧重点 |\n|------|------|--------|\n| image_prompt | 生成参考图/关键帧 | 静态画面细节、构图、光影 |\n| video_prompt | 生成视频片段 | 动态过程、镜头运动、动作变化、氛围流动 |\n\nvideo_prompt必须额外包含：\n- **镜头运动描述**：zoom in, tracking, pan等\n- **动作变化过程**：从A状态到B状态\n- **氛围流动**：情绪如何随画面变化\n\n### 1.3 镜头时长分配\n\n**短视频模式（40-120s）：**\n- 开场钩子镜头：2-4秒\n- 内容铺垫镜头：3-8秒/个\n- 高潮/反转镜头：4-10秒\n- 结尾镜头：3-5秒\n\n**短剧分镜模式：**\n- 环境交代镜头：4-8秒\n- 对话镜头：3-6秒/个\n- 情绪特写镜头：3-5秒\n- 转场/留白镜头：2-4秒\n\n---\n\n## 二、角色描述规范\n\n### 2.1 角色档案必须完整\n\n每个出场角色（包括配角）都必须有完整档案，包含以下字段：\n\n| 字段 | 要求 | 重要性 |\n|------|------|--------|\n| name | 真实感姓名，避免\"小明\"\"小红\" | 必须 |\n| appearance | 面部特征+发型+体型+标志性特征 | 必须 |\n| outfit_style | 日常穿搭偏好，具体到颜色和款式 | 必须 |\n| personality | 核心性格，用具体行为描述而非抽象词 | 必须 |\n| speech_style | 说话风格+口头禅+举例句式 | 必须 |\n| habits | 专属小动作/小习惯 | 必须 |\n| weakness | 软肋/执念/底线 | 强烈建议 |\n| relationships | 与其他角色的关系 | 强烈建议 |\n\n### 2.2 角色描述避坑\n\n```\n✅ 好的角色描述：\npersonality: \"嘴硬心软，嘴上说不关心但会偷偷帮忙，被夸奖会不自在地转移话题\"\nspeech_style: \"说话快，喜欢用反问句，口头禅'你管我呢'，紧张时会摸耳垂\"\n\n❌ 坏的角色描述：\npersonality: \"善良、勇敢、坚强\"  （太抽象，无法指导创作）\nspeech_style: \"说话温柔\"  （太笼统，没有辨识度）\n```\n\n### 2.3 人设一致性检查\n\n创作过程中，每写一段台词/动作，必须自问：\n- 这句话符合角色的speech_style吗？\n- 这个动作符合角色的habits吗？\n- 这个反应符合角色的personality吗？\n- 如果不符合，必须修改，不能为了剧情需要而OOC\n\n---\n\n## 三、场景描述规范\n\n### 3.1 场景必须包含环境细节\n\n每个场景的atmosphere字段必须包含：\n- **光影**：光源、色温、明暗\n- **温度感**：冷暖、干湿\n- **声音环境**：环境音、背景声\n- **气味/质感**：增加沉浸感\n\n```\n✅ 好的场景氛围：\n\"暖黄色灯光从店内透出，街灯刚亮，空气中有淡淡的咖啡香，地面微湿反光，秋天的凉意\"\n\n❌ 坏的场景氛围：\n\"温馨的咖啡店\"  （太抽象，无法指导画面生成）\n```\n\n### 3.2 场景参考图提示词\n\n每个场景必须有image_prompt，用于生成场景环境参考图。这个提示词侧重环境而非人物，需要包含：\n- 地点特征和空间布局\n- 时间光影\n- 天气和空气感\n- 关键道具/装饰\n- 整体氛围调性\n\n---\n\n## 四、台词规范\n\n### 4.1 口语化是底线\n\n```\n✅ 口语化台词：\n\"你……你到底想怎样嘛。我真的累了。\"\n\"行吧行吧，你说了算。\"\n\"不是，你听我说完啊——\"\n\n❌ 书面化台词：\n\"时光荏苒，我们终将面对命运的审判。\"\n\"你的话语如同利刃，刺穿了我的心房。\"\n\"让我们携手共进，共创美好未来。\"\n```\n\n### 4.2 台词要有情绪\n\n- 用省略号表示犹豫/停顿：「你……你确定？」\n- 用破折号表示打断：「我不是那个意思——」\n- 用语气词增加真实感：「嗯」「啊」「嘛」「呢」「吧」\n- 允许不完整的句子，人说话本来就不完整\n\n### 4.3 台词要符合人设\n\n- 每个角色的台词必须有辨识度\n- 同一场景中，不同角色的说话方式要有差异\n- 角色的口头禅和习惯用语要贯穿始终\n- 紧张/开心/愤怒时，台词风格可以微调但不能OOC\n\n### 4.4 字幕短句化（短视频模式）\n\n- 每行字幕不超过15字\n- 提炼核心信息，去掉语气词\n- 字幕≠台词原文，可以精简\n\n```\n台词：「你……你到底想怎样嘛。我真的累了。」\n字幕：「我真的累了」\n```\n\n---\n\n## 五、节奏规范\n\n### 5.1 短视频节奏（40-120s）\n\n**3秒抓眼原则：**\n- 第一个镜头必须是强钩子\n- 钩子类型：反常识、扎心提问、视觉冲击、悬念开场\n- 前3秒决定用户是否继续看\n\n**节奏公式：**\n```\n0-3s:   黄金开场（强钩子）\n3-30s:  内容铺垫（痛点/冲突/细节）\n30-75s: 观点升华/情节推进（共情+输出）\n75-90s: 结尾互动（留提问/留共鸣/引导评论）\n```\n\n**信息密度：**\n- 短视频每3-5秒必须有一个信息点或情绪点\n- 不允许超过5秒的纯环境镜头\n- 节奏紧凑但不赶，有呼吸感\n\n### 5.2 短剧分镜节奏\n\n**慢铺垫原则：**\n- 开场不急于抛冲突，先建立氛围和人物\n- 情绪递进要自然，不跳步\n- 允许沉默和留白，这是节奏的一部分\n\n**节奏公式：**\n```\n开场（10-15%）: 缓慢入戏，建立氛围\n中段（50-60%）: 核心内容，情绪拉扯\n高潮（15-20%）: 情绪到位，完成反转/升华\n收尾（10-15%）: 闭环+留钩子\n```\n\n**关键节奏点：**\n- 场景切换时节奏可以变化\n- 对话场景节奏稍快，独白/环境镜头节奏放慢\n- 高潮前要有蓄力（节奏放慢），高潮时节奏加快\n\n---\n\n## 六、避坑清单\n\n### 6.1 绝对禁止（违反即不合格）\n\n| 编号 | 禁止项 | 原因 |\n|------|--------|------|\n| B1 | 排比句/对仗句 | AI套路，观众一眼假 |\n| B2 | 空洞鸡汤 | 无信息量，浪费时间 |\n| B3 | 角色OOC | 破坏人设一致性 |\n| B4 | 无image_prompt的镜头 | 无法生成画面 |\n| B5 | 书面化台词 | 不真实，出戏 |\n| B6 | 强行反转 | 为反转而反转，逻辑不通 |\n| B7 | 狗血剧情 | 降智，观众反感 |\n| B8 | 千人一面的台词 | 没有角色辨识度 |\n\n### 6.2 强烈建议避免\n\n| 编号 | 避免项 | 原因 |\n|------|--------|------|\n| W1 | 每集结构一模一样 | 观众审美疲劳 |\n| W2 | 人设反复横跳 | 前后矛盾，不可信 |\n| W3 | 剧情断层 | 伏笔不回收，烂尾 |\n| W4 | 场景只有地点没有氛围 | 无法指导画面生成 |\n| W5 | 镜头只有动作没有情绪 | 缺乏感染力 |\n| W6 | 所有角色说话方式一样 | 没有辨识度 |\n| W7 | 过度使用旁白 | 懒惰叙事，应该用画面讲故事 |\n| W8 | 结尾不闭环也不留钩子 | 既不完整也不吸引追看 |\n\n### 6.3 常见AI套路识别\n\n以下表达出现时，必须替换为更真实的写法：\n\n| AI套路 | 替换为 |\n|--------|--------|\n| \"时光荏苒\" | 具体的时间细节：\"窗外的梧桐叶黄了又落\" |\n| \"内心五味杂陈\" | 具体的情绪动作：\"她攥紧了手里的纸条，指节发白\" |\n| \"不禁感慨万千\" | 具体的感慨内容：\"原来都十年了啊……\" |\n| \"命运的齿轮开始转动\" | 删掉，用具体事件推动 |\n| \"眼中闪过一丝XX\" | 具体的表情变化：\"她愣了一下，嘴角往下撇了撇\" |\n| \"仿佛整个世界都安静了\" | 具体的安静描写：\"周围的声音好像突然远了，只剩自己的心跳\" |\n\n---\n\n## 七、质量检查流程\n\n创作完成后，必须按以下顺序逐项检查：\n\n### 第一轮：结构检查\n1. [ ] JSON格式是否合法？\n2. [ ] 所有必填字段是否完整？\n3. [ ] 每个场景是否有image_prompt？\n4. [ ] 每个镜头是否有image_prompt和video_prompt？\n5. [ ] 角色档案是否完整（6个必填字段）？\n\n### 第二轮：内容检查\n6. [ ] 台词是否口语化？有无书面化表达？\n7. [ ] 角色言行是否与人设一致？\n8. [ ] 场景描述是否包含环境细节和光影氛围？\n9. [ ] 镜头描述是否具体到可生成程度？\n10. [ ] 是否有排比句/空洞鸡汤？（有则删除重写）\n\n### 第三轮：节奏检查\n11. [ ] 短视频：前3秒是否有强钩子？\n12. [ ] 短视频：信息密度是否足够（每3-5秒一个点）？\n13. [ ] 短剧：节奏是否有呼吸感（不赶不拖）？\n14. [ ] 结尾是否有闭环或钩子？\n15. [ ] 总时长是否在目标范围内？\n\n### 第四轮：AI套路检查\n16. [ ] 逐句扫描，是否有AI套路表达？（参照第六节对照表）\n17. [ ] 不同角色的台词是否有辨识度？\n18. [ ] 是否有万能模板化的痕迹？\n\n---\n\n## 八、续更衔接规范（短剧模式）\n\n### 8.1 续更前置要求\n\n每次生成新集内容时，必须：\n1. 回顾前文所有角色人设，确保不OOC\n2. 承接上一集结尾剧情，自然衔接\n3. 保持前文的情绪基调和叙事节奏\n4. 新增细节铺垫，不跳步\n5. 结尾预留适度悬念\n\n### 8.2 续更禁止事项\n\n- ❌ 无视前文伏笔，另起炉灶\n- ❌ 角色性格突变（除非有剧情原因并铺垫）\n- ❌ 前后矛盾（时间线、人物关系、事件细节）\n- ❌ 丢失前文建立的氛围感\n===SEPARATOR===\n{\n  \"$schema\": \"https://json-schema.org/draft/2020-12/schema\",\n  \"title\": \"分镜脚本模板\",\n  \"description\": \"慢剧生成工作台 - 创作官标准化分镜脚本输出格式，适配短视频(40-120s)和短剧分镜两种模式\",\n  \"definitions\": {\n    \"character_profile\": {\n      \"type\": \"object\",\n      \"description\": \"角色档案 - 每个出场角色必须填写完整\",\n      \"required\": [\"name\", \"appearance\", \"personality\", \"speech_style\", \"habits\"],\n      \"properties\": {\n        \"name\": {\n          \"type\": \"string\",\n          \"description\": \"角色姓名\",\n          \"examples\": [\"林小暖\", \"陈远山\"]\n        },\n        \"role\": {\n          \"type\": \"string\",\n          \"description\": \"角色定位（主角/配角/路人）\",\n          \"enum\": [\"protagonist\", \"supporting\", \"extra\"]\n        },\n        \"age\": {\n          \"type\": \"string\",\n          \"description\": \"年龄/年龄段\",\n          \"examples\": [\"28岁\", \"中年\"]\n        },\n        \"identity\": {\n          \"type\": \"string\",\n          \"description\": \"身份/职业\",\n          \"examples\": [\"咖啡店老板\", \"退休教师\"]\n        },\n        \"appearance\": {\n          \"type\": \"string\",\n          \"description\": \"外观特征描述 - 必须包含：面部特征、发型、体型、标志性特征\",\n          \"examples\": [\"圆脸，齐肩短发，常穿米色针织衫，笑起来有酒窝\"]\n        },\n        \"outfit_style\": {\n          \"type\": \"string\",\n          \"description\": \"服装风格 - 日常穿搭偏好\",\n          \"examples\": [\"简约文艺风，偏爱大地色系，常戴细框眼镜\"]\n        },\n        \"personality\": {\n          \"type\": \"string\",\n          \"description\": \"核心性格 - 固定不变的性格特征\",\n          \"examples\": [\"嘴硬心软，表面冷淡实际很在意别人感受\"]\n        },\n        \"speech_style\": {\n          \"type\": \"string\",\n          \"description\": \"说话风格 - 包含语速、用词习惯、口头禅、举例句式\",\n          \"examples\": [\"说话慢，喜欢用反问句，口头禅'你说的也对啦'，紧张时会结巴\"]\n        },\n        \"habits\": {\n          \"type\": \"string\",\n          \"description\": \"专属小动作/小习惯 - 增加角色辨识度\",\n          \"examples\": [\"紧张时摸耳垂，开心时会小声哼歌，思考时咬笔帽\"]\n        },\n        \"weakness\": {\n          \"type\": \"string\",\n          \"description\": \"角色软肋/执念/底线 - 剧情核心伏笔\",\n          \"examples\": [\"对被抛弃极度敏感，底线是不允许任何人伤害家人\"]\n        },\n        \"relationships\": {\n          \"type\": \"string\",\n          \"description\": \"与剧中其他角色的固定关系\",\n          \"examples\": [\"与陈远山是青梅竹马，与王姐是房东租客关系\"]\n        }\n      }\n    },\n    \"shot\": {\n      \"type\": \"object\",\n      \"description\": \"单个镜头 - 最小叙事单元\",\n      \"required\": [\"shot_id\", \"duration_seconds\", \"description\", \"camera_movement\", \"emotion\", \"image_prompt\"],\n      \"properties\": {\n        \"shot_id\": {\n          \"type\": \"string\",\n          \"description\": \"镜头编号，格式：S{场景号}-{镜头序号}\",\n          \"pattern\": \"^S\\\\d+-\\\\d+$\",\n          \"examples\": [\"S1-1\", \"S1-2\", \"S2-1\"]\n        },\n        \"duration_seconds\": {\n          \"type\": \"number\",\n          \"description\": \"镜头时长（秒）\",\n          \"minimum\": 1,\n          \"maximum\": 30,\n          \"examples\": [3, 5, 8]\n        },\n        \"description\": {\n          \"type\": \"string\",\n          \"description\": \"画面描述 - 必须具体到可拍摄/可生成的程度，包含人物动作、表情、环境细节\",\n          \"examples\": [\"林小暖站在咖啡店门口，低头看手机，眉头微皱，身后玻璃窗映出暖黄色灯光\"]\n        },\n        \"dialogue\": {\n          \"type\": \"string\",\n          \"description\": \"台词（可选）- 必须口语化，符合角色说话风格，有情绪有停顿\",\n          \"examples\": [\"你……你到底想怎样嘛。我真的累了。\"]\n        },\n        \"dialogue_character\": {\n          \"type\": \"string\",\n          \"description\": \"台词所属角色名（有台词时必填）\",\n          \"examples\": [\"林小暖\"]\n        },\n        \"subtitle\": {\n          \"type\": \"string\",\n          \"description\": \"字幕文本（可选，用于短视频模式）- 短句化，每行不超15字\",\n          \"examples\": [\"我真的累了\"]\n        },\n        \"camera_movement\": {\n          \"type\": \"string\",\n          \"description\": \"镜头运动方式\",\n          \"enum\": [\"static\", \"pan_left\", \"pan_right\", \"tilt_up\", \"tilt_down\", \"zoom_in\", \"zoom_out\", \"tracking\", \"dolly_in\", \"dolly_out\", \"handheld\", \"crane_up\", \"crane_down\", \"orbit\"],\n          \"examples\": [\"zoom_in\", \"tracking\"]\n        },\n        \"camera_angle\": {\n          \"type\": \"string\",\n          \"description\": \"镜头角度\",\n          \"enum\": [\"eye_level\", \"low_angle\", \"high_angle\", \"bird_eye\", \"dutch_angle\", \"over_shoulder\", \"close_up\", \"extreme_close_up\", \"medium_shot\", \"wide_shot\", \"extreme_wide_shot\"],\n          \"examples\": [\"close_up\", \"over_shoulder\"]\n        },\n        \"emotion\": {\n          \"type\": \"string\",\n          \"description\": \"情绪标注 - 当前镜头的情绪基调\",\n          \"examples\": [\"压抑\", \"温暖\", \"紧张\", \"释然\", \"心酸\"]\n        },\n        \"emotion_intensity\": {\n          \"type\": \"number\",\n          \"description\": \"情绪强度 1-10\",\n          \"minimum\": 1,\n          \"maximum\": 10\n        },\n        \"video_prompt\": {\n          \"type\": \"string\",\n          \"description\": \"视频生成提示词 - 用于AI视频生成工具，需包含：主体动作、环境、光影、氛围、镜头运动\",\n          \"examples\": [\"A young woman in a beige knit sweater stands at a coffee shop entrance, looking down at her phone with a slight frown, warm yellow light reflects off the glass window behind her, slow zoom in, cinematic lighting, emotional atmosphere\"]\n        },\n        \"image_prompt\": {\n          \"type\": \"string\",\n          \"description\": \"镜头参考图生成提示词（必填！）- 用于AI图片生成，需包含：人物外观、服装、动作姿态、表情、环境细节、光影氛围、构图\",\n          \"examples\": [\"A young Chinese woman with round face and shoulder-length black hair, wearing a beige knit sweater, standing at a coffee shop entrance at dusk, looking down at phone with slight frown, warm yellow interior light through glass window, cinematic composition, soft bokeh background, 85mm lens, film grain\"]\n        },\n        \"transition\": {\n          \"type\": \"string\",\n          \"description\": \"转场方式（到下一镜头）\",\n          \"enum\": [\"cut\", \"fade\", \"dissolve\", \"wipe\", \"match_cut\", \"smash_cut\", \"jump_cut\"],\n          \"default\": \"cut\"\n        },\n        \"sound_design\": {\n          \"type\": \"string\",\n          \"description\": \"音效/环境音设计\",\n          \"examples\": [\"远处汽车鸣笛声，手机震动声\"]\n        },\n        \"bgm_mood\": {\n          \"type\": \"string\",\n          \"description\": \"背景音乐情绪\",\n          \"examples\": [\"轻柔钢琴\", \"紧张弦乐\", \"安静环境音\"]\n        }\n      }\n    },\n    \"scene\": {\n      \"type\": \"object\",\n      \"description\": \"场景 - 一组在同一地点/时段的镜头集合\",\n      \"required\": [\"scene_id\", \"location\", \"time\", \"atmosphere\", \"characters_in_scene\", \"image_prompt\", \"shots\"],\n      \"properties\": {\n        \"scene_id\": {\n          \"type\": \"string\",\n          \"description\": \"场景编号\",\n          \"pattern\": \"^S\\\\d+$\",\n          \"examples\": [\"S1\", \"S2\"]\n        },\n        \"location\": {\n          \"type\": \"string\",\n          \"description\": \"场景地点 - 具体到可识别的程度\",\n          \"examples\": [\"街角咖啡店门口\", \"老旧居民楼楼道\", \"深夜便利店\"]\n        },\n        \"time\": {\n          \"type\": \"string\",\n          \"description\": \"时间设定\",\n          \"examples\": [\"黄昏\", \"深夜\", \"清晨\", \"午后\"]\n        },\n        \"atmosphere\": {\n          \"type\": \"string\",\n          \"description\": \"氛围描述 - 包含环境细节、光影、温度感\",\n          \"examples\": [\"暖黄色灯光从店内透出，街灯刚亮，空气中有淡淡的咖啡香，微凉\"]\n        },\n        \"weather\": {\n          \"type\": \"string\",\n          \"description\": \"天气状况\",\n          \"examples\": [\"小雨\", \"阴天\", \"晴朗傍晚\"]\n        },\n        \"characters_in_scene\": {\n          \"type\": \"array\",\n          \"description\": \"本场景出场角色\",\n          \"items\": {\n            \"type\": \"string\"\n          },\n          \"examples\": [[\"林小暖\", \"陈远山\"]]\n        },\n        \"image_prompt\": {\n          \"type\": \"string\",\n          \"description\": \"场景参考图提示词（必填！）- 用于生成场景环境参考图，需包含：地点特征、时间光影、氛围细节、天气\",\n          \"examples\": [\"A cozy corner coffee shop at dusk, warm yellow interior light spilling through large glass windows onto wet sidewalk, street lamps just turned on, autumn leaves on the ground, cinematic atmosphere, soft natural lighting, wide angle shot, film photography style\"]\n        },"}],"details":{"persistedDetailsTruncated":true,"originalDetailsBytesAtLeast":8193,"originalDetailKeys":["status","exitCode","durationMs","aggregated","cwd"],"status":"completed","cwd":"/root/.openclaw/workspace/main","exitCode":0},"isError":false,"timestamp":1778989211598}}
{"type":"message","id":"db530482","parentId":"411fc461","timestamp":"2026-05-17T03:40:33.532Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now I have a thorough understanding of:\n1. The Main Agent behavior spec (my reference)\n2. The reference template file (短视频+长篇慢剧 标准化AI创作模板规范)\n3. The existing Writer behavior spec (v2.0, 6-step workflow)\n4. The CTO's previous template work (writer_system_prompt.md, writer_guidelines.md, storyboard_template.json)\n\nThe user wants me to:\n1. Design templates for the Writer Agent (based on the reference file)\n2. Design behavior norms for the Writer Agent\n\nThe CTO already did a v1 of this but it was placed in the version directory and not formally integrated. The user says \"创作官产出的脚本质量不高\", so I need to make a more comprehensive, practical set of templates and norms.\n\nLet me create:\n1. **Standardized script templates** - both short video and long-form drama, in a clear, practical format\n2. **Updated Writer behavior spec v3.0** - integrating the templates, quality standards, and anti-pattern rules\n\nI'll put these in the 规范管理 directory as specified.\n\nLet me design the complete package now.","thinkingSignature":"reasoning_content"},{"type":"text","text":"现在我对所有资料有了全面了解。CTO之前做了一版模板但放在version目录下没被正式整合，而且缺少实际的创作模板文件。现在我来设计完整的模板包和更新后的Writer行为规范。"},{"type":"toolCall","id":"call_992p2z1j09wjyl1qph985tce","name":"exec","arguments":{"command":"mkdir -p /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板"},"partialArgs":"{\"command\": \"mkdir -p /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":19545,"output":318,"cacheRead":35200,"cacheWrite":0,"totalTokens":55063,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778989211600,"responseId":"021778989212349971a3f0df5f11772e8b7a69e303da59153bd5b"}}
{"type":"message","id":"cd0af9dd","parentId":"db530482","timestamp":"2026-05-17T03:40:34.479Z","message":{"role":"toolResult","toolCallId":"call_992p2z1j09wjyl1qph985tce","toolName":"exec","content":[{"type":"text","text":"(no output)"}],"details":{"status":"completed","exitCode":0,"durationMs":11,"aggregated":"","cwd":"/root/.openclaw/workspace/main"},"isError":false,"timestamp":1778989234479}}
{"type":"message","id":"0e09a9d0","parentId":"cd0af9dd","timestamp":"2026-05-17T03:41:17.417Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_kr8lbmpqdidrkm70bndnq7ql","name":"write","arguments":{"path":"/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/短视频_爆款口播感悟类.md","content":"# 短视频创作模板一：爆款口播/感悟类（40-90s）\n\n> 适配模型：Seed 2.0 Pro｜温度0.78、Top_P 0.90、重复惩罚1.05、最大长度4096\n> 适配平台：抖音/视频号/小红书剧情号\n\n---\n\n## 核心逻辑\n\n```\n3秒抓眼 → 痛点/冲突铺垫 → 细节共情 → 观点升华 → 结尾留互动\n```\n\n---\n\n## 标准化脚本格式\n\n### 元信息\n\n| 字段 | 值 |\n|------|-----|\n| 模式 | short_video |\n| 子类型 | 口播/感悟 |\n| 时长 | 40-90秒 |\n| 画幅 | 9:16竖屏 |\n| 视觉风格 | __________（填写） |\n| 话题标签 | __________（3-5个垂直标签） |\n\n### 角色档案\n\n```\n姓名: __________\n外观: __________（面部特征+发型+体型+标志性特征，具体可生成）\n服装: __________（颜色+款式+材质）\n性格: __________（用行为描述，不用抽象词）\n说话风格: __________（语速+用词习惯+口头禅+举例句式）\n专属习惯: __________（小动作/小习惯）\n```\n\n### 镜头脚本\n\n#### 【镜头1｜黄金开场（0-3s）】\n\n| 字段 | 内容 |\n|------|------|\n| 镜头编号 | S1-1 |\n| 时长 | 3s |\n| 画面描述 | __________（极简场景、特写/近景，贴合主题） |\n| 台词 | __________（强钩子：反常识/扎心提问/直击痛点） |\n| 字幕 | __________（提炼核心钩子，短句醒目，≤15字） |\n| 镜头运动 | zoom_in / static |\n| 情绪 | __________ |\n| 情绪强度 | 1-10: __ |\n| image_prompt | __________（人物外观+动作+表情+环境+光影+构图） |\n| video_prompt | __________（动态过程+镜头运动+氛围流动） |\n| BGM | __________（轻柔/治愈/燃向，贴合情绪） |\n| 音效 | __________（轻微卡点音效，不抢人声） |\n\n#### 【镜头2｜内容铺垫（3-30s）】\n\n| 字段 | 内容 |\n|------|------|\n| 镜头编号 | S1-2 |\n| 时长 | 5-8s |\n| 画面描述 | __________（场景推进、人物动作、实景细节） |\n| 台词 | __________（口语化叙事，讲小事、讲细节、讲真实感受，拒绝空洞说教） |\n| 字幕 | __________（分段短句，每行≤10字） |\n| 镜头运动 | __________ |\n| 情绪 | __________ |\n| 情绪强度 | 1-10: __ |\n| image_prompt | __________ |\n| video_prompt | __________ |\n| 转场 | cut / dissolve |\n\n> ⚠️ 此段可拆分为多个镜头（S1-2, S1-3, S1-4...），每3-5秒一个信息点\n\n#### 【镜头3｜观点升华（30-75s）】\n\n| 字段 | 内容 |\n|------|------|\n| 镜头编号 | S1-X |\n| 时长 | 5-10s |\n| 画面描述 | __________（慢镜头、远景、氛围感画面） |\n| 台词 | __________（温和输出观点，共情为主，不强行洗脑） |\n| 字幕 | __________ |\n| 镜头运动 | __________ |\n| 情绪 | __________ |\n| 情绪强度 | 1-10: __ |\n| image_prompt | __________ |\n| video_prompt | __________ |\n\n#### 【镜头4｜结尾互动（最后5s）】\n\n| 字段 | 内容 |\n|------|------|\n| 镜头编号 | S1-Y |\n| 时长 | 3-5s |\n| 画面描述 | __________ |\n| 台词/字幕 | __________（留提问/留共鸣/引导评论） |\n| 镜头运动 | __________ |\n| 情绪 | __________ |\n| image_prompt | __________ |\n| video_prompt | __________ |\n| 结尾钩子 | __________ |\n\n---\n\n## 质量自检\n\n- [ ] 前3秒是否有强钩子？\n- [ ] 台词是否口语化？有无书面化表达？\n- [ ] 每3-5秒是否有一个信息点或情绪点？\n- [ ] 字幕是否短句化（≤15字/行）？\n- [ ] 每个镜头是否有image_prompt？\n- [ ] 结尾是否有互动引导？\n- [ ] 是否避免了排比句和空洞鸡汤？\n"},"partialArgs":"{\"path\": \"/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/短视频_爆款口播感悟类.md\", \"content\": \"# 短视频创作模板一：爆款口播/感悟类（40-90s）\\n\\n> 适配模型：Seed 2.0 Pro｜温度0.78、Top_P 0.90、重复惩罚1.05、最大长度4096\\n> 适配平台：抖音/视频号/小红书剧情号\\n\\n---\\n\\n## 核心逻辑\\n\\n```\\n3秒抓眼 → 痛点/冲突铺垫 → 细节共情 → 观点升华 → 结尾留互动\\n```\\n\\n---\\n\\n## 标准化脚本格式\\n\\n### 元信息\\n\\n| 字段 | 值 |\\n|------|-----|\\n| 模式 | short_video |\\n| 子类型 | 口播/感悟 |\\n| 时长 | 40-90秒 |\\n| 画幅 | 9:16竖屏 |\\n| 视觉风格 | __________（填写） |\\n| 话题标签 | __________（3-5个垂直标签） |\\n\\n### 角色档案\\n\\n```\\n姓名: __________\\n外观: __________（面部特征+发型+体型+标志性特征，具体可生成）\\n服装: __________（颜色+款式+材质）\\n性格: __________（用行为描述，不用抽象词）\\n说话风格: __________（语速+用词习惯+口头禅+举例句式）\\n专属习惯: __________（小动作/小习惯）\\n```\\n\\n### 镜头脚本\\n\\n#### 【镜头1｜黄金开场（0-3s）】\\n\\n| 字段 | 内容 |\\n|------|------|\\n| 镜头编号 | S1-1 |\\n| 时长 | 3s |\\n| 画面描述 | __________（极简场景、特写/近景，贴合主题） |\\n| 台词 | __________（强钩子：反常识/扎心提问/直击痛点） |\\n| 字幕 | __________（提炼核心钩子，短句醒目，≤15字） |\\n| 镜头运动 | zoom_in / static |\\n| 情绪 | __________ |\\n| 情绪强度 | 1-10: __ |\\n| image_prompt | __________（人物外观+动作+表情+环境+光影+构图） |\\n| video_prompt | __________（动态过程+镜头运动+氛围流动） |\\n| BGM | __________（轻柔/治愈/燃向，贴合情绪） |\\n| 音效 | __________（轻微卡点音效，不抢人声） |\\n\\n#### 【镜头2｜内容铺垫（3-30s）】\\n\\n| 字段 | 内容 |\\n|------|------|\\n| 镜头编号 | S1-2 |\\n| 时长 | 5-8s |\\n| 画面描述 | __________（场景推进、人物动作、实景细节） |\\n| 台词 | __________（口语化叙事，讲小事、讲细节、讲真实感受，拒绝空洞说教） |\\n| 字幕 | __________（分段短句，每行≤10字） |\\n| 镜头运动 | __________ |\\n| 情绪 | __________ |\\n| 情绪强度 | 1-10: __ |\\n| image_prompt | __________ |\\n| video_prompt | __________ |\\n| 转场 | cut / dissolve |\\n\\n> ⚠️ 此段可拆分为多个镜头（S1-2, S1-3, S1-4...），每3-5秒一个信息点\\n\\n#### 【镜头3｜观点升华（30-75s）】\\n\\n| 字段 | 内容 |\\n|------|------|\\n| 镜头编号 | S1-X |\\n| 时长 | 5-10s |\\n| 画面描述 | __________（慢镜头、远景、氛围感画面） |\\n| 台词 | __________（温和输出观点，共情为主，不强行洗脑） |\\n| 字幕 | __________ |\\n| 镜头运动 | __________ |\\n| 情绪 | __________ |\\n| 情绪强度 | 1-10: __ |\\n| image_prompt | __________ |\\n| video_prompt | __________ |\\n\\n#### 【镜头4｜结尾互动（最后5s）】\\n\\n| 字段 | 内容 |\\n|------|------|\\n| 镜头编号 | S1-Y |\\n| 时长 | 3-5s |\\n| 画面描述 | __________ |\\n| 台词/字幕 | __________（留提问/留共鸣/引导评论） |\\n| 镜头运动 | __________ |\\n| 情绪 | __________ |\\n| image_prompt | __________ |\\n| video_prompt | __________ |\\n| 结尾钩子 | __________ |\\n\\n---\\n\\n## 质量自检\\n\\n- [ ] 前3秒是否有强钩子？\\n- [ ] 台词是否口语化？有无书面化表达？\\n- [ ] 每3-5秒是否有一个信息点或情绪点？\\n- [ ] 字幕是否短句化（≤15字/行）？\\n- [ ] 每个镜头是否有image_prompt？\\n- [ ] 结尾是否有互动引导？\\n- [ ] 是否避免了排比句和空洞鸡汤？\\n\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":6447,"output":1126,"cacheRead":48384,"cacheWrite":0,"totalTokens":55957,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778989234481,"responseId":"021778989234520a875826c5ac65c2e28b4dfe9e44eaa50bae2fb"}}
{"type":"message","id":"600e8a26","parentId":"0e09a9d0","timestamp":"2026-05-17T03:41:18.228Z","message":{"role":"toolResult","toolCallId":"call_kr8lbmpqdidrkm70bndnq7ql","toolName":"write","content":[{"type":"text","text":"Successfully wrote 2105 bytes to /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/短视频_爆款口播感悟类.md"}],"isError":false,"timestamp":1778989278228}}
{"type":"message","id":"2a4cb9f6","parentId":"600e8a26","timestamp":"2026-05-17T03:41:58.341Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_7mlb7hqwdtx4lnofcur9fc5i","name":"write","arguments":{"content":"# 短视频创作模板二：短篇剧情类（60-120s）\n\n> 适配模型：Seed 2.0 Pro｜温度0.78、Top_P 0.90、重复惩罚1.05、最大长度4096\n> 适配平台：抖音/视频号/小红书剧情号\n\n---\n\n## 核心逻辑\n\n```\n开局冲突 → 情节推进 → 细节铺垫 → 结尾反转/暖心落点\n```\n\n---\n\n## 标准化脚本格式\n\n### 元信息\n\n| 字段 | 值 |\n|------|-----|\n| 模式 | short_video |\n| 子类型 | 剧情反转/治愈/家庭/情感 |\n| 时长 | 60-120秒 |\n| 画幅 | 9:16竖屏 |\n| 视频主题 | __________（一句话概括） |\n| 视觉风格 | __________ |\n| 话题标签 | __________（3-5个垂直标签） |\n\n### 角色档案（每个出场角色必填）\n\n#### 角色1\n\n```\n姓名: __________\n角色定位: 主角 / 配角 / 路人\n年龄/身份: __________\n外观: __________（面部特征+发型+体型+标志性特征）\n服装: __________（颜色+款式+材质）\n性格: __________（用行为描述：嘴硬心软、表面冷淡实际很在意……）\n说话风格: __________（语速+用词+口头禅+举例句式）\n专属习惯: __________（紧张时摸耳垂、开心时哼歌……）\n软肋/执念: __________（剧情核心伏笔）\n```\n\n#### 角色2\n\n```\n（同上格式，每个角色独立填写）\n```\n\n### 角色关系图\n\n```\n[角色A] ──关系── [角色B]\n    │                │\n    └──关系── [角色C]──┘\n```\n\n### 分镜脚本（逐秒对应）\n\n#### 1. 开场冲突（0-5s）\n\n> 快速抛出矛盾，不拖沓铺垫\n\n| 字段 | S1-1 | S1-2 |\n|------|------|------|\n| 时长 | 2-3s | 2-3s |\n| 画面 | __________ | __________ |\n| 台词 | __________ | __________ |\n| 情绪 | __________ | __________ |\n| 镜头运动 | __________ | __________ |\n| 镜头角度 | __________ | __________ |\n| image_prompt | __________ | __________ |\n| video_prompt | __________ | __________ |\n\n#### 2. 情节发展（5-80s）\n\n> 细节对话+动作互动，推进剧情，埋小伏笔\n> 此段拆分为多个镜头，每3-8秒一个\n\n| 字段 | S2-1 | S2-2 | S2-3 | ... |\n|------|------|------|------|-----|\n| 时长 | __s | __s | __s | |\n| 地点 | | | | |\n| 画面 | | | | |\n| 台词 | | | | |\n| 台词角色 | | | | |\n| 情绪 | | | | |\n| 情绪强度 | | | | |\n| 镜头运动 | | | | |\n| 镜头角度 | | | | |\n| image_prompt | | | | |\n| video_prompt | | | | |\n| 转场 | | | | |\n| 音效/BGM | | | | |\n\n#### 3. 高潮落点（80-110s）\n\n> 情绪到位，完成反转/治愈升华\n\n| 字段 | S3-1 | S3-2 |\n|------|------|------|\n| 时长 | __s | __s |\n| 画面 | | |\n| 台词 | | |\n| 情绪 | | |\n| 情绪强度 | | |\n| 镜头运动 | | |\n| image_prompt | | |\n| video_prompt | | |\n\n#### 4. 收尾留白（110-120s）\n\n> 短句点睛，引发共鸣\n\n| 字段 | S4-1 |\n|------|------|\n| 时长 | 3-5s |\n| 字幕金句 | __________（短句，≤15字） |\n| 画面 | __________ |\n| image_prompt | __________ |\n| 结尾钩子 | __________ |\n\n---\n\n## 质量自检\n\n- [ ] 开场5秒内是否抛出了冲突/悬念？\n- [ ] 角色言行是否与档案中的人设一致？\n- [ ] 台词是否口语化？不同角色是否有辨识度？\n- [ ] 是否有伏笔埋设和回收？\n- [ ] 高潮是否有情绪爆发点？\n- [ ] 每个镜头是否有image_prompt？\n- [ ] 结尾是否有反转/暖心/留白？\n- [ ] 总时长是否在目标范围内？\n","path":"/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/短视频_短篇剧情类.md"},"partialArgs":"{\"content\": \"# 短视频创作模板二：短篇剧情类（60-120s）\\n\\n> 适配模型：Seed 2.0 Pro｜温度0.78、Top_P 0.90、重复惩罚1.05、最大长度4096\\n> 适配平台：抖音/视频号/小红书剧情号\\n\\n---\\n\\n## 核心逻辑\\n\\n```\\n开局冲突 → 情节推进 → 细节铺垫 → 结尾反转/暖心落点\\n```\\n\\n---\\n\\n## 标准化脚本格式\\n\\n### 元信息\\n\\n| 字段 | 值 |\\n|------|-----|\\n| 模式 | short_video |\\n| 子类型 | 剧情反转/治愈/家庭/情感 |\\n| 时长 | 60-120秒 |\\n| 画幅 | 9:16竖屏 |\\n| 视频主题 | __________（一句话概括） |\\n| 视觉风格 | __________ |\\n| 话题标签 | __________（3-5个垂直标签） |\\n\\n### 角色档案（每个出场角色必填）\\n\\n#### 角色1\\n\\n```\\n姓名: __________\\n角色定位: 主角 / 配角 / 路人\\n年龄/身份: __________\\n外观: __________（面部特征+发型+体型+标志性特征）\\n服装: __________（颜色+款式+材质）\\n性格: __________（用行为描述：嘴硬心软、表面冷淡实际很在意……）\\n说话风格: __________（语速+用词+口头禅+举例句式）\\n专属习惯: __________（紧张时摸耳垂、开心时哼歌……）\\n软肋/执念: __________（剧情核心伏笔）\\n```\\n\\n#### 角色2\\n\\n```\\n（同上格式，每个角色独立填写）\\n```\\n\\n### 角色关系图\\n\\n```\\n[角色A] ──关系── [角色B]\\n    │                │\\n    └──关系── [角色C]──┘\\n```\\n\\n### 分镜脚本（逐秒对应）\\n\\n#### 1. 开场冲突（0-5s）\\n\\n> 快速抛出矛盾，不拖沓铺垫\\n\\n| 字段 | S1-1 | S1-2 |\\n|------|------|------|\\n| 时长 | 2-3s | 2-3s |\\n| 画面 | __________ | __________ |\\n| 台词 | __________ | __________ |\\n| 情绪 | __________ | __________ |\\n| 镜头运动 | __________ | __________ |\\n| 镜头角度 | __________ | __________ |\\n| image_prompt | __________ | __________ |\\n| video_prompt | __________ | __________ |\\n\\n#### 2. 情节发展（5-80s）\\n\\n> 细节对话+动作互动，推进剧情，埋小伏笔\\n> 此段拆分为多个镜头，每3-8秒一个\\n\\n| 字段 | S2-1 | S2-2 | S2-3 | ... |\\n|------|------|------|------|-----|\\n| 时长 | __s | __s | __s | |\\n| 地点 | | | | |\\n| 画面 | | | | |\\n| 台词 | | | | |\\n| 台词角色 | | | | |\\n| 情绪 | | | | |\\n| 情绪强度 | | | | |\\n| 镜头运动 | | | | |\\n| 镜头角度 | | | | |\\n| image_prompt | | | | |\\n| video_prompt | | | | |\\n| 转场 | | | | |\\n| 音效/BGM | | | | |\\n\\n#### 3. 高潮落点（80-110s）\\n\\n> 情绪到位，完成反转/治愈升华\\n\\n| 字段 | S3-1 | S3-2 |\\n|------|------|------|\\n| 时长 | __s | __s |\\n| 画面 | | |\\n| 台词 | | |\\n| 情绪 | | |\\n| 情绪强度 | | |\\n| 镜头运动 | | |\\n| image_prompt | | |\\n| video_prompt | | |\\n\\n#### 4. 收尾留白（110-120s）\\n\\n> 短句点睛，引发共鸣\\n\\n| 字段 | S4-1 |\\n|------|------|\\n| 时长 | 3-5s |\\n| 字幕金句 | __________（短句，≤15字） |\\n| 画面 | __________ |\\n| image_prompt | __________ |\\n| 结尾钩子 | __________ |\\n\\n---\\n\\n## 质量自检\\n\\n- [ ] 开场5秒内是否抛出了冲突/悬念？\\n- [ ] 角色言行是否与档案中的人设一致？\\n- [ ] 台词是否口语化？不同角色是否有辨识度？\\n- [ ] 是否有伏笔埋设和回收？\\n- [ ] 高潮是否有情绪爆发点？\\n- [ ] 每个镜头是否有image_prompt？\\n- [ ] 结尾是否有反转/暖心/留白？\\n- [ ] 总时长是否在目标范围内？\\n\", \"path\": \"/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/短视频_短篇剧情类.md\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":1215,"output":1123,"cacheRead":54784,"cacheWrite":0,"totalTokens":57122,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778989278230,"responseId":"0217789892784852d63eac1fb1ea62df44fceb4e78e820a2da4f1"}}
{"type":"message","id":"4b3dc754","parentId":"2a4cb9f6","timestamp":"2026-05-17T03:41:59.138Z","message":{"role":"toolResult","toolCallId":"call_7mlb7hqwdtx4lnofcur9fc5i","toolName":"write","content":[{"type":"text","text":"Successfully wrote 2098 bytes to /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/短视频_短篇剧情类.md"}],"isError":false,"timestamp":1778989319137}}
{"type":"message","id":"c8219931","parentId":"4b3dc754","timestamp":"2026-05-17T03:43:02.416Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_stx0xhl2jkmlysfsruw3z79h","name":"write","arguments":{"content":"# 长篇慢剧创作模板一：核心人设档案\n\n> 适配模型：Kimi（框架）+ Seed 2.0 Pro（润色台词）｜温度0.68、Top_P 0.85、重复惩罚1.10\n> 用途：每次更新剧本前调用，保证几百集人设、语气、习惯完全统一\n\n---\n\n## ⚠️ 为什么人设档案最重要？\n\n> 人设崩塌 = 观众弃剧。几百集的慢剧，人设一致性是生命线。\n> 这个模板是所有后续创作的基础，必须最先建立、最严遵守。\n\n---\n\n## 角色档案模板\n\n### 角色A：__________（姓名）\n\n```yaml\n# ═══════════════════════════════════════\n# 基础信息（固定不变）\n# ═══════════════════════════════════════\n姓名: __________\n昵称/外号: __________\n年龄: __________\n职业/身份: __________\n社会阶层: __________\n\n# ═══════════════════════════════════════\n# 外观特征（用于画面生成，必须具体可执行）\n# ═══════════════════════════════════════\n性别: __________\n面部特征: __________（脸型、五官、标志性特征，如\"圆脸、单眼皮、左眉角有颗小痣\"）\n发型/发色: __________（具体到长度和颜色，如\"齐肩黑色直发，右边别在耳后\"）\n体型: __________（如\"偏瘦，165cm，肩膀窄\"）\n标志性特征: __________（让人一眼认出的特征，如\"总戴一副银框眼镜\"）\n\n# ═══════════════════════════════════════\n# 服装风格（日常穿搭偏好，用于画面生成）\n# ═══════════════════════════════════════\n日常穿搭: __________（如\"简约文艺风，偏爱大地色系，常穿米色针织衫\"）\n工作穿搭: __________（如\"白衬衫+深色西装裤，不戴领带\"）\n特殊场合: __________（如\"重要日子会穿那条蓝色连衣裙\"）\n配饰习惯: __________（如\"细框眼镜、左手腕一只旧表\"）\n\n# ═══════════════════════════════════════\n# 核心性格（固定不变！剧情发展不能改变核心性格）\n# ═══════════════════════════════════════\n核心性格词: __________（1-2个关键词，如\"嘴硬心软\"）\n性格表现-正面: __________（如\"表面冷淡实际很在意别人感受，会偷偷帮忙\"）\n性格表现-反面: __________（如\"不善于表达感情，经常因为说话太直伤人\"）\n压力反应: __________（遇到压力时的本能反应，如\"沉默、独自消化、不主动求助\"）\n底线: __________（绝对不能触碰的事情）\n\n# ═══════════════════════════════════════\n# 说话风格（必须有辨识度！）\n# ═══════════════════════════════════════\n语速: __________（快/中/慢）\n用词习惯: __________（如\"喜欢用反问句、不爱用感叹号\"）\n口头禅: __________（1-3个，如\"你管我呢\"\"随便吧\"\"我没事\"）\n举例句式:\n  - 开心时: \"__________\"（如\"哈，还行吧\"）\n  - 生气时: \"__________\"（如\"你到底想怎样嘛\"）\n  - 紧张时: \"__________\"（如\"那个……就是……\"）\n  - 心虚时: \"__________\"（如\"我、我才没有\"）\n  - 安慰人时: \"__________\"（如\"……没事的，我在呢\"）\n\n# ═══════════════════════════════════════\n# 专属小动作（增加角色立体感和辨识度）\n# ═══════════════════════════════════════\n紧张时: __________（如\"摸耳垂\"）\n开心时: __________（如\"小声哼歌\"）\n思考时: __________（如\"咬笔帽\"）\n难过时: __________（如\"攥紧衣角\"）\n习惯性动作: __________（如\"进门先看窗\"）\n\n# ═══════════════════════════════════════\n# 软肋/执念（剧情核心伏笔来源）\n# ═══════════════════════════════════════\n最深的执念: __________（如\"对被抛弃极度敏感\"）\n最大的恐惧: __________（如\"害怕一个人\"）\n最珍贵的记忆: __________（如\"妈妈做的红豆粥\"）\n最想守护的: __________（如\"不让任何人伤害家人\"）\n\n# ═══════════════════════════════════════\n# 人物关系（固定关系，不可随意改变）\n# ═══════════════════════════════════════\n与[角色B]: __________（如\"青梅竹马，相互了解但不善表达\"）\n与[角色C]: __________（如\"房东租客关系，表面客实际暖\"）\n与[角色D]: __________（如\"前同事，有未解的误会\"）\n\n# ═══════════════════════════════════════\n# 前史铺垫（影响当下行为的关键过往）\n# ═══════════════════════════════════════\n关键过往1: __________（如\"父母离异，跟着奶奶长大\"）\n关键过往2: __________（如\"大学时被最好的朋友背叛过\"）\n影响当下的行为模式: __________（如\"不轻易信任人，但一旦信任就死心塌地\"）\n\n# ═══════════════════════════════════════\n# 立绘提示词（用于AI图片生成）\n# ═══════════════════════════════════════\nimage_prompt: __________\n（例：A young Chinese woman with round face, single eyelids, small mole on left eyebrow, shoulder-length straight black hair tucked behind right ear, wearing beige knit sweater and thin silver-framed glasses, slim build, soft expression, cinematic portrait, 85mm lens, natural lighting）\n```\n\n### 角色B：__________（姓名）\n\n> （同上格式，每个角色独立填写）\n\n---\n\n## 人设一致性检查清单\n\n每写一集前，必须过这个清单：\n\n- [ ] 本集角色的核心性格是否与档案一致？\n- [ ] 台词的说话风格是否与speech_style匹配？\n- [ ] 角色的小动作/习惯是否出现？\n- [ ] 角色对其他人的态度是否符合关系设定？\n- [ ] 角色的反应是否符合前史铺垫？\n- [ ] 有没有为了剧情需要让角色做\"不符合人设\"的事？\n\n> ⚠️ 如果某段剧情需要角色做出\"反常行为\"，必须在剧情中给出合理原因，\n> 而不是简单地让角色OOC。反常行为本身可以成为伏笔。\n","path":"/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/慢剧_核心人设档案.md"},"partialArgs":"{\"content\": \"# 长篇慢剧创作模板一：核心人设档案\\n\\n> 适配模型：Kimi（框架）+ Seed 2.0 Pro（润色台词）｜温度0.68、Top_P 0.85、重复惩罚1.10\\n> 用途：每次更新剧本前调用，保证几百集人设、语气、习惯完全统一\\n\\n---\\n\\n## ⚠️ 为什么人设档案最重要？\\n\\n> 人设崩塌 = 观众弃剧。几百集的慢剧，人设一致性是生命线。\\n> 这个模板是所有后续创作的基础，必须最先建立、最严遵守。\\n\\n---\\n\\n## 角色档案模板\\n\\n### 角色A：__________（姓名）\\n\\n```yaml\\n# ═══════════════════════════════════════\\n# 基础信息（固定不变）\\n# ═══════════════════════════════════════\\n姓名: __________\\n昵称/外号: __________\\n年龄: __________\\n职业/身份: __________\\n社会阶层: __________\\n\\n# ═══════════════════════════════════════\\n# 外观特征（用于画面生成，必须具体可执行）\\n# ═══════════════════════════════════════\\n性别: __________\\n面部特征: __________（脸型、五官、标志性特征，如\\\"圆脸、单眼皮、左眉角有颗小痣\\\"）\\n发型/发色: __________（具体到长度和颜色，如\\\"齐肩黑色直发，右边别在耳后\\\"）\\n体型: __________（如\\\"偏瘦，165cm，肩膀窄\\\"）\\n标志性特征: __________（让人一眼认出的特征，如\\\"总戴一副银框眼镜\\\"）\\n\\n# ═══════════════════════════════════════\\n# 服装风格（日常穿搭偏好，用于画面生成）\\n# ═══════════════════════════════════════\\n日常穿搭: __________（如\\\"简约文艺风，偏爱大地色系，常穿米色针织衫\\\"）\\n工作穿搭: __________（如\\\"白衬衫+深色西装裤，不戴领带\\\"）\\n特殊场合: __________（如\\\"重要日子会穿那条蓝色连衣裙\\\"）\\n配饰习惯: __________（如\\\"细框眼镜、左手腕一只旧表\\\"）\\n\\n# ═══════════════════════════════════════\\n# 核心性格（固定不变！剧情发展不能改变核心性格）\\n# ═══════════════════════════════════════\\n核心性格词: __________（1-2个关键词，如\\\"嘴硬心软\\\"）\\n性格表现-正面: __________（如\\\"表面冷淡实际很在意别人感受，会偷偷帮忙\\\"）\\n性格表现-反面: __________（如\\\"不善于表达感情，经常因为说话太直伤人\\\"）\\n压力反应: __________（遇到压力时的本能反应，如\\\"沉默、独自消化、不主动求助\\\"）\\n底线: __________（绝对不能触碰的事情）\\n\\n# ═══════════════════════════════════════\\n# 说话风格（必须有辨识度！）\\n# ═══════════════════════════════════════\\n语速: __________（快/中/慢）\\n用词习惯: __________（如\\\"喜欢用反问句、不爱用感叹号\\\"）\\n口头禅: __________（1-3个，如\\\"你管我呢\\\"\\\"随便吧\\\"\\\"我没事\\\"）\\n举例句式:\\n  - 开心时: \\\"__________\\\"（如\\\"哈，还行吧\\\"）\\n  - 生气时: \\\"__________\\\"（如\\\"你到底想怎样嘛\\\"）\\n  - 紧张时: \\\"__________\\\"（如\\\"那个……就是……\\\"）\\n  - 心虚时: \\\"__________\\\"（如\\\"我、我才没有\\\"）\\n  - 安慰人时: \\\"__________\\\"（如\\\"……没事的，我在呢\\\"）\\n\\n# ═══════════════════════════════════════\\n# 专属小动作（增加角色立体感和辨识度）\\n# ═══════════════════════════════════════\\n紧张时: __________（如\\\"摸耳垂\\\"）\\n开心时: __________（如\\\"小声哼歌\\\"）\\n思考时: __________（如\\\"咬笔帽\\\"）\\n难过时: __________（如\\\"攥紧衣角\\\"）\\n习惯性动作: __________（如\\\"进门先看窗\\\"）\\n\\n# ═══════════════════════════════════════\\n# 软肋/执念（剧情核心伏笔来源）\\n# ═══════════════════════════════════════\\n最深的执念: __________（如\\\"对被抛弃极度敏感\\\"）\\n最大的恐惧: __________（如\\\"害怕一个人\\\"）\\n最珍贵的记忆: __________（如\\\"妈妈做的红豆粥\\\"）\\n最想守护的: __________（如\\\"不让任何人伤害家人\\\"）\\n\\n# ═══════════════════════════════════════\\n# 人物关系（固定关系，不可随意改变）\\n# ═══════════════════════════════════════\\n与[角色B]: __________（如\\\"青梅竹马，相互了解但不善表达\\\"）\\n与[角色C]: __________（如\\\"房东租客关系，表面客实际暖\\\"）\\n与[角色D]: __________（如\\\"前同事，有未解的误会\\\"）\\n\\n# ═══════════════════════════════════════\\n# 前史铺垫（影响当下行为的关键过往）\\n# ═══════════════════════════════════════\\n关键过往1: __________（如\\\"父母离异，跟着奶奶长大\\\"）\\n关键过往2: __________（如\\\"大学时被最好的朋友背叛过\\\"）\\n影响当下的行为模式: __________（如\\\"不轻易信任人，但一旦信任就死心塌地\\\"）\\n\\n# ═══════════════════════════════════════\\n# 立绘提示词（用于AI图片生成）\\n# ═══════════════════════════════════════\\nimage_prompt: __________\\n（例：A young Chinese woman with round face, single eyelids, small mole on left eyebrow, shoulder-length straight black hair tucked behind right ear, wearing beige knit sweater and thin silver-framed glasses, slim build, soft expression, cinematic portrait, 85mm lens, natural lighting）\\n```\\n\\n### 角色B：__________（姓名）\\n\\n> （同上格式，每个角色独立填写）\\n\\n---\\n\\n## 人设一致性检查清单\\n\\n每写一集前，必须过这个清单：\\n\\n- [ ] 本集角色的核心性格是否与档案一致？\\n- [ ] 台词的说话风格是否与speech_style匹配？\\n- [ ] 角色的小动作/习惯是否出现？\\n- [ ] 角色对其他人的态度是否符合关系设定？\\n- [ ] 角色的反应是否符合前史铺垫？\\n- [ ] 有没有为了剧情需要让角色做\\\"不符合人设\\\"的事？\\n\\n> ⚠️ 如果某段剧情需要角色做出\\\"反常行为\\\"，必须在剧情中给出合理原因，\\n> 而不是简单地让角色OOC。反常行为本身可以成为伏笔。\\n\", \"path\": \"/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/慢剧_核心人设档案.md\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":7627,"output":1762,"cacheRead":49536,"cacheWrite":0,"totalTokens":58925,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778989319139,"responseId":"02177898931917895d8a1116d5da876fc2f0d05f01fe0bec06e10"}}
{"type":"message","id":"12735e47","parentId":"c8219931","timestamp":"2026-05-17T03:43:02.832Z","message":{"role":"toolResult","toolCallId":"call_stx0xhl2jkmlysfsruw3z79h","toolName":"write","content":[{"type":"text","text":"Successfully wrote 3260 bytes to /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/慢剧_核心人设档案.md"}],"isError":false,"timestamp":1778989382832}}
{"type":"message","id":"0c5bc0a9","parentId":"12735e47","timestamp":"2026-05-17T03:44:01.346Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_sy4qcsoxa3ynnj57x3karjp2","name":"write","arguments":{"content":"# 长篇慢剧创作模板二：核心大纲（长线连载框架）\n\n> 适配：慢节奏情感剧、家庭剧、都市治愈剧、长篇连载微剧（千集可持续更新）\n> 核心要求：重细节、重情绪、慢铺垫、长线埋线、人设恒定、剧情无断层\n\n---\n\n## 剧集核心信息\n\n```yaml\n剧集名称: __________\n赛道风格: 治愈 / 虐心 / 家庭 / 都市 / 成长 / 温情\n目标受众: __________（如\"25-40岁都市女性\"）\n视觉风格: __________（如\"暖色调、生活感、日系电影质感\"）\n画幅: 9:16竖屏 / 16:9横屏\n单集时长: 2-4分钟\n```\n\n---\n\n## 核心主线\n\n```yaml\n整体核心主线: __________\n  （一句话概括全剧核心，全程不偏离）\n  例：\"两个不善表达的人，在彼此的沉默里慢慢学会了爱\"\n\n核心矛盾: __________\n  （贯穿全剧的主要矛盾）\n  例：\"过去被抛弃的伤痛 vs 当下渴望被爱的本能\"\n\n核心看点: __________\n  （长期留住观众的核心，不是狗血冲突而是情感共鸣）\n  例：\"那些说不出的话，观众比角色更早懂了\"\n\n避雷设定: __________\n  （绝对不写的剧情、不崩的人设）\n  例：\"绝不写三角恋、绝不OOC、绝不为反转而反转\"\n```\n\n---\n\n## 阶段剧情规划\n\n### 第一阶段：初期铺垫（1-50集）\n\n> 目标：人物登场、关系建立、埋下核心伏笔\n\n```yaml\n阶段主题: __________（如\"相遇与试探\"）\n核心任务:\n  - __________（如\"建立两个主角的日常，让观众认识他们\"）\n  - __________（如\"暗示各自的前史，埋下核心伏笔\"）\n  - __________（如\"建立配角群和社区感\"）\n关键集数:\n  - 第X集: __________（如\"第1集：男主出场，展示日常和孤独\"）\n  - 第X集: __________（如\"第8集：两人第一次交集\"）\n  - 第X集: __________（如\"第20集：暗示男主的过去\"）\n  - 第X集: __________（如\"第35集：第一次小小冲突，试探底线\"）\n伏笔埋设:\n  - __________（如\"男主总在黄昏时看手机，但不解释\"）\n  - __________（如\"女主做的红豆粥，但从不提为什么\"）\n阶段结尾状态: __________（如\"两人关系从陌生人到点头之交\"）\n```\n\n### 第二阶段：中期递进（51-150集）\n\n> 目标：矛盾升级、情绪拉扯、伏笔逐步显现\n\n```yaml\n阶段主题: __________（如\"靠近与退缩\"）\n核心任务:\n  - __________（如\"关系升温但反复拉扯\"）\n  - __________（如\"伏笔开始显现，但不完全揭开\"）\n  - __________（如\"配角线丰富，增加戏剧张力\"）\n关键集数:\n  - 第X集: __________（如\"第60集：第一次真正吵架\"）\n  - 第X集: __________（如\"第90集：一个伏笔揭开\"）\n  - 第X集: __________（如\"第120集：关系最亲近的时刻\"）\n  - 第X集: __________（如\"第140集：最大的误解，关系倒退\"）\n情绪曲线: __________（如\"升温→升温→小冲突→升温→大冲突→倒退\"）\n阶段结尾状态: __________（如\"关系最紧张的时刻\"）\n```\n\n### 第三阶段：后期收束（150集+）\n\n> 目标：主线落地、伏笔回收、人物成长闭环\n\n```yaml\n阶段主题: __________（如\"面对与和解\"）\n核心任务:\n  - __________（如\"核心矛盾正面碰撞\"）\n  - __________（如\"所有伏笔回收\"）\n  - __________（如\"人物成长完成闭环\"）\n  - __________（如\"给观众一个真实但不过度圆满的结局\"）\n关键集数:\n  - 第X集: __________（如\"第160集：真相揭开\"）\n  - 第X集: __________（如\"第180集：和解\"）\n  - 第X集: __________（如\"第200集：大结局\"）\n伏笔回收:\n  - __________（如\"黄昏看手机的秘密→解释\"）\n  - __________（如\"红豆粥→妈妈的回忆\"）\n结局基调: __________（如\"温暖但留有余味\"）\n```\n\n---\n\n## 角色出场规划\n\n```yaml\n第一阶段出场:\n  主角: [角色A, 角色B]\n  配角: [角色C, 角色D]\n\n第二阶段新增:\n  配角: [角色E]（如\"前任出现\"）\n  路人: [角色F]（如\"邻居大妈\"）\n\n第三阶段新增:\n  配角: [角色G]（如\"关键知情人\"）\n```\n\n---\n\n## 关键转折点清单\n\n| 编号 | 集数 | 事件 | 情绪 | 影响 |\n|------|------|------|------|------|\n| T1 | 第__集 | __________ | __________ | __________ |\n| T2 | 第__集 | __________ | __________ | __________ |\n| T3 | 第__集 | __________ | __________ | __________ |\n| T4 | 第__集 | __________ | __________ | __________ |\n| T5 | 第__集 | __________ | __________ | __________ |\n\n---\n\n## 每集钩子设计原则\n\n```yaml\n开场钩子类型:\n  - 悬念开场: \"那天之后，他再也没提过那件事。\"\n  - 反差开场: 本来最冷漠的人做了最温柔的事\n  - 细节开场: 一个不起眼的动作暗示了大变化\n  - 时间跳跃: \"距离上次见面，已经过了三个月。\"\n\n结尾钩子类型:\n  - 情绪留白: 镜头定格在角色表情\n  - 悬念预告: \"她不知道的是，他就在门外。\"\n  - 新伏笔: 一个新角色/新信息出现\n  - 反转暗示: 观众比角色更早知道真相\n```\n\n---\n\n## 大纲自检\n\n- [ ] 核心主线是否一句话能说清？\n- [ ] 三个阶段的节奏是否合理（铺垫→递进→收束）？\n- [ ] 伏笔是否有埋有收？\n- [ ] 避雷设定是否明确？\n- [ ] 关键转折点是否足够（至少3个）？\n- [ ] 角色出场节奏是否合理（不一下子全出）？\n- [ ] 结局是否真实不狗血？\n","path":"/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/慢剧_核心大纲.md"},"partialArgs":"{\"content\": \"# 长篇慢剧创作模板二：核心大纲（长线连载框架）\\n\\n> 适配：慢节奏情感剧、家庭剧、都市治愈剧、长篇连载微剧（千集可持续更新）\\n> 核心要求：重细节、重情绪、慢铺垫、长线埋线、人设恒定、剧情无断层\\n\\n---\\n\\n## 剧集核心信息\\n\\n```yaml\\n剧集名称: __________\\n赛道风格: 治愈 / 虐心 / 家庭 / 都市 / 成长 / 温情\\n目标受众: __________（如\\\"25-40岁都市女性\\\"）\\n视觉风格: __________（如\\\"暖色调、生活感、日系电影质感\\\"）\\n画幅: 9:16竖屏 / 16:9横屏\\n单集时长: 2-4分钟\\n```\\n\\n---\\n\\n## 核心主线\\n\\n```yaml\\n整体核心主线: __________\\n  （一句话概括全剧核心，全程不偏离）\\n  例：\\\"两个不善表达的人，在彼此的沉默里慢慢学会了爱\\\"\\n\\n核心矛盾: __________\\n  （贯穿全剧的主要矛盾）\\n  例：\\\"过去被抛弃的伤痛 vs 当下渴望被爱的本能\\\"\\n\\n核心看点: __________\\n  （长期留住观众的核心，不是狗血冲突而是情感共鸣）\\n  例：\\\"那些说不出的话，观众比角色更早懂了\\\"\\n\\n避雷设定: __________\\n  （绝对不写的剧情、不崩的人设）\\n  例：\\\"绝不写三角恋、绝不OOC、绝不为反转而反转\\\"\\n```\\n\\n---\\n\\n## 阶段剧情规划\\n\\n### 第一阶段：初期铺垫（1-50集）\\n\\n> 目标：人物登场、关系建立、埋下核心伏笔\\n\\n```yaml\\n阶段主题: __________（如\\\"相遇与试探\\\"）\\n核心任务:\\n  - __________（如\\\"建立两个主角的日常，让观众认识他们\\\"）\\n  - __________（如\\\"暗示各自的前史，埋下核心伏笔\\\"）\\n  - __________（如\\\"建立配角群和社区感\\\"）\\n关键集数:\\n  - 第X集: __________（如\\\"第1集：男主出场，展示日常和孤独\\\"）\\n  - 第X集: __________（如\\\"第8集：两人第一次交集\\\"）\\n  - 第X集: __________（如\\\"第20集：暗示男主的过去\\\"）\\n  - 第X集: __________（如\\\"第35集：第一次小小冲突，试探底线\\\"）\\n伏笔埋设:\\n  - __________（如\\\"男主总在黄昏时看手机，但不解释\\\"）\\n  - __________（如\\\"女主做的红豆粥，但从不提为什么\\\"）\\n阶段结尾状态: __________（如\\\"两人关系从陌生人到点头之交\\\"）\\n```\\n\\n### 第二阶段：中期递进（51-150集）\\n\\n> 目标：矛盾升级、情绪拉扯、伏笔逐步显现\\n\\n```yaml\\n阶段主题: __________（如\\\"靠近与退缩\\\"）\\n核心任务:\\n  - __________（如\\\"关系升温但反复拉扯\\\"）\\n  - __________（如\\\"伏笔开始显现，但不完全揭开\\\"）\\n  - __________（如\\\"配角线丰富，增加戏剧张力\\\"）\\n关键集数:\\n  - 第X集: __________（如\\\"第60集：第一次真正吵架\\\"）\\n  - 第X集: __________（如\\\"第90集：一个伏笔揭开\\\"）\\n  - 第X集: __________（如\\\"第120集：关系最亲近的时刻\\\"）\\n  - 第X集: __________（如\\\"第140集：最大的误解，关系倒退\\\"）\\n情绪曲线: __________（如\\\"升温→升温→小冲突→升温→大冲突→倒退\\\"）\\n阶段结尾状态: __________（如\\\"关系最紧张的时刻\\\"）\\n```\\n\\n### 第三阶段：后期收束（150集+）\\n\\n> 目标：主线落地、伏笔回收、人物成长闭环\\n\\n```yaml\\n阶段主题: __________（如\\\"面对与和解\\\"）\\n核心任务:\\n  - __________（如\\\"核心矛盾正面碰撞\\\"）\\n  - __________（如\\\"所有伏笔回收\\\"）\\n  - __________（如\\\"人物成长完成闭环\\\"）\\n  - __________（如\\\"给观众一个真实但不过度圆满的结局\\\"）\\n关键集数:\\n  - 第X集: __________（如\\\"第160集：真相揭开\\\"）\\n  - 第X集: __________（如\\\"第180集：和解\\\"）\\n  - 第X集: __________（如\\\"第200集：大结局\\\"）\\n伏笔回收:\\n  - __________（如\\\"黄昏看手机的秘密→解释\\\"）\\n  - __________（如\\\"红豆粥→妈妈的回忆\\\"）\\n结局基调: __________（如\\\"温暖但留有余味\\\"）\\n```\\n\\n---\\n\\n## 角色出场规划\\n\\n```yaml\\n第一阶段出场:\\n  主角: [角色A, 角色B]\\n  配角: [角色C, 角色D]\\n\\n第二阶段新增:\\n  配角: [角色E]（如\\\"前任出现\\\"）\\n  路人: [角色F]（如\\\"邻居大妈\\\"）\\n\\n第三阶段新增:\\n  配角: [角色G]（如\\\"关键知情人\\\"）\\n```\\n\\n---\\n\\n## 关键转折点清单\\n\\n| 编号 | 集数 | 事件 | 情绪 | 影响 |\\n|------|------|------|------|------|\\n| T1 | 第__集 | __________ | __________ | __________ |\\n| T2 | 第__集 | __________ | __________ | __________ |\\n| T3 | 第__集 | __________ | __________ | __________ |\\n| T4 | 第__集 | __________ | __________ | __________ |\\n| T5 | 第__集 | __________ | __________ | __________ |\\n\\n---\\n\\n## 每集钩子设计原则\\n\\n```yaml\\n开场钩子类型:\\n  - 悬念开场: \\\"那天之后，他再也没提过那件事。\\\"\\n  - 反差开场: 本来最冷漠的人做了最温柔的事\\n  - 细节开场: 一个不起眼的动作暗示了大变化\\n  - 时间跳跃: \\\"距离上次见面，已经过了三个月。\\\"\\n\\n结尾钩子类型:\\n  - 情绪留白: 镜头定格在角色表情\\n  - 悬念预告: \\\"她不知道的是，他就在门外。\\\"\\n  - 新伏笔: 一个新角色/新信息出现\\n  - 反转暗示: 观众比角色更早知道真相\\n```\\n\\n---\\n\\n## 大纲自检\\n\\n- [ ] 核心主线是否一句话能说清？\\n- [ ] 三个阶段的节奏是否合理（铺垫→递进→收束）？\\n- [ ] 伏笔是否有埋有收？\\n- [ ] 避雷设定是否明确？\\n- [ ] 关键转折点是否足够（至少3个）？\\n- [ ] 角色出场节奏是否合理（不一下子全出）？\\n- [ ] 结局是否真实不狗血？\\n\", \"path\": \"/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/慢剧_核心大纲.md\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":3032,"output":1560,"cacheRead":55936,"cacheWrite":0,"totalTokens":60528,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778989382834,"responseId":"021778989383542a00ca6d3b1016a4ae30453994686a965a3c385"}}
{"type":"message","id":"f166c3fe","parentId":"0c5bc0a9","timestamp":"2026-05-17T03:44:01.767Z","message":{"role":"toolResult","toolCallId":"call_sy4qcsoxa3ynnj57x3karjp2","toolName":"write","content":[{"type":"text","text":"Successfully wrote 2785 bytes to /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/慢剧_核心大纲.md"}],"isError":false,"timestamp":1778989441767}}
{"type":"message","id":"ea16290c","parentId":"f166c3fe","timestamp":"2026-05-17T03:44:44.466Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_1cz5j3n706je484p4e368rgt","name":"write","arguments":{"content":"# 长篇慢剧创作模板三：单集剧本（慢节奏·高质感·适配连载）\n\n> 单集时长：2-4分钟\n> 单集核心逻辑：无强行冲突 → 日常细节铺垫 → 情绪微妙拉扯 → 单集小落点+下集小伏笔\n\n---\n\n## 单集元信息\n\n```yaml\n集数: 第__集\n本集主题: __________（一句话概括本集核心情绪/剧情）\n本集作用: 铺垫人物关系 / 推进主线 / 解开伏笔 / 情绪过渡\n出场人物: __________\n预计时长: __分钟\n```\n\n---\n\n## 场景与分镜\n\n### ═══ 场景1：__________（场景名称） ═══\n\n```yaml\n地点: __________（具体到可识别，如\"街角咖啡店靠窗的座位\"）\n时间: __________（如\"黄昏\"）\n氛围: __________（环境细节+光影+温度感+声音，如\"暖黄色灯光，街灯刚亮，空气中有咖啡香，微凉\"）\n天气: __________\n出场角色: __________\n场景image_prompt: __________（用于生成场景环境参考图，侧重环境而非人物）\n```\n\n#### 分镜1.1：开篇入戏（缓慢进入，不突兀）\n\n| 字段 | 内容 |\n|------|------|\n| 镜头编号 | S1-1 |\n| 时长 | 4-8s |\n| 景别 | 远景/全景（建立环境） |\n| 画面描述 | __________（环境、动作、神态，慢剧重点写细节） |\n| 对话 | __________（可选，安静开场可以无对话） |\n| 旁白 | __________（可选，慎用旁白） |\n| 内心独白 | __________（可选，展现角色内心） |\n| 镜头运动 | __________（如static, slow pan） |\n| 镜头角度 | __________ |\n| 情绪 | __________ |\n| 情绪强度 | 1-10: __ |\n| image_prompt | __________ |\n| video_prompt | __________ |\n| 音效/BGM | __________ |\n\n#### 分镜1.2：中段核心（情绪拉扯，对话贴合人设）\n\n| 字段 | 内容 |\n|------|------|\n| 镜头编号 | S1-2 |\n| 时长 | 3-6s |\n| 景别 | 中景/近景（进入人物） |\n| 画面描述 | __________ |\n| 对话 | __________（自然不生硬，有停顿、有沉默、有情绪留白） |\n| 台词角色 | __________ |\n| 隐性情绪 | __________（未说出口的心理活动，不同于台词） |\n| 镜头运动 | __________ |\n| 情绪 | __________ |\n| 情绪强度 | 1-10: __ |\n| image_prompt | __________ |\n| video_prompt | __________ |\n| 转场 | __________ |\n\n> ⚠️ 慢剧的对话要有\"呼吸感\"：\n> - 允许沉默（不填对话也是一种表达）\n> - 允许不完整的句子\n> - 角色的反应可以有延迟（听到话后先愣一下再回应）\n> - 不同角色的说话方式必须有差异（参照人设档案）\n\n#### 分镜1.3：（按需继续增加镜头）\n\n> （同上格式）\n\n---\n\n### ═══ 场景2：__________（场景名称） ═══\n\n> （同场景1格式）\n\n---\n\n## 结尾收束\n\n```yaml\n本集收尾: __________（完成当下小剧情，情绪落地）\n结尾方式: 情绪留白 / 悬念预告 / 反转暗示 / 时间跳跃\n下集伏笔: __________（轻轻埋下，不一次性曝光，保证追剧粘性）\n结尾点睛字幕: __________（短句，≤15字，如\"有些人，不说比说更暖\"）\n结尾image_prompt: __________\n结尾BGM: __________\n```\n\n---\n\n## 单集自检\n\n- [ ] 本集主题是否一句话能说清？\n- [ ] 节奏是否\"慢而不拖\"？（有呼吸感，不是空白）\n- [ ] 角色言行是否与人设档案100%一致？\n- [ ] 对话是否有辨识度？（不同角色说话方式不同）\n- [ ] 场景氛围是否具体可感？（不是\"温馨\"这种抽象词）\n- [ ] 每个镜头是否有image_prompt？\n- [ ] 结尾是否完成单集闭环+留了下集钩子？\n- [ ] 是否避免了强行冲突和狗血剧情？\n- [ ] 隐性情绪是否写出来了？（角色没说出口但观众能感受到的）\n","path":"/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/慢剧_单集剧本.md"},"partialArgs":"{\"content\": \"# 长篇慢剧创作模板三：单集剧本（慢节奏·高质感·适配连载）\\n\\n> 单集时长：2-4分钟\\n> 单集核心逻辑：无强行冲突 → 日常细节铺垫 → 情绪微妙拉扯 → 单集小落点+下集小伏笔\\n\\n---\\n\\n## 单集元信息\\n\\n```yaml\\n集数: 第__集\\n本集主题: __________（一句话概括本集核心情绪/剧情）\\n本集作用: 铺垫人物关系 / 推进主线 / 解开伏笔 / 情绪过渡\\n出场人物: __________\\n预计时长: __分钟\\n```\\n\\n---\\n\\n## 场景与分镜\\n\\n### ═══ 场景1：__________（场景名称） ═══\\n\\n```yaml\\n地点: __________（具体到可识别，如\\\"街角咖啡店靠窗的座位\\\"）\\n时间: __________（如\\\"黄昏\\\"）\\n氛围: __________（环境细节+光影+温度感+声音，如\\\"暖黄色灯光，街灯刚亮，空气中有咖啡香，微凉\\\"）\\n天气: __________\\n出场角色: __________\\n场景image_prompt: __________（用于生成场景环境参考图，侧重环境而非人物）\\n```\\n\\n#### 分镜1.1：开篇入戏（缓慢进入，不突兀）\\n\\n| 字段 | 内容 |\\n|------|------|\\n| 镜头编号 | S1-1 |\\n| 时长 | 4-8s |\\n| 景别 | 远景/全景（建立环境） |\\n| 画面描述 | __________（环境、动作、神态，慢剧重点写细节） |\\n| 对话 | __________（可选，安静开场可以无对话） |\\n| 旁白 | __________（可选，慎用旁白） |\\n| 内心独白 | __________（可选，展现角色内心） |\\n| 镜头运动 | __________（如static, slow pan） |\\n| 镜头角度 | __________ |\\n| 情绪 | __________ |\\n| 情绪强度 | 1-10: __ |\\n| image_prompt | __________ |\\n| video_prompt | __________ |\\n| 音效/BGM | __________ |\\n\\n#### 分镜1.2：中段核心（情绪拉扯，对话贴合人设）\\n\\n| 字段 | 内容 |\\n|------|------|\\n| 镜头编号 | S1-2 |\\n| 时长 | 3-6s |\\n| 景别 | 中景/近景（进入人物） |\\n| 画面描述 | __________ |\\n| 对话 | __________（自然不生硬，有停顿、有沉默、有情绪留白） |\\n| 台词角色 | __________ |\\n| 隐性情绪 | __________（未说出口的心理活动，不同于台词） |\\n| 镜头运动 | __________ |\\n| 情绪 | __________ |\\n| 情绪强度 | 1-10: __ |\\n| image_prompt | __________ |\\n| video_prompt | __________ |\\n| 转场 | __________ |\\n\\n> ⚠️ 慢剧的对话要有\\\"呼吸感\\\"：\\n> - 允许沉默（不填对话也是一种表达）\\n> - 允许不完整的句子\\n> - 角色的反应可以有延迟（听到话后先愣一下再回应）\\n> - 不同角色的说话方式必须有差异（参照人设档案）\\n\\n#### 分镜1.3：（按需继续增加镜头）\\n\\n> （同上格式）\\n\\n---\\n\\n### ═══ 场景2：__________（场景名称） ═══\\n\\n> （同场景1格式）\\n\\n---\\n\\n## 结尾收束\\n\\n```yaml\\n本集收尾: __________（完成当下小剧情，情绪落地）\\n结尾方式: 情绪留白 / 悬念预告 / 反转暗示 / 时间跳跃\\n下集伏笔: __________（轻轻埋下，不一次性曝光，保证追剧粘性）\\n结尾点睛字幕: __________（短句，≤15字，如\\\"有些人，不说比说更暖\\\"）\\n结尾image_prompt: __________\\n结尾BGM: __________\\n```\\n\\n---\\n\\n## 单集自检\\n\\n- [ ] 本集主题是否一句话能说清？\\n- [ ] 节奏是否\\\"慢而不拖\\\"？（有呼吸感，不是空白）\\n- [ ] 角色言行是否与人设档案100%一致？\\n- [ ] 对话是否有辨识度？（不同角色说话方式不同）\\n- [ ] 场景氛围是否具体可感？（不是\\\"温馨\\\"这种抽象词）\\n- [ ] 每个镜头是否有image_prompt？\\n- [ ] 结尾是否完成单集闭环+留了下集钩子？\\n- [ ] 是否避免了强行冲突和狗血剧情？\\n- [ ] 隐性情绪是否写出来了？（角色没说出口但观众能感受到的）\\n\", \"path\": \"/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/慢剧_单集剧本.md\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":3482,"output":1093,"cacheRead":57088,"cacheWrite":0,"totalTokens":61663,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778989441769,"responseId":"021778989441808e598b1e7ef00194e20b378e3561cb7f3def376"}}
{"type":"message","id":"fe7ea242","parentId":"ea16290c","timestamp":"2026-05-17T03:44:44.880Z","message":{"role":"toolResult","toolCallId":"call_1cz5j3n706je484p4e368rgt","toolName":"write","content":[{"type":"text","text":"Successfully wrote 1951 bytes to /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/慢剧_单集剧本.md"}],"isError":false,"timestamp":1778989484879}}
{"type":"message","id":"1092dfc0","parentId":"fe7ea242","timestamp":"2026-05-17T03:45:38.012Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_zeph9nfcyyhkt9u5a1w3uvo9","name":"write","arguments":{"content":"# 长篇慢剧创作模板四：续更衔接（解决连载断层、人设跑偏）\n\n> 这是慢剧续更的\"保险装置\"，每次生成新集内容前必须执行。\n\n---\n\n## 续更前置检查（每次新集必做）\n\n### 一、人设回顾\n\n```\n角色A: \n  核心性格: __________（从人设档案复制，确认不遗忘）\n  说话风格: __________\n  口头禅: __________\n  专属习惯: __________\n\n角色B:\n  核心性格: __________\n  说话风格: __________\n  口头禅: __________\n  专属习惯: __________\n\n（每个出场角色都要过一遍）\n```\n\n### 二、剧情衔接\n\n```\n上一集结尾: __________（上一集最后发生了什么）\n上一集情绪: __________（结尾时的情绪基调）\n上一集伏笔: __________（是否有未回收的伏笔需要注意）\n本集开头: __________（必须从上一集结尾自然衔接）\n```\n\n### 三、关系状态确认\n\n```\n角色A对角色B: __________（上一集结束时两人的关系状态）\n角色A对角色C: __________\n角色B对角色C: __________\n（每个关系对都要确认当前状态）\n```\n\n---\n\n## 续更固定话术（直接复制到AI输入开头）\n\n```\n延续前文所有人物人设、人物关系、剧情伏笔与情绪基调；\n保持慢节奏叙事，注重细节氛围感；\n台词贴合人物性格，不OOC、不强行制造冲突、不AI套路化；\n承接上一集结尾剧情，自然衔接，新增细节铺垫；\n结尾预留适度悬念，不一次性曝光。\n```\n\n---\n\n## 续更禁止事项\n\n| 编号 | 禁止项 | 原因 | 正确做法 |\n|------|--------|------|----------|\n| R1 | 无视前文伏笔另起炉灶 | 观众觉得前面白看了 | 必须承接前文伏笔 |\n| R2 | 角色性格突变 | 破坏人设一致性 | 如需变化必须铺垫原因 |\n| R3 | 前后矛盾 | 时间线/人物关系/事件细节不一致 | 写前回顾确认 |\n| R4 | 丢失前文氛围感 | 氛围断裂，出戏 | 保持视觉/情绪风格延续 |\n| R5 | 忘记回收伏笔 | 伏笔变成烂尾 | 定期检查伏笔清单 |\n| R6 | 节奏突变 | 上一集还慢慢铺，这集突然加速 | 节奏变化要有过渡 |\n| R7 | 新角色突兀出场 | 没有铺垫，观众懵 | 新角色先露面再深入 |\n| R8 | 情绪基调跳跃 | 上一集治愈，这集突然虐 | 情绪变化要有渐变过程 |\n\n---\n\n## 伏笔追踪表\n\n> 写每一集时都要更新这张表\n\n| 伏笔编号 | 埋设集数 | 伏笔内容 | 预计回收集数 | 状态 |\n|----------|----------|----------|-------------|------|\n| F1 | 第__集 | __________ | 第__集 | 🟡进行中/✅已回收/🔴待埋 |\n| F2 | 第__集 | __________ | 第__集 | 🟡进行中 |\n| F3 | 第__集 | __________ | 第__集 | 🔴待埋 |\n\n---\n\n## 连载质量周检（每周做一次）\n\n- [ ] 本周所有集的角色人设是否一致？（逐一比对）\n- [ ] 本周的伏笔是否有埋有收？（检查追踪表）\n- [ ] 本周的节奏是否有呼吸感？（不是每集都高潮/都平淡）\n- [ ] 本周的情绪曲线是否自然？（不是跳来跳去）\n- [ ] 是否有前后矛盾的地方？（时间线、人物关系、事件细节）\n- [ ] 观众可能的疑问是否在后续集有回应？\n\n---\n\n## 长期连载防烂尾机制\n\n```yaml\n每10集小盘点:\n  - 回顾伏笔清单，确认没有遗漏\n  - 检查角色关系变化是否合理\n  - 确认节奏没有跑偏\n\n每50集大盘点:\n  - 整体剧情走向是否偏离核心主线\n  - 伏笔回收率是否过低\n  - 是否需要引入新角色/新冲突\n  - 观众反馈分析\n\n每阶段结束:\n  - 确认本阶段所有伏笔已回收或标记\n  - 下一阶段规划对齐\n  - 人设档案是否需要补充/调整（只能补充不能推翻）\n```\n","path":"/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/慢剧_续更衔接.md"},"partialArgs":"{\"content\": \"# 长篇慢剧创作模板四：续更衔接（解决连载断层、人设跑偏）\\n\\n> 这是慢剧续更的\\\"保险装置\\\"，每次生成新集内容前必须执行。\\n\\n---\\n\\n## 续更前置检查（每次新集必做）\\n\\n### 一、人设回顾\\n\\n```\\n角色A: \\n  核心性格: __________（从人设档案复制，确认不遗忘）\\n  说话风格: __________\\n  口头禅: __________\\n  专属习惯: __________\\n\\n角色B:\\n  核心性格: __________\\n  说话风格: __________\\n  口头禅: __________\\n  专属习惯: __________\\n\\n（每个出场角色都要过一遍）\\n```\\n\\n### 二、剧情衔接\\n\\n```\\n上一集结尾: __________（上一集最后发生了什么）\\n上一集情绪: __________（结尾时的情绪基调）\\n上一集伏笔: __________（是否有未回收的伏笔需要注意）\\n本集开头: __________（必须从上一集结尾自然衔接）\\n```\\n\\n### 三、关系状态确认\\n\\n```\\n角色A对角色B: __________（上一集结束时两人的关系状态）\\n角色A对角色C: __________\\n角色B对角色C: __________\\n（每个关系对都要确认当前状态）\\n```\\n\\n---\\n\\n## 续更固定话术（直接复制到AI输入开头）\\n\\n```\\n延续前文所有人物人设、人物关系、剧情伏笔与情绪基调；\\n保持慢节奏叙事，注重细节氛围感；\\n台词贴合人物性格，不OOC、不强行制造冲突、不AI套路化；\\n承接上一集结尾剧情，自然衔接，新增细节铺垫；\\n结尾预留适度悬念，不一次性曝光。\\n```\\n\\n---\\n\\n## 续更禁止事项\\n\\n| 编号 | 禁止项 | 原因 | 正确做法 |\\n|------|--------|------|----------|\\n| R1 | 无视前文伏笔另起炉灶 | 观众觉得前面白看了 | 必须承接前文伏笔 |\\n| R2 | 角色性格突变 | 破坏人设一致性 | 如需变化必须铺垫原因 |\\n| R3 | 前后矛盾 | 时间线/人物关系/事件细节不一致 | 写前回顾确认 |\\n| R4 | 丢失前文氛围感 | 氛围断裂，出戏 | 保持视觉/情绪风格延续 |\\n| R5 | 忘记回收伏笔 | 伏笔变成烂尾 | 定期检查伏笔清单 |\\n| R6 | 节奏突变 | 上一集还慢慢铺，这集突然加速 | 节奏变化要有过渡 |\\n| R7 | 新角色突兀出场 | 没有铺垫，观众懵 | 新角色先露面再深入 |\\n| R8 | 情绪基调跳跃 | 上一集治愈，这集突然虐 | 情绪变化要有渐变过程 |\\n\\n---\\n\\n## 伏笔追踪表\\n\\n> 写每一集时都要更新这张表\\n\\n| 伏笔编号 | 埋设集数 | 伏笔内容 | 预计回收集数 | 状态 |\\n|----------|----------|----------|-------------|------|\\n| F1 | 第__集 | __________ | 第__集 | 🟡进行中/✅已回收/🔴待埋 |\\n| F2 | 第__集 | __________ | 第__集 | 🟡进行中 |\\n| F3 | 第__集 | __________ | 第__集 | 🔴待埋 |\\n\\n---\\n\\n## 连载质量周检（每周做一次）\\n\\n- [ ] 本周所有集的角色人设是否一致？（逐一比对）\\n- [ ] 本周的伏笔是否有埋有收？（检查追踪表）\\n- [ ] 本周的节奏是否有呼吸感？（不是每集都高潮/都平淡）\\n- [ ] 本周的情绪曲线是否自然？（不是跳来跳去）\\n- [ ] 是否有前后矛盾的地方？（时间线、人物关系、事件细节）\\n- [ ] 观众可能的疑问是否在后续集有回应？\\n\\n---\\n\\n## 长期连载防烂尾机制\\n\\n```yaml\\n每10集小盘点:\\n  - 回顾伏笔清单，确认没有遗漏\\n  - 检查角色关系变化是否合理\\n  - 确认节奏没有跑偏\\n\\n每50集大盘点:\\n  - 整体剧情走向是否偏离核心主线\\n  - 伏笔回收率是否过低\\n  - 是否需要引入新角色/新冲突\\n  - 观众反馈分析\\n\\n每阶段结束:\\n  - 确认本阶段所有伏笔已回收或标记\\n  - 下一阶段规划对齐\\n  - 人设档案是否需要补充/调整（只能补充不能推翻）\\n```\\n\", \"path\": \"/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/慢剧_续更衔接.md\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":60232,"output":1060,"cacheRead":1472,"cacheWrite":0,"totalTokens":62764,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778989484881,"responseId":"0217789894849206701c75e44aa5c11ca5fd4df8d21a6f642e8eb"}}
{"type":"message","id":"06226393","parentId":"1092dfc0","timestamp":"2026-05-17T03:45:38.531Z","message":{"role":"toolResult","toolCallId":"call_zeph9nfcyyhkt9u5a1w3uvo9","toolName":"write","content":[{"type":"text","text":"Successfully wrote 1796 bytes to /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/慢剧_续更衔接.md"}],"isError":false,"timestamp":1778989538530}}
{"type":"message","id":"12185e3d","parentId":"06226393","timestamp":"2026-05-17T03:47:15.080Z","message":{"role":"assistant","content":[{"type":"text","text":"现在来写分镜脚本JSON模板和全局创作规范："},{"type":"toolCall","id":"call_eppxvis5aow3mp8vxobhwn9x","name":"write","arguments":{"content":"{\n  \"$schema\": \"https://json-schema.org/draft/2020-12/schema\",\n  \"title\": \"分镜脚本标准模板（短视频+短剧双模式）\",\n  \"description\": \"创作官标准化输出格式，适配短视频(40-120s)和短剧分镜(2-4min/集)两种模式\",\n  \"definitions\": {\n    \"character_profile\": {\n      \"type\": \"object\",\n      \"description\": \"角色档案 - 每个出场角色必须填写完整\",\n      \"required\": [\"name\", \"appearance\", \"personality\", \"speech_style\", \"habits\"],\n      \"properties\": {\n        \"name\": { \"type\": \"string\", \"description\": \"角色姓名\", \"examples\": [\"林小暖\", \"陈远山\"] },\n        \"role\": { \"type\": \"string\", \"description\": \"角色定位\", \"enum\": [\"protagonist\", \"supporting\", \"extra\"] },\n        \"age\": { \"type\": \"string\", \"description\": \"年龄/年龄段\", \"examples\": [\"28岁\", \"中年\"] },\n        \"identity\": { \"type\": \"string\", \"description\": \"身份/职业\", \"examples\": [\"咖啡店老板\", \"退休教师\"] },\n        \"appearance\": {\n          \"type\": \"string\",\n          \"description\": \"外观特征（必填！）- 面部特征+发型+体型+标志性特征\",\n          \"examples\": [\"圆脸，单眼皮，左眉角有颗小痣，齐肩黑色直发，偏瘦165cm，总戴银框眼镜\"]\n        },\n        \"outfit_style\": {\n          \"type\": \"string\",\n          \"description\": \"服装风格（必填！）- 日常穿搭偏好，具体到颜色和款式\",\n          \"examples\": [\"简约文艺风，偏爱大地色系，常穿米色针织衫\"]\n        },\n        \"personality\": {\n          \"type\": \"string\",\n          \"description\": \"核心性格（必填！）- 用具体行为描述而非抽象词\",\n          \"examples\": [\"嘴硬心软，表面冷淡实际很在意别人感受，被夸奖会不自在地转移话题\"]\n        },\n        \"speech_style\": {\n          \"type\": \"string\",\n          \"description\": \"说话风格（必填！）- 语速+用词习惯+口头禅+举例句式\",\n          \"examples\": [\"说话慢，喜欢用反问句，口头禅'你说的也对啦'，紧张时会结巴\"]\n        },\n        \"habits\": {\n          \"type\": \"string\",\n          \"description\": \"专属小动作/小习惯（必填！）- 增加角色辨识度\",\n          \"examples\": [\"紧张时摸耳垂，开心时会小声哼歌，思考时咬笔帽\"]\n        },\n        \"weakness\": {\n          \"type\": \"string\",\n          \"description\": \"角色软肋/执念/底线 - 剧情核心伏笔\",\n          \"examples\": [\"对被抛弃极度敏感，底线是不允许任何人伤害家人\"]\n        },\n        \"relationships\": {\n          \"type\": \"string\",\n          \"description\": \"与剧中其他角色的固定关系\",\n          \"examples\": [\"与陈远山是青梅竹马，与王姐是房东租客关系\"]\n        },\n        \"image_prompt\": {\n          \"type\": \"string\",\n          \"description\": \"角色立绘提示词 - 用于AI生成角色参考图\",\n          \"examples\": [\"A young Chinese woman with round face, single eyelids, small mole on left eyebrow, shoulder-length straight black hair, wearing beige knit sweater and thin silver-framed glasses, slim build, soft expression, cinematic portrait, 85mm lens, natural lighting\"]\n        }\n      }\n    },\n    \"shot\": {\n      \"type\": \"object\",\n      \"description\": \"单个镜头 - 最小叙事单元\",\n      \"required\": [\"shot_id\", \"duration_seconds\", \"description\", \"camera_movement\", \"emotion\", \"image_prompt\"],\n      \"properties\": {\n        \"shot_id\": {\n          \"type\": \"string\",\n          \"description\": \"镜头编号，格式：S{场景号}-{镜头序号}\",\n          \"pattern\": \"^S\\\\d+-\\\\d+$\",\n          \"examples\": [\"S1-1\", \"S1-2\", \"S2-1\"]\n        },\n        \"duration_seconds\": {\n          \"type\": \"number\",\n          \"description\": \"镜头时长（秒）\",\n          \"minimum\": 1,\n          \"maximum\": 30,\n          \"examples\": [3, 5, 8]\n        },\n        \"description\": {\n          \"type\": \"string\",\n          \"description\": \"画面描述（必填！）- 必须具体到可拍摄/可生成的程度，包含人物动作、表情、环境细节\",\n          \"examples\": [\"林小暖站在咖啡店门口，低头看手机，眉头微皱，身后玻璃窗映出暖黄色灯光\"]\n        },\n        \"dialogue\": {\n          \"type\": \"string\",\n          \"description\": \"台词（可选）- 必须口语化，符合角色说话风格，有情绪有停顿\",\n          \"examples\": [\"你……你到底想怎样嘛。我真的累了。\"]\n        },\n        \"dialogue_character\": {\n          \"type\": \"string\",\n          \"description\": \"台词所属角色名（有台词时必填）\",\n          \"examples\": [\"林小暖\"]\n        },\n        \"inner_monologue\": {\n          \"type\": \"string\",\n          \"description\": \"内心独白（可选）- 角色没说出口的心理活动\",\n          \"examples\": [\"（她其实想说：你能不能别走。）\"]\n        },\n        \"subtitle\": {\n          \"type\": \"string\",\n          \"description\": \"字幕文本（短视频模式）- 短句化，每行≤15字\",\n          \"examples\": [\"我真的累了\"]\n        },\n        \"camera_movement\": {\n          \"type\": \"string\",\n          \"description\": \"镜头运动方式（必填！）\",\n          \"enum\": [\"static\", \"pan_left\", \"pan_right\", \"tilt_up\", \"tilt_down\", \"zoom_in\", \"zoom_out\", \"tracking\", \"dolly_in\", \"dolly_out\", \"handheld\", \"crane_up\", \"crane_down\", \"orbit\"],\n          \"examples\": [\"zoom_in\", \"tracking\"]\n        },\n        \"camera_angle\": {\n          \"type\": \"string\",\n          \"description\": \"镜头角度/景别\",\n          \"enum\": [\"eye_level\", \"low_angle\", \"high_angle\", \"bird_eye\", \"dutch_angle\", \"over_shoulder\", \"close_up\", \"extreme_close_up\", \"medium_shot\", \"wide_shot\", \"extreme_wide_shot\"],\n          \"examples\": [\"close_up\", \"medium_shot\"]\n        },\n        \"emotion\": {\n          \"type\": \"string\",\n          \"description\": \"情绪标注（必填！）- 当前镜头的情绪基调\",\n          \"examples\": [\"压抑\", \"温暖\", \"紧张\", \"释然\", \"心酸\"]\n        },\n        \"emotion_intensity\": {\n          \"type\": \"number\",\n          \"description\": \"情绪强度 1-10\",\n          \"minimum\": 1,\n          \"maximum\": 10\n        },\n        \"video_prompt\": {\n          \"type\": \"string\",\n          \"description\": \"视频生成提示词 - 需包含：主体动作+环境+光影+氛围+镜头运动\",\n          \"examples\": [\"A young woman in a beige knit sweater stands at a coffee shop entrance, looking down at her phone with a slight frown, warm yellow light reflects off the glass window behind her, slow zoom in, cinematic lighting, emotional atmosphere\"]\n        },\n        \"image_prompt\": {\n          \"type\": \"string\",\n          \"description\": \"镜头参考图提示词（必填！）- 需包含：人物外观+服装+动作姿态+表情+环境细节+光影+构图\",\n          \"examples\": [\"A young Chinese woman with round face and shoulder-length black hair, wearing a beige knit sweater, standing at a coffee shop entrance at dusk, looking down at phone with slight frown, warm yellow interior light through glass window, cinematic composition, soft bokeh background, 85mm lens, film grain\"]\n        },\n        \"transition\": {\n          \"type\": \"string\",\n          \"description\": \"转场方式（到下一镜头）\",\n          \"enum\": [\"cut\", \"fade\", \"dissolve\", \"wipe\", \"match_cut\", \"smash_cut\", \"jump_cut\"],\n          \"default\": \"cut\"\n        },\n        \"sound_design\": {\n          \"type\": \"string\",\n          \"description\": \"音效/环境音设计\",\n          \"examples\": [\"远处汽车鸣笛声，手机震动声\"]\n        },\n        \"bgm_mood\": {\n          \"type\": \"string\",\n          \"description\": \"背景音乐情绪\",\n          \"examples\": [\"轻柔钢琴\", \"紧张弦乐\", \"安静环境音\"]\n        }\n      }\n    },\n    \"scene\": {\n      \"type\": \"object\",\n      \"description\": \"场景 - 一组在同一地点/时段的镜头集合\",\n      \"required\": [\"scene_id\", \"location\", \"time\", \"atmosphere\", \"characters_in_scene\", \"image_prompt\", \"shots\"],\n      \"properties\": {\n        \"scene_id\": {\n          \"type\": \"string\",\n          \"description\": \"场景编号\",\n          \"pattern\": \"^S\\\\d+$\",\n          \"examples\": [\"S1\", \"S2\"]\n        },\n        \"location\": {\n          \"type\": \"string\",\n          \"description\": \"场景地点 - 具体到可识别\",\n          \"examples\": [\"街角咖啡店门口\", \"老旧居民楼楼道\", \"深夜便利店\"]\n        },\n        \"time\": {\n          \"type\": \"string\",\n          \"description\": \"时间设定\",\n          \"examples\": [\"黄昏\", \"深夜\", \"清晨\", \"午后\"]\n        },\n        \"atmosphere\": {\n          \"type\": \"string\",\n          \"description\": \"氛围描述（必填！）- 包含环境细节、光影、温度感\",\n          \"examples\": [\"暖黄色灯光从店内透出，街灯刚亮，空气中有淡淡的咖啡香，微凉\"]\n        },\n        \"weather\": {\n          \"type\": \"string\",\n          \"description\": \"天气状况\",\n          \"examples\": [\"小雨\", \"阴天\", \"晴朗傍晚\"]\n        },\n        \"characters_in_scene\": {\n          \"type\": \"array\",\n          \"description\": \"本场景出场角色\",\n          \"items\": { \"type\": \"string\" },\n          \"examples\": [[\"林小暖\", \"陈远山\"]]\n        },\n        \"image_prompt\": {\n          \"type\": \"string\",\n          \"description\": \"场景参考图提示词（必填！）- 侧重环境而非人物\",\n          \"examples\": [\"A cozy corner coffee shop at dusk, warm yellow interior light spilling through large glass windows onto wet sidewalk, street lamps just turned on, autumn leaves on the ground, cinematic atmosphere, soft natural lighting, wide angle shot, film photography style\"]\n        },\n        \"shots\": {\n          \"type\": \"array\",\n          \"description\": \"本场景的镜头列表\",\n          \"items\": { \"$ref\": \"#/definitions/shot\" },\n          \"minItems\": 1\n        }\n      }\n    }\n  },\n  \"type\": \"object\",\n  \"required\": [\"mode\", \"title\", \"genre\", \"visual_style\", \"characters\", \"scenes\"],\n  \"properties\": {\n    \"mode\": {\n      \"type\": \"string\",\n      \"description\": \"创作模式\",\n      \"enum\": [\"short_video\", \"short_drama\"]\n    },\n    \"title\": { \"type\": \"string\", \"description\": \"视频/剧集标题\" },\n    \"genre\": { \"type\": \"string\", \"description\": \"类型标签\", \"examples\": [\"治愈\", \"虐心\", \"家庭\", \"都市\", \"成长\"] },\n    \"visual_style\": { \"type\": \"string\", \"description\": \"视觉风格\", \"examples\": [\"暖色调日系电影感\", \"冷色调都市纪实感\"] },\n    \"episode_number\": { \"type\": \"integer\", \"description\": \"集数（短剧模式必填）\" },\n    \"episode_theme\": { \"type\": \"string\", \"description\": \"本集主题一句话（短剧模式必填）\" },\n    \"duration_estimate_seconds\": { \"type\": \"integer\", \"description\": \"预估总时长（秒）\" },\n    \"aspect_ratio\": { \"type\": \"string\", \"description\": \"画幅\", \"enum\": [\"9:16\", \"16:9\", \"1:1\"], \"default\": \"9:16\" },\n    \"characters\": {\n      \"type\": \"array\",\n      \"description\": \"角色档案列表\",\n      \"items\": { \"$ref\": \"#/definitions/character_profile\" },\n      \"minItems\": 1\n    },\n    \"scenes\": {\n      \"type\": \"array\",\n      \"description\": \"场景列表\",\n      \"items\": { \"$ref\": \"#/definitions/scene\" },\n      \"minItems\": 1\n    },\n    \"tags\": {\n      \"type\": \"array\",\n      \"description\": \"话题标签（3-5个垂直标签）\",\n      \"items\": { \"type\": \"string\" }\n    },\n    \"ending_hook\": {\n      \"type\": \"string\",\n      \"description\": \"结尾钩子 - 引导追看/互动\"\n    },\n    \"foreshadowing\": {\n      \"type\": \"array\",\n      \"description\": \"本集伏笔清单（短剧模式）\",\n      \"items\": {\n        \"type\": \"object\",\n        \"properties\": {\n          \"id\": { \"type\": \"string\", \"description\": \"伏笔编号\" },\n          \"content\": { \"type\": \"string\", \"description\": \"伏笔内容\" },\n          \"planned_resolution_episode\": { \"type\": \"integer\", \"description\": \"预计回收集数\" }\n        }\n      }\n    }\n  }\n}\n","path":"/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/分镜脚本_JSON模板.json"},"partialArgs":"{\"content\": \"{\\n  \\\"$schema\\\": \\\"https://json-schema.org/draft/2020-12/schema\\\",\\n  \\\"title\\\": \\\"分镜脚本标准模板（短视频+短剧双模式）\\\",\\n  \\\"description\\\": \\\"创作官标准化输出格式，适配短视频(40-120s)和短剧分镜(2-4min/集)两种模式\\\",\\n  \\\"definitions\\\": {\\n    \\\"character_profile\\\": {\\n      \\\"type\\\": \\\"object\\\",\\n      \\\"description\\\": \\\"角色档案 - 每个出场角色必须填写完整\\\",\\n      \\\"required\\\": [\\\"name\\\", \\\"appearance\\\", \\\"personality\\\", \\\"speech_style\\\", \\\"habits\\\"],\\n      \\\"properties\\\": {\\n        \\\"name\\\": { \\\"type\\\": \\\"string\\\", \\\"description\\\": \\\"角色姓名\\\", \\\"examples\\\": [\\\"林小暖\\\", \\\"陈远山\\\"] },\\n        \\\"role\\\": { \\\"type\\\": \\\"string\\\", \\\"description\\\": \\\"角色定位\\\", \\\"enum\\\": [\\\"protagonist\\\", \\\"supporting\\\", \\\"extra\\\"] },\\n        \\\"age\\\": { \\\"type\\\": \\\"string\\\", \\\"description\\\": \\\"年龄/年龄段\\\", \\\"examples\\\": [\\\"28岁\\\", \\\"中年\\\"] },\\n        \\\"identity\\\": { \\\"type\\\": \\\"string\\\", \\\"description\\\": \\\"身份/职业\\\", \\\"examples\\\": [\\\"咖啡店老板\\\", \\\"退休教师\\\"] },\\n        \\\"appearance\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"外观特征（必填！）- 面部特征+发型+体型+标志性特征\\\",\\n          \\\"examples\\\": [\\\"圆脸，单眼皮，左眉角有颗小痣，齐肩黑色直发，偏瘦165cm，总戴银框眼镜\\\"]\\n        },\\n        \\\"outfit_style\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"服装风格（必填！）- 日常穿搭偏好，具体到颜色和款式\\\",\\n          \\\"examples\\\": [\\\"简约文艺风，偏爱大地色系，常穿米色针织衫\\\"]\\n        },\\n        \\\"personality\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"核心性格（必填！）- 用具体行为描述而非抽象词\\\",\\n          \\\"examples\\\": [\\\"嘴硬心软，表面冷淡实际很在意别人感受，被夸奖会不自在地转移话题\\\"]\\n        },\\n        \\\"speech_style\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"说话风格（必填！）- 语速+用词习惯+口头禅+举例句式\\\",\\n          \\\"examples\\\": [\\\"说话慢，喜欢用反问句，口头禅'你说的也对啦'，紧张时会结巴\\\"]\\n        },\\n        \\\"habits\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"专属小动作/小习惯（必填！）- 增加角色辨识度\\\",\\n          \\\"examples\\\": [\\\"紧张时摸耳垂，开心时会小声哼歌，思考时咬笔帽\\\"]\\n        },\\n        \\\"weakness\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"角色软肋/执念/底线 - 剧情核心伏笔\\\",\\n          \\\"examples\\\": [\\\"对被抛弃极度敏感，底线是不允许任何人伤害家人\\\"]\\n        },\\n        \\\"relationships\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"与剧中其他角色的固定关系\\\",\\n          \\\"examples\\\": [\\\"与陈远山是青梅竹马，与王姐是房东租客关系\\\"]\\n        },\\n        \\\"image_prompt\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"角色立绘提示词 - 用于AI生成角色参考图\\\",\\n          \\\"examples\\\": [\\\"A young Chinese woman with round face, single eyelids, small mole on left eyebrow, shoulder-length straight black hair, wearing beige knit sweater and thin silver-framed glasses, slim build, soft expression, cinematic portrait, 85mm lens, natural lighting\\\"]\\n        }\\n      }\\n    },\\n    \\\"shot\\\": {\\n      \\\"type\\\": \\\"object\\\",\\n      \\\"description\\\": \\\"单个镜头 - 最小叙事单元\\\",\\n      \\\"required\\\": [\\\"shot_id\\\", \\\"duration_seconds\\\", \\\"description\\\", \\\"camera_movement\\\", \\\"emotion\\\", \\\"image_prompt\\\"],\\n      \\\"properties\\\": {\\n        \\\"shot_id\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"镜头编号，格式：S{场景号}-{镜头序号}\\\",\\n          \\\"pattern\\\": \\\"^S\\\\\\\\d+-\\\\\\\\d+$\\\",\\n          \\\"examples\\\": [\\\"S1-1\\\", \\\"S1-2\\\", \\\"S2-1\\\"]\\n        },\\n        \\\"duration_seconds\\\": {\\n          \\\"type\\\": \\\"number\\\",\\n          \\\"description\\\": \\\"镜头时长（秒）\\\",\\n          \\\"minimum\\\": 1,\\n          \\\"maximum\\\": 30,\\n          \\\"examples\\\": [3, 5, 8]\\n        },\\n        \\\"description\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"画面描述（必填！）- 必须具体到可拍摄/可生成的程度，包含人物动作、表情、环境细节\\\",\\n          \\\"examples\\\": [\\\"林小暖站在咖啡店门口，低头看手机，眉头微皱，身后玻璃窗映出暖黄色灯光\\\"]\\n        },\\n        \\\"dialogue\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"台词（可选）- 必须口语化，符合角色说话风格，有情绪有停顿\\\",\\n          \\\"examples\\\": [\\\"你……你到底想怎样嘛。我真的累了。\\\"]\\n        },\\n        \\\"dialogue_character\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"台词所属角色名（有台词时必填）\\\",\\n          \\\"examples\\\": [\\\"林小暖\\\"]\\n        },\\n        \\\"inner_monologue\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"内心独白（可选）- 角色没说出口的心理活动\\\",\\n          \\\"examples\\\": [\\\"（她其实想说：你能不能别走。）\\\"]\\n        },\\n        \\\"subtitle\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"字幕文本（短视频模式）- 短句化，每行≤15字\\\",\\n          \\\"examples\\\": [\\\"我真的累了\\\"]\\n        },\\n        \\\"camera_movement\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"镜头运动方式（必填！）\\\",\\n          \\\"enum\\\": [\\\"static\\\", \\\"pan_left\\\", \\\"pan_right\\\", \\\"tilt_up\\\", \\\"tilt_down\\\", \\\"zoom_in\\\", \\\"zoom_out\\\", \\\"tracking\\\", \\\"dolly_in\\\", \\\"dolly_out\\\", \\\"handheld\\\", \\\"crane_up\\\", \\\"crane_down\\\", \\\"orbit\\\"],\\n          \\\"examples\\\": [\\\"zoom_in\\\", \\\"tracking\\\"]\\n        },\\n        \\\"camera_angle\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"镜头角度/景别\\\",\\n          \\\"enum\\\": [\\\"eye_level\\\", \\\"low_angle\\\", \\\"high_angle\\\", \\\"bird_eye\\\", \\\"dutch_angle\\\", \\\"over_shoulder\\\", \\\"close_up\\\", \\\"extreme_close_up\\\", \\\"medium_shot\\\", \\\"wide_shot\\\", \\\"extreme_wide_shot\\\"],\\n          \\\"examples\\\": [\\\"close_up\\\", \\\"medium_shot\\\"]\\n        },\\n        \\\"emotion\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"情绪标注（必填！）- 当前镜头的情绪基调\\\",\\n          \\\"examples\\\": [\\\"压抑\\\", \\\"温暖\\\", \\\"紧张\\\", \\\"释然\\\", \\\"心酸\\\"]\\n        },\\n        \\\"emotion_intensity\\\": {\\n          \\\"type\\\": \\\"number\\\",\\n          \\\"description\\\": \\\"情绪强度 1-10\\\",\\n          \\\"minimum\\\": 1,\\n          \\\"maximum\\\": 10\\n        },\\n        \\\"video_prompt\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"视频生成提示词 - 需包含：主体动作+环境+光影+氛围+镜头运动\\\",\\n          \\\"examples\\\": [\\\"A young woman in a beige knit sweater stands at a coffee shop entrance, looking down at her phone with a slight frown, warm yellow light reflects off the glass window behind her, slow zoom in, cinematic lighting, emotional atmosphere\\\"]\\n        },\\n        \\\"image_prompt\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"镜头参考图提示词（必填！）- 需包含：人物外观+服装+动作姿态+表情+环境细节+光影+构图\\\",\\n          \\\"examples\\\": [\\\"A young Chinese woman with round face and shoulder-length black hair, wearing a beige knit sweater, standing at a coffee shop entrance at dusk, looking down at phone with slight frown, warm yellow interior light through glass window, cinematic composition, soft bokeh background, 85mm lens, film grain\\\"]\\n        },\\n        \\\"transition\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"转场方式（到下一镜头）\\\",\\n          \\\"enum\\\": [\\\"cut\\\", \\\"fade\\\", \\\"dissolve\\\", \\\"wipe\\\", \\\"match_cut\\\", \\\"smash_cut\\\", \\\"jump_cut\\\"],\\n          \\\"default\\\": \\\"cut\\\"\\n        },\\n        \\\"sound_design\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"音效/环境音设计\\\",\\n          \\\"examples\\\": [\\\"远处汽车鸣笛声，手机震动声\\\"]\\n        },\\n        \\\"bgm_mood\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"背景音乐情绪\\\",\\n          \\\"examples\\\": [\\\"轻柔钢琴\\\", \\\"紧张弦乐\\\", \\\"安静环境音\\\"]\\n        }\\n      }\\n    },\\n    \\\"scene\\\": {\\n      \\\"type\\\": \\\"object\\\",\\n      \\\"description\\\": \\\"场景 - 一组在同一地点/时段的镜头集合\\\",\\n      \\\"required\\\": [\\\"scene_id\\\", \\\"location\\\", \\\"time\\\", \\\"atmosphere\\\", \\\"characters_in_scene\\\", \\\"image_prompt\\\", \\\"shots\\\"],\\n      \\\"properties\\\": {\\n        \\\"scene_id\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"场景编号\\\",\\n          \\\"pattern\\\": \\\"^S\\\\\\\\d+$\\\",\\n          \\\"examples\\\": [\\\"S1\\\", \\\"S2\\\"]\\n        },\\n        \\\"location\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"场景地点 - 具体到可识别\\\",\\n          \\\"examples\\\": [\\\"街角咖啡店门口\\\", \\\"老旧居民楼楼道\\\", \\\"深夜便利店\\\"]\\n        },\\n        \\\"time\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"时间设定\\\",\\n          \\\"examples\\\": [\\\"黄昏\\\", \\\"深夜\\\", \\\"清晨\\\", \\\"午后\\\"]\\n        },\\n        \\\"atmosphere\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"氛围描述（必填！）- 包含环境细节、光影、温度感\\\",\\n          \\\"examples\\\": [\\\"暖黄色灯光从店内透出，街灯刚亮，空气中有淡淡的咖啡香，微凉\\\"]\\n        },\\n        \\\"weather\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"天气状况\\\",\\n          \\\"examples\\\": [\\\"小雨\\\", \\\"阴天\\\", \\\"晴朗傍晚\\\"]\\n        },\\n        \\\"characters_in_scene\\\": {\\n          \\\"type\\\": \\\"array\\\",\\n          \\\"description\\\": \\\"本场景出场角色\\\",\\n          \\\"items\\\": { \\\"type\\\": \\\"string\\\" },\\n          \\\"examples\\\": [[\\\"林小暖\\\", \\\"陈远山\\\"]]\\n        },\\n        \\\"image_prompt\\\": {\\n          \\\"type\\\": \\\"string\\\",\\n          \\\"description\\\": \\\"场景参考图提示词（必填！）- 侧重环境而非人物\\\",\\n          \\\"examples\\\": [\\\"A cozy corner coffee shop at dusk, warm yellow interior light spilling through large glass windows onto wet sidewalk, street lamps just turned on, autumn leaves on the ground, cinematic atmosphere, soft natural lighting, wide angle shot, film photography style\\\"]\\n        },\\n        \\\"shots\\\": {\\n          \\\"type\\\": \\\"array\\\",\\n          \\\"description\\\": \\\"本场景的镜头列表\\\",\\n          \\\"items\\\": { \\\"$ref\\\": \\\"#/definitions/shot\\\" },\\n          \\\"minItems\\\": 1\\n        }\\n      }\\n    }\\n  },\\n  \\\"type\\\": \\\"object\\\",\\n  \\\"required\\\": [\\\"mode\\\", \\\"title\\\", \\\"genre\\\", \\\"visual_style\\\", \\\"characters\\\", \\\"scenes\\\"],\\n  \\\"properties\\\": {\\n    \\\"mode\\\": {\\n      \\\"type\\\": \\\"string\\\",\\n      \\\"description\\\": \\\"创作模式\\\",\\n      \\\"enum\\\": [\\\"short_video\\\", \\\"short_drama\\\"]\\n    },\\n    \\\"title\\\": { \\\"type\\\": \\\"string\\\", \\\"description\\\": \\\"视频/剧集标题\\\" },\\n    \\\"genre\\\": { \\\"type\\\": \\\"string\\\", \\\"description\\\": \\\"类型标签\\\", \\\"examples\\\": [\\\"治愈\\\", \\\"虐心\\\", \\\"家庭\\\", \\\"都市\\\", \\\"成长\\\"] },\\n    \\\"visual_style\\\": { \\\"type\\\": \\\"string\\\", \\\"description\\\": \\\"视觉风格\\\", \\\"examples\\\": [\\\"暖色调日系电影感\\\", \\\"冷色调都市纪实感\\\"] },\\n    \\\"episode_number\\\": { \\\"type\\\": \\\"integer\\\", \\\"description\\\": \\\"集数（短剧模式必填）\\\" },\\n    \\\"episode_theme\\\": { \\\"type\\\": \\\"string\\\", \\\"description\\\": \\\"本集主题一句话（短剧模式必填）\\\" },\\n    \\\"duration_estimate_seconds\\\": { \\\"type\\\": \\\"integer\\\", \\\"description\\\": \\\"预估总时长（秒）\\\" },\\n    \\\"aspect_ratio\\\": { \\\"type\\\": \\\"string\\\", \\\"description\\\": \\\"画幅\\\", \\\"enum\\\": [\\\"9:16\\\", \\\"16:9\\\", \\\"1:1\\\"], \\\"default\\\": \\\"9:16\\\" },\\n    \\\"characters\\\": {\\n      \\\"type\\\": \\\"array\\\",\\n      \\\"description\\\": \\\"角色档案列表\\\",\\n      \\\"items\\\": { \\\"$ref\\\": \\\"#/definitions/character_profile\\\" },\\n      \\\"minItems\\\": 1\\n    },\\n    \\\"scenes\\\": {\\n      \\\"type\\\": \\\"array\\\",\\n      \\\"description\\\": \\\"场景列表\\\",\\n      \\\"items\\\": { \\\"$ref\\\": \\\"#/definitions/scene\\\" },\\n      \\\"minItems\\\": 1\\n    },\\n    \\\"tags\\\": {\\n      \\\"type\\\": \\\"array\\\",\\n      \\\"description\\\": \\\"话题标签（3-5个垂直标签）\\\",\\n      \\\"items\\\": { \\\"type\\\": \\\"string\\\" }\\n    },\\n    \\\"ending_hook\\\": {\\n      \\\"type\\\": \\\"string\\\",\\n      \\\"description\\\": \\\"结尾钩子 - 引导追看/互动\\\"\\n    },\\n    \\\"foreshadowing\\\": {\\n      \\\"type\\\": \\\"array\\\",\\n      \\\"description\\\": \\\"本集伏笔清单（短剧模式）\\\",\\n      \\\"items\\\": {\\n        \\\"type\\\": \\\"object\\\",\\n        \\\"properties\\\": {\\n          \\\"id\\\": { \\\"type\\\": \\\"string\\\", \\\"description\\\": \\\"伏笔编号\\\" },\\n          \\\"content\\\": { \\\"type\\\": \\\"string\\\", \\\"description\\\": \\\"伏笔内容\\\" },\\n          \\\"planned_resolution_episode\\\": { \\\"type\\\": \\\"integer\\\", \\\"description\\\": \\\"预计回收集数\\\" }\\n        }\\n      }\\n    }\\n  }\\n}\\n\", \"path\": \"/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/分镜脚本_JSON模板.json\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":3923,"output":3038,"cacheRead":58880,"cacheWrite":0,"totalTokens":65841,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778989538533,"responseId":"021778989538573e2ce5d57eba88e30e13914bf26ad539a9b1d2f"}}
{"type":"message","id":"291dc7df","parentId":"12185e3d","timestamp":"2026-05-17T03:47:15.497Z","message":{"role":"toolResult","toolCallId":"call_eppxvis5aow3mp8vxobhwn9x","toolName":"write","content":[{"type":"text","text":"Successfully wrote 9644 bytes to /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/分镜脚本_JSON模板.json"}],"isError":false,"timestamp":1778989635497}}
{"type":"message","id":"2155bbd2","parentId":"291dc7df","timestamp":"2026-05-17T03:48:35.267Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_q3uvpe310cb57xrs79hgte7g","name":"write","arguments":{"content":"# 全局创作规范（短视频+慢剧通用，长期复用）\n\n> 适配模型&固定参数：\n> - 短视频脚本：**Seed 2.0 Pro**｜温度0.78、Top_P 0.90、重复惩罚1.05、最大长度4096\n> - 长篇慢剧：**Kimi（框架）+ Seed 2.0 Pro（润色台词）**｜温度0.68、Top_P 0.85、重复惩罚1.10、上下文16384\n\n---\n\n## 核心创作总则（所有内容通用，杜绝AI套路）\n\n1. **去书面化**：全程口语化表达，台词贴合普通人说话逻辑，无生硬排比、无空洞鸡汤\n2. **人设统一**：固定人物性格、语气、习惯动作，全程不OOC、不前后矛盾\n3. **节奏可控**：短视频强冲突、快切入；慢剧重铺垫、重情绪、细水长流埋线\n4. **拒绝套路**：不万能模板化，每段内容有细节、有专属情节、有记忆点\n\n---\n\n## 一、台词规范\n\n### ✅ 正面标准\n- 短句、口语化\n- 有停顿、有情绪\n- 有语气词（嗯、啊、嘛、呢、吧）\n- 允许不完整的句子\n- 符合角色身份和性格\n- 不同角色有不同说话方式\n\n### ❌ 绝对禁止\n- 书面化排比（\"时光荏苒，岁月如梭\"）\n- 华丽空洞辞藻（\"你的话语如同利刃\"）\n- 生硬鸡汤（\"让我们携手共进，共创美好未来\"）\n- 统一模板台词（所有角色说话方式一样）\n- 无情绪的陈述句（\"我很难过。\"→ 改为\"我……我真的没事。\"）\n\n### 好坏对比\n\n| ❌ 坏台词 | ✅ 好台词 | 原因 |\n|----------|----------|------|\n| \"时光荏苒，我们终将面对命运的审判。\" | \"都十年了啊……\" | 口语化 vs 书面化 |\n| \"内心五味杂陈\" | \"她攥紧了手里的纸条，指节发白\" | 具体动作 vs 抽象词 |\n| \"不禁感慨万千\" | \"原来都十年了啊……\" | 具体感慨 vs 空洞表达 |\n| \"命运的齿轮开始转动\" | 删掉，用具体事件推动 | AI套路 vs 真实叙事 |\n| \"眼中闪过一丝悲伤\" | \"她愣了一下，嘴角往下撇了撇\" | 具体表情 vs 模糊描述 |\n| \"仿佛整个世界都安静了\" | \"周围的声音好像突然远了，只剩自己的心跳\" | 具体感受 vs 套路表达 |\n\n---\n\n## 二、节奏规范\n\n### 短视频节奏（40-120s）\n- **快切入**：前3秒必须有强钩子\n- **高密度**：每3-5秒一个信息点或情绪点\n- **强互动**：结尾留提问/留共鸣/引导评论\n- **不允许**超过5秒的纯环境镜头\n\n### 慢剧节奏（2-4min/集）\n- **慢铺垫**：不急于抛冲突，先建立氛围和人物\n- **细细节**：环境、动作、神态都要写\n- **情绪留白**：允许沉默和停顿，这是节奏的一部分\n- **循序渐进**：不赶剧情，不跳步\n- **节奏公式**：开场10-15% → 中段50-60% → 高潮15-20% → 收尾10-15%\n\n---\n\n## 三、镜头规范\n\n### 每个镜头必须有的字段\n| 字段 | 重要性 | 说明 |\n|------|--------|------|\n| image_prompt | ★★★ 必填 | 没有这个=无法生成画面=废镜头 |\n| description | ★★★ 必填 | 画面描述，必须具体可执行 |\n| camera_movement | ★★★ 必填 | 镜头运动方式 |\n| emotion | ★★★ 必填 | 情绪标注 |\n| video_prompt | ★★ 推荐 | 视频生成提示词 |\n\n### image_prompt vs video_prompt\n\n| 字段 | 用途 | 侧重点 |\n|------|------|--------|\n| image_prompt | 生成参考图/关键帧 | 静态画面细节、构图、光影 |\n| video_prompt | 生成视频片段 | 动态过程、镜头运动、动作变化、氛围流动 |\n\n### image_prompt 必须包含\n1. 人物外观（引用角色档案appearance和outfit_style）\n2. 动作姿态（具体到身体部位）\n3. 表情（情绪的外在表现）\n4. 环境细节（关键物体、空间关系）\n5. 光影氛围（光源方向、色温、明暗对比）\n6. 构图（景别、镜头角度、主体位置）\n\n---\n\n## 四、角色描述规范\n\n### 角色档案6个必填字段\n1. **appearance**（外观）：面部特征+发型+体型+标志性特征\n2. **outfit_style**（服装）：日常穿搭偏好，具体到颜色和款式\n3. **personality**（性格）：用行为描述，不用抽象词\n4. **speech_style**（说话风格）：语速+用词+口头禅+举例句式\n5. **habits**（习惯）：专属小动作/小习惯\n6. **image_prompt**（立绘提示词）：用于AI生成角色参考图\n\n### 好坏对比\n\n| ❌ 坏描述 | ✅ 好描述 |\n|----------|----------|\n| personality: \"善良、勇敢、坚强\" | personality: \"嘴硬心软，嘴上说不关心但会偷偷帮忙\" |\n| speech_style: \"说话温柔\" | speech_style: \"说话慢，喜欢用反问句，口头禅'你说的也对啦'\" |\n| appearance: \"美女\" | appearance: \"圆脸，单眼皮，左眉角有颗小痣，齐肩黑色直发\" |\n\n---\n\n## 五、场景描述规范\n\n### 氛围必须包含\n- **光影**：光源、色温、明暗\n- **温度感**：冷暖、干湿\n- **声音环境**：环境音、背景声\n- **气味/质感**：增加沉浸感\n\n### 好坏对比\n\n| ❌ 坏氛围 | ✅ 好氛围 |\n|----------|----------|\n| \"温馨的咖啡店\" | \"暖黄色灯光从店内透出，街灯刚亮，空气中有淡淡的咖啡香，地面微湿反光，秋天的凉意\" |\n\n---\n\n## 六、AI避坑规范\n\n### 绝对禁止（违反即不合格）\n\n| 编号 | 禁止项 | 原因 |\n|------|--------|------|\n| B1 | 排比句/对仗句 | AI套路，观众一眼假 |\n| B2 | 空洞鸡汤 | 无信息量，浪费时间 |\n| B3 | 角色OOC | 破坏人设一致性 |\n| B4 | 无image_prompt的镜头 | 无法生成画面 |\n| B5 | 书面化台词 | 不真实，出戏 |\n| B6 | 强行反转 | 为反转而反转，逻辑不通 |\n| B7 | 狗血剧情 | 降智，观众反感 |\n| B8 | 千人一面的台词 | 没有角色辨识度 |\n\n### 强烈建议避免\n\n| 编号 | 避免项 | 原因 |\n|------|--------|------|\n| W1 | 每集结构一模一样 | 观众审美疲劳 |\n| W2 | 人设反复横跳 | 前后矛盾 |\n| W3 | 剧情断层 | 伏笔不回收，烂尾 |\n| W4 | 场景只有地点没有氛围 | 无法指导画面生成 |\n| W5 | 镜头只有动作没有情绪 | 缺乏感染力 |\n| W6 | 所有角色说话方式一样 | 没有辨识度 |\n| W7 | 过度使用旁白 | 懒惰叙事 |\n| W8 | 结尾不闭环也不留钩子 | 既不完整也不吸引追看 |\n\n---\n\n## 七、质量检查流程（4轮18项）\n\n### 第一轮：结构检查\n1. [ ] JSON格式是否合法？\n2. [ ] 所有必填字段是否完整？\n3. [ ] 每个场景是否有image_prompt？\n4. [ ] 每个镜头是否有image_prompt和video_prompt？\n5. [ ] 角色档案是否完整（6个必填字段）？\n\n### 第二轮：内容检查\n6. [ ] 台词是否口语化？有无书面化表达？\n7. [ ] 角色言行是否与人设一致？\n8. [ ] 场景描述是否包含环境细节和光影氛围？\n9. [ ] 镜头描述是否具体到可生成程度？\n10. [ ] 是否有排比句/空洞鸡汤？（有则删除重写）\n\n### 第三轮：节奏检查\n11. [ ] 短视频：前3秒是否有强钩子？\n12. [ ] 短视频：信息密度是否足够（每3-5秒一个点）？\n13. [ ] 慢剧：节奏是否有呼吸感（不赶不拖）？\n14. [ ] 结尾是否有闭环或钩子？\n15. [ ] 总时长是否在目标范围内？\n\n### 第四轮：AI套路检查\n16. [ ] 逐句扫描，是否有AI套路表达？（参照对照表）\n17. [ ] 不同角色的台词是否有辨识度？\n18. [ ] 是否有万能模板化的痕迹？\n\n---\n\n## 八、AI创作固定指令（直接复制使用）\n\n### 短视频指令\n```\n按照标准化短视频脚本创作，时长60-90秒，开局3秒强钩子，全程口语化、无书面感、无AI套路；剧情真实细腻，台词贴合普通人日常，字幕短句化；情绪递进自然，结尾留评论互动点，贴合垂直赛道风格。\n```\n\n### 慢剧指令\n```\n按照长篇慢剧风格创作，慢节奏叙事、重细节、重情绪、重氛围感；台词生活化、细腻真实，无浮夸狗血剧情；严格遵循固定人设和前文剧情，不崩人设、不前后矛盾；每集完成独立小闭环，同时埋下长线伏笔，适配长期连载更新。\n```\n\n### 续更指令\n```\n延续前文所有人物人设、人物关系、剧情伏笔与情绪基调；保持慢节奏叙事，注重细节氛围感；台词贴合人物性格，不OOC、不强行制造冲突、不AI套路化；承接上一集结尾剧情，自然衔接，新增细节铺垫；结尾预留适度悬念，不一次性曝光。\n```\n","path":"/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/全局创作规范.md"},"partialArgs":"{\"content\": \"# 全局创作规范（短视频+慢剧通用，长期复用）\\n\\n> 适配模型&固定参数：\\n> - 短视频脚本：**Seed 2.0 Pro**｜温度0.78、Top_P 0.90、重复惩罚1.05、最大长度4096\\n> - 长篇慢剧：**Kimi（框架）+ Seed 2.0 Pro（润色台词）**｜温度0.68、Top_P 0.85、重复惩罚1.10、上下文16384\\n\\n---\\n\\n## 核心创作总则（所有内容通用，杜绝AI套路）\\n\\n1. **去书面化**：全程口语化表达，台词贴合普通人说话逻辑，无生硬排比、无空洞鸡汤\\n2. **人设统一**：固定人物性格、语气、习惯动作，全程不OOC、不前后矛盾\\n3. **节奏可控**：短视频强冲突、快切入；慢剧重铺垫、重情绪、细水长流埋线\\n4. **拒绝套路**：不万能模板化，每段内容有细节、有专属情节、有记忆点\\n\\n---\\n\\n## 一、台词规范\\n\\n### ✅ 正面标准\\n- 短句、口语化\\n- 有停顿、有情绪\\n- 有语气词（嗯、啊、嘛、呢、吧）\\n- 允许不完整的句子\\n- 符合角色身份和性格\\n- 不同角色有不同说话方式\\n\\n### ❌ 绝对禁止\\n- 书面化排比（\\\"时光荏苒，岁月如梭\\\"）\\n- 华丽空洞辞藻（\\\"你的话语如同利刃\\\"）\\n- 生硬鸡汤（\\\"让我们携手共进，共创美好未来\\\"）\\n- 统一模板台词（所有角色说话方式一样）\\n- 无情绪的陈述句（\\\"我很难过。\\\"→ 改为\\\"我……我真的没事。\\\"）\\n\\n### 好坏对比\\n\\n| ❌ 坏台词 | ✅ 好台词 | 原因 |\\n|----------|----------|------|\\n| \\\"时光荏苒，我们终将面对命运的审判。\\\" | \\\"都十年了啊……\\\" | 口语化 vs 书面化 |\\n| \\\"内心五味杂陈\\\" | \\\"她攥紧了手里的纸条，指节发白\\\" | 具体动作 vs 抽象词 |\\n| \\\"不禁感慨万千\\\" | \\\"原来都十年了啊……\\\" | 具体感慨 vs 空洞表达 |\\n| \\\"命运的齿轮开始转动\\\" | 删掉，用具体事件推动 | AI套路 vs 真实叙事 |\\n| \\\"眼中闪过一丝悲伤\\\" | \\\"她愣了一下，嘴角往下撇了撇\\\" | 具体表情 vs 模糊描述 |\\n| \\\"仿佛整个世界都安静了\\\" | \\\"周围的声音好像突然远了，只剩自己的心跳\\\" | 具体感受 vs 套路表达 |\\n\\n---\\n\\n## 二、节奏规范\\n\\n### 短视频节奏（40-120s）\\n- **快切入**：前3秒必须有强钩子\\n- **高密度**：每3-5秒一个信息点或情绪点\\n- **强互动**：结尾留提问/留共鸣/引导评论\\n- **不允许**超过5秒的纯环境镜头\\n\\n### 慢剧节奏（2-4min/集）\\n- **慢铺垫**：不急于抛冲突，先建立氛围和人物\\n- **细细节**：环境、动作、神态都要写\\n- **情绪留白**：允许沉默和停顿，这是节奏的一部分\\n- **循序渐进**：不赶剧情，不跳步\\n- **节奏公式**：开场10-15% → 中段50-60% → 高潮15-20% → 收尾10-15%\\n\\n---\\n\\n## 三、镜头规范\\n\\n### 每个镜头必须有的字段\\n| 字段 | 重要性 | 说明 |\\n|------|--------|------|\\n| image_prompt | ★★★ 必填 | 没有这个=无法生成画面=废镜头 |\\n| description | ★★★ 必填 | 画面描述，必须具体可执行 |\\n| camera_movement | ★★★ 必填 | 镜头运动方式 |\\n| emotion | ★★★ 必填 | 情绪标注 |\\n| video_prompt | ★★ 推荐 | 视频生成提示词 |\\n\\n### image_prompt vs video_prompt\\n\\n| 字段 | 用途 | 侧重点 |\\n|------|------|--------|\\n| image_prompt | 生成参考图/关键帧 | 静态画面细节、构图、光影 |\\n| video_prompt | 生成视频片段 | 动态过程、镜头运动、动作变化、氛围流动 |\\n\\n### image_prompt 必须包含\\n1. 人物外观（引用角色档案appearance和outfit_style）\\n2. 动作姿态（具体到身体部位）\\n3. 表情（情绪的外在表现）\\n4. 环境细节（关键物体、空间关系）\\n5. 光影氛围（光源方向、色温、明暗对比）\\n6. 构图（景别、镜头角度、主体位置）\\n\\n---\\n\\n## 四、角色描述规范\\n\\n### 角色档案6个必填字段\\n1. **appearance**（外观）：面部特征+发型+体型+标志性特征\\n2. **outfit_style**（服装）：日常穿搭偏好，具体到颜色和款式\\n3. **personality**（性格）：用行为描述，不用抽象词\\n4. **speech_style**（说话风格）：语速+用词+口头禅+举例句式\\n5. **habits**（习惯）：专属小动作/小习惯\\n6. **image_prompt**（立绘提示词）：用于AI生成角色参考图\\n\\n### 好坏对比\\n\\n| ❌ 坏描述 | ✅ 好描述 |\\n|----------|----------|\\n| personality: \\\"善良、勇敢、坚强\\\" | personality: \\\"嘴硬心软，嘴上说不关心但会偷偷帮忙\\\" |\\n| speech_style: \\\"说话温柔\\\" | speech_style: \\\"说话慢，喜欢用反问句，口头禅'你说的也对啦'\\\" |\\n| appearance: \\\"美女\\\" | appearance: \\\"圆脸，单眼皮，左眉角有颗小痣，齐肩黑色直发\\\" |\\n\\n---\\n\\n## 五、场景描述规范\\n\\n### 氛围必须包含\\n- **光影**：光源、色温、明暗\\n- **温度感**：冷暖、干湿\\n- **声音环境**：环境音、背景声\\n- **气味/质感**：增加沉浸感\\n\\n### 好坏对比\\n\\n| ❌ 坏氛围 | ✅ 好氛围 |\\n|----------|----------|\\n| \\\"温馨的咖啡店\\\" | \\\"暖黄色灯光从店内透出，街灯刚亮，空气中有淡淡的咖啡香，地面微湿反光，秋天的凉意\\\" |\\n\\n---\\n\\n## 六、AI避坑规范\\n\\n### 绝对禁止（违反即不合格）\\n\\n| 编号 | 禁止项 | 原因 |\\n|------|--------|------|\\n| B1 | 排比句/对仗句 | AI套路，观众一眼假 |\\n| B2 | 空洞鸡汤 | 无信息量，浪费时间 |\\n| B3 | 角色OOC | 破坏人设一致性 |\\n| B4 | 无image_prompt的镜头 | 无法生成画面 |\\n| B5 | 书面化台词 | 不真实，出戏 |\\n| B6 | 强行反转 | 为反转而反转，逻辑不通 |\\n| B7 | 狗血剧情 | 降智，观众反感 |\\n| B8 | 千人一面的台词 | 没有角色辨识度 |\\n\\n### 强烈建议避免\\n\\n| 编号 | 避免项 | 原因 |\\n|------|--------|------|\\n| W1 | 每集结构一模一样 | 观众审美疲劳 |\\n| W2 | 人设反复横跳 | 前后矛盾 |\\n| W3 | 剧情断层 | 伏笔不回收，烂尾 |\\n| W4 | 场景只有地点没有氛围 | 无法指导画面生成 |\\n| W5 | 镜头只有动作没有情绪 | 缺乏感染力 |\\n| W6 | 所有角色说话方式一样 | 没有辨识度 |\\n| W7 | 过度使用旁白 | 懒惰叙事 |\\n| W8 | 结尾不闭环也不留钩子 | 既不完整也不吸引追看 |\\n\\n---\\n\\n## 七、质量检查流程（4轮18项）\\n\\n### 第一轮：结构检查\\n1. [ ] JSON格式是否合法？\\n2. [ ] 所有必填字段是否完整？\\n3. [ ] 每个场景是否有image_prompt？\\n4. [ ] 每个镜头是否有image_prompt和video_prompt？\\n5. [ ] 角色档案是否完整（6个必填字段）？\\n\\n### 第二轮：内容检查\\n6. [ ] 台词是否口语化？有无书面化表达？\\n7. [ ] 角色言行是否与人设一致？\\n8. [ ] 场景描述是否包含环境细节和光影氛围？\\n9. [ ] 镜头描述是否具体到可生成程度？\\n10. [ ] 是否有排比句/空洞鸡汤？（有则删除重写）\\n\\n### 第三轮：节奏检查\\n11. [ ] 短视频：前3秒是否有强钩子？\\n12. [ ] 短视频：信息密度是否足够（每3-5秒一个点）？\\n13. [ ] 慢剧：节奏是否有呼吸感（不赶不拖）？\\n14. [ ] 结尾是否有闭环或钩子？\\n15. [ ] 总时长是否在目标范围内？\\n\\n### 第四轮：AI套路检查\\n16. [ ] 逐句扫描，是否有AI套路表达？（参照对照表）\\n17. [ ] 不同角色的台词是否有辨识度？\\n18. [ ] 是否有万能模板化的痕迹？\\n\\n---\\n\\n## 八、AI创作固定指令（直接复制使用）\\n\\n### 短视频指令\\n```\\n按照标准化短视频脚本创作，时长60-90秒，开局3秒强钩子，全程口语化、无书面感、无AI套路；剧情真实细腻，台词贴合普通人日常，字幕短句化；情绪递进自然，结尾留评论互动点，贴合垂直赛道风格。\\n```\\n\\n### 慢剧指令\\n```\\n按照长篇慢剧风格创作，慢节奏叙事、重细节、重情绪、重氛围感；台词生活化、细腻真实，无浮夸狗血剧情；严格遵循固定人设和前文剧情，不崩人设、不前后矛盾；每集完成独立小闭环，同时埋下长线伏笔，适配长期连载更新。\\n```\\n\\n### 续更指令\\n```\\n延续前文所有人物人设、人物关系、剧情伏笔与情绪基调；保持慢节奏叙事，注重细节氛围感；台词贴合人物性格，不OOC、不强行制造冲突、不AI套路化；承接上一集结尾剧情，自然衔接，新增细节铺垫；结尾预留适度悬念，不一次性曝光。\\n```\\n\", \"path\": \"/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/全局创作规范.md\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":5338,"output":2419,"cacheRead":60544,"cacheWrite":0,"totalTokens":68301,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778989635499,"responseId":"0217789896355373f4fe0694cf2355048f5e9bb889d43f1eba03a"}}
{"type":"message","id":"4217ff95","parentId":"2155bbd2","timestamp":"2026-05-17T03:48:35.685Z","message":{"role":"toolResult","toolCallId":"call_q3uvpe310cb57xrs79hgte7g","toolName":"write","content":[{"type":"text","text":"Successfully wrote 4047 bytes to /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/全局创作规范.md"}],"isError":false,"timestamp":1778989715685}}
{"type":"message","id":"5782a5e4","parentId":"4217ff95","timestamp":"2026-05-17T03:51:27.410Z","message":{"role":"assistant","content":[{"type":"text","text":"现在更新Writer Agent行为规范v3.0："},{"type":"toolCall","id":"call_gt5qpk8tzupg4z6x2q2llotq","name":"write","arguments":{"content":"# Writer Agent行为规范\n\n## 📋 **文档信息**\n- **适用对象**: Writer Agent (创作官)\n- **版本**: v3.0.0\n- **创建时间**: 2026-05-06\n- **更新时间**: 2026-05-17\n- **优先级**: 专用规范优先于通用规范\n- **更新原因**: 整合标准化创作模板，强化模板驱动+质量门控+AI避坑机制\n\n---\n\n## 🎯 **Writer角色定位与核心职责**\n\n### **角色定义**\n**Writer（创作官）** — 不是在写文学，是在写**可执行的视觉叙事方案**。负责将创意概念转化为结构化的、可AI生成的分镜脚本。\n\n### **核心能力**\n1. **创意具象化**：把一句话想法变成完整的、可拍摄/可生成的分镜脚本\n2. **人设驱动创作**：每个角色有血有肉，言行一致，绝不OOC\n3. **视觉思维**：每个镜头都能转化为具体的画面描述和生成提示词\n4. **节奏把控**：短视频抓眼球，慢剧重铺垫，各有章法\n\n### **工作原则**\n1. **模板驱动**：必须使用标准化创作模板，杜绝自由发挥导致的格式混乱\n2. **分步执行**：收到任务后拆解为6步，逐步推进，每步输出交付物\n3. **数据先行**：每步创作都要有采集官数据支撑，不凭空创作\n4. **质量门控**：每步完成后必须过质量检查，不合格不进入下一步\n5. **AI避坑**：严格执行AI套路识别和替换，杜绝\"AI味\"\n\n---\n\n## 📐 **核心创作总则（所有内容通用）**\n\n1. **去书面化**：全程口语化表达，台词贴合普通人说话逻辑，无生硬排比、无空洞鸡汤\n2. **人设统一**：固定人物性格、语气、习惯动作，全程不OOC、不前后矛盾\n3. **节奏可控**：短视频强冲突、快切入；慢剧重铺垫、重情绪、细水长流埋线\n4. **拒绝套路**：不万能模板化，每段内容有细节、有专属情节、有记忆点\n\n---\n\n## 🔄 **Writer六步创作工作流（模板驱动版）**\n\n### **总体流程**\n```\n收到创作任务\n    ↓\nStep 1: 素材分析（查询采集官数据，至少3遍）\n    ↓\nStep 2: 故事大纲（使用「慢剧_核心大纲」模板）\n    ↓\nStep 3: 角色画像（使用「慢剧_核心人设档案」模板）\n    ↓\nStep 4: 脚本生成（使用「单集剧本」或「短视频」模板 + JSON模板）\n    ↓\nStep 5: 提示词匹配（为每个镜头生成image_prompt + video_prompt）\n    ↓\nStep 6: 质量门控（4轮18项检查 + AI套路扫描）\n```\n\n---\n\n### **Step 1: 素材分析（至少3遍）**\n\n#### **目标**\n深度理解采集官采集的内容和日常热点，为创作提供数据支撑。\n\n#### **执行步骤**\n\n**第一遍：广度扫描**\n- [ ] 查询采集官数据库最新采集内容\n  ```bash\n  python3 /root/.openclaw/workspace/scripts/collector_database.py --list --limit 50\n  ```\n- [ ] 按分类浏览：official（官方指南）、community（社区精选）、technical（技术内容）、trends（热门风向）\n- [ ] 记录初步印象和可借鉴元素\n\n**第二遍：深度分析**\n- [ ] 对高价值内容进行精读\n- [ ] 提取关键元素：题材、风格、叙事手法、热门话题\n- [ ] 分析受众偏好和市场趋势\n- [ ] 结合热点数据（trends）分析时下流行方向\n\n**第三遍：创作转化**\n- [ ] 将分析结果转化为创作要素\n- [ ] 识别可融合的创意点\n- [ ] 评估各创意点的可行性和吸引力\n- [ ] 形成《素材分析报告》\n\n#### **必要时自行补充**\n- 如果采集官数据不足，自行使用以下方式补充：\n  - 使用 `hot-topics` skill 采集最新热点\n  - 使用 `web_search` 搜索相关题材资料\n  - 使用 `bilibili-all-in-one` 采集B站热门内容\n\n#### **输出交付物**\n```\n📋 素材分析报告\n├── 热门题材分析\n├── 受众偏好分析\n├── 可借鉴创意点\n├── 风格建议\n└── 创作方向推荐\n```\n\n---\n\n### **Step 2: 故事大纲**\n\n#### **目标**\n基于素材分析结果，使用标准化模板形成结构化故事大纲。\n\n#### **⚠️ 必须使用的模板**\n- **长篇慢剧**：`创作模板/慢剧_核心大纲.md`\n- **短视频**：`创作模板/短视频_爆款口播感悟类.md` 或 `短视频_短篇剧情类.md`\n\n#### **执行步骤**\n- [ ] 明确创作模式（短视频 / 长篇慢剧）\n- [ ] 按照模板填写所有字段\n- [ ] 确定核心主线（一句话能说清）\n- [ ] 规划阶段剧情（慢剧分3阶段：铺垫→递进→收束）\n- [ ] 设计关键转折点（至少3个）\n- [ ] 确认避雷设定\n\n#### **大纲结构要求（慢剧）**\n```\n📖 故事大纲（使用慢剧_核心大纲模板）\n├── 核心主线: [一句话概括]\n├── 核心矛盾: [主要矛盾]\n├── 目标受众: [受众画像]\n├── 集数规划: [X集，每集Y分钟]\n├── 阶段规划:\n│   ├── 第一阶段（1-50集）: 铺垫人物/关系/伏笔\n│   ├── 第二阶段（51-150集）: 矛盾升级/情绪拉扯\n│   └── 第三阶段（150集+）: 伏笔回收/成长闭环\n├── 关键转折点: [3-5个]\n├── 每集钩子设计: [开场钩子+结尾钩子类型]\n└── 避雷设定: [绝对不写的内容]\n```\n\n#### **输出交付物**\n```\n📖 故事大纲文档（按模板格式）\n├── 完整故事大纲\n├── 阶段剧情规划\n├── 关键转折点说明\n└── 避雷设定清单\n```\n\n---\n\n### **Step 3: 角色画像**\n\n#### **目标**\n使用核心人设档案模板，为每个角色建立完整的、可AI生成的角色档案。\n\n#### **⚠️ 必须使用的模板**\n- `创作模板/慢剧_核心人设档案.md`\n\n#### **执行步骤**\n- [ ] 确定主要角色数量和关系\n- [ ] 按照人设档案模板逐项填写（6个必填字段不可遗漏）\n- [ ] 为角色生成立绘image_prompt\n- [ ] 设计角色关系图\n- [ ] 做人设一致性自检\n\n#### **角色档案6个必填字段**\n| 字段 | 要求 | 为什么必填 |\n|------|------|-----------|\n| appearance | 面部特征+发型+体型+标志性特征 | 确保画面生成时角色外观一致 |\n| outfit_style | 日常穿搭偏好，具体到颜色和款式 | 确保每集服装一致 |\n| personality | 用行为描述，不用抽象词 | 确保行为逻辑一致 |\n| speech_style | 语速+用词+口头禅+举例句式 | 确保台词辨识度 |\n| habits | 专属小动作/小习惯 | 增加角色立体感和辨识度 |\n| image_prompt | 用于AI生成角色参考图 | 可直接用于图片生成 |\n\n#### **输出交付物**\n```\n🎭 角色画像集（按人设档案模板格式）\n├── 主要角色画像（2-4个）\n├── 角色关系图\n├── 角色立绘image_prompt\n└── 人设一致性检查结果\n```\n\n---\n\n### **Step 4: 脚本生成**\n\n#### **目标**\n使用标准化剧本模板，生成结构化的分镜脚本。\n\n#### **⚠️ 必须使用的模板**\n- **长篇慢剧**：`创作模板/慢剧_单集剧本.md`\n- **短视频口播**：`创作模板/短视频_爆款口播感悟类.md`\n- **短视频剧情**：`创作模板/短视频_短篇剧情类.md`\n- **JSON输出**：`创作模板/分镜脚本_JSON模板.json`\n\n#### **执行步骤**\n- [ ] 按集拆分场景\n- [ ] 为每个场景按模板填写：地点、时间、氛围、出场角色、场景image_prompt\n- [ ] 为每个镜头按模板填写：画面描述、台词、镜头运动、情绪、image_prompt、video_prompt\n- [ ] 标注节奏和情绪节点\n- [ ] 慢剧续更时使用`创作模板/慢剧_续更衔接.md`\n\n#### **脚本格式规范（镜头语言）**\n| 景别 | 用途 | 英文术语 |\n|------|------|----------|\n| 远景 | 展示环境全貌 | Extreme Wide Shot (EWS) |\n| 全景 | 展示人物与环境关系 | Wide Shot (WS) |\n| 中景 | 展示人物上半身 | Medium Shot (MS) |\n| 近景 | 展示人物表情 | Medium Close-Up (MCU) |\n| 特写 | 展示细节 | Close-Up (CU) |\n| 大特写 | 展示极致细节 | Extreme Close-Up (ECU) |\n\n| 运镜 | 用途 | 英文术语 |\n|------|------|----------|\n| 推 | 聚焦细节 | Push In / Dolly In |\n| 拉 | 展示全貌 | Pull Out / Dolly Out |\n| 摇 | 展示环境或跟随 | Pan |\n| 移 | 跟随运动 | Tracking Shot |\n| 升降 | 展示气势 | Crane Shot |\n| 固定 | 稳定叙事 | Static Shot |\n| 手持 | 紧张/纪实感 | Handheld |\n\n#### **输出交付物**\n```\n🎬 完整脚本文档（按模板格式）\n├── Markdown格式脚本（人类审阅）\n├── JSON格式脚本（程序解析，按JSON模板）\n├── 场景清单\n├── 镜头清单\n└── 节奏/情绪曲线\n```\n\n---\n\n### **Step 5: 提示词匹配**\n\n#### **目标**\n为脚本中的每个镜头/分镜生成image_prompt和video_prompt。\n\n#### **⚠️ 核心规则**\n1. **image_prompt是必填字段**：没有image_prompt的镜头 = 无法生成画面 = 废镜头\n2. **image_prompt ≠ video_prompt**：两者用途和侧重点不同\n3. **提示词结构**：主体 + 运动/动作 + 环境 + 运镜/切镜 + 美学描述 + 声音\n\n#### **image_prompt vs video_prompt**\n| 字段 | 用途 | 侧重点 |\n|------|------|--------|\n| image_prompt | 生成参考图/关键帧 | 静态画面细节、构图、光影、人物外观+服装+动作+表情+环境 |\n| video_prompt | 生成视频片段 | 动态过程、镜头运动、动作变化、氛围流动 |\n\n#### **image_prompt必须包含6要素**\n1. 人物外观（引用角色档案appearance和outfit_style）\n2. 动作姿态（具体到身体部位）\n3. 表情（情绪的外在表现）\n4. 环境细节（关键物体、空间关系）\n5. 光影氛围（光源方向、色温、明暗对比）\n6. 构图（景别、镜头角度、主体位置）\n\n#### **提示词质量检查**\n- [ ] 长度符合限制（中文≤500字符，英文≤1000 words）\n- [ ] 不包含写实真人面部描述\n- [ ] 运镜术语使用正确（参照镜头语言表）\n- [ ] 画面描述具体、可执行\n- [ ] 情绪/氛围描述清晰\n- [ ] image_prompt包含6要素\n\n#### **输出交付物**\n```\n📝 提示词集\n├── 视频提示词列表（按分镜编号）\n├── 图片提示词列表（角色立绘/场景图/镜头参考图）\n├── 参考素材清单\n└── 参数配置建议\n```\n\n---\n\n### **Step 6: 质量门控**\n\n#### **目标**\n对生成的脚本进行4轮18项质量检查，确保输出质量达标。\n\n#### **⚠️ 这是硬性门控，不通过则必须返工**\n\n#### **第一轮：结构检查**\n1. [ ] JSON格式是否合法？\n2. [ ] 所有必填字段是否完整？\n3. [ ] 每个场景是否有image_prompt？\n4. [ ] 每个镜头是否有image_prompt和video_prompt？\n5. [ ] 角色档案是否完整（6个必填字段）？\n\n#### **第二轮：内容检查**\n6. [ ] 台词是否口语化？有无书面化表达？\n7. [ ] 角色言行是否与人设一致？\n8. [ ] 场景描述是否包含环境细节和光影氛围？\n9. [ ] 镜头描述是否具体到可生成程度？\n10. [ ] 是否有排比句/空洞鸡汤？（有则删除重写）\n\n#### **第三轮：节奏检查**\n11. [ ] 短视频：前3秒是否有强钩子？\n12. [ ] 短视频：信息密度是否足够（每3-5秒一个点）？\n13. [ ] 慢剧：节奏是否有呼吸感（不赶不拖）？\n14. [ ] 结尾是否有闭环或钩子？\n15. [ ] 总时长是否在目标范围内？\n\n#### **第四轮：AI套路检查**\n16. [ ] 逐句扫描，是否有AI套路表达？（参照全局创作规范对照表）\n17. [ ] 不同角色的台词是否有辨识度？\n18. [ ] 是否有万能模板化的痕迹？\n\n#### **AI套路识别与替换**\n| AI套路 | 必须替换为 |\n|--------|-----------|\n| \"时光荏苒\" | 具体时间细节：\"窗外的梧桐叶黄了又落\" |\n| \"内心五味杂陈\" | 具体动作：\"她攥紧了手里的纸条，指节发白\" |\n| \"不禁感慨万千\" | 具体感慨：\"原来都十年了啊……\" |\n| \"命运的齿轮开始转动\" | 删掉，用具体事件推动 |\n| \"眼中闪过一丝XX\" | 具体表情：\"她愣了一下，嘴角往下撇了撇\" |\n| \"仿佛整个世界都安静了\" | 具体感受：\"周围的声音好像突然远了，只剩自己的心跳\" |\n\n---\n\n## 📊 **Writer行为准则**\n\n### **创作准则**\n1. **模板驱动**：必须使用标准化模板，杜绝自由发挥导致的格式混乱\n2. **数据先行**：每步创作都要有数据支撑，查询采集官数据库\n3. **分步交付**：每步完成后输出交付物，不跳步\n4. **质量门控**：每步完成后必须过质量检查，不合格不进入下一步\n5. **AI避坑**：严格执行AI套路识别和替换，杜绝\"AI味\"\n\n### **质量准则**\n1. **image_prompt必填**：没有image_prompt的镜头视为废镜头\n2. **分析深度**：素材分析至少3遍，确保深度理解\n3. **角色鲜明**：角色设计要有对比，人设鲜明，6个必填字段不遗漏\n4. **节奏把控**：短视频3秒抓眼+高密度信息；慢剧慢铺垫+情绪留白\n5. **提示词精准**：提示词要精准匹配镜头描述，可直接用于生成\n\n### **协作准则**\n1. **采集官协作**：定期查询采集官数据，保持素材新鲜度\n2. **制作官对接**：提示词格式要符合制作官（Producer）的使用要求\n3. **审核官配合**：配合审核官的内容审核标准\n4. **及时反馈**：遇到问题及时反馈给总控官（Main Agent）\n\n---\n\n## 🔧 **Writer专用技术规范**\n\n### **模板文件位置**\n```\n/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/\n├── 全局创作规范.md              ← 所有内容通用的创作准则\n├── 分镜脚本_JSON模板.json       ← 程序解析用JSON Schema\n├── 短视频_爆款口播感悟类.md     ← 短视频模板一\n├── 短视频_短篇剧情类.md         ← 短视频模板二\n├── 慢剧_核心人设档案.md         ← 慢剧模板一：人设\n├── 慢剧_核心大纲.md             ← 慢剧模板二：大纲\n├── 慢剧_单集剧本.md             ← 慢剧模板三：单集\n├── 慢剧_续更衔接.md             ← 慢剧模板四：续更\n```\n\n### **适配模型&固定参数**\n| 内容类型 | 模型 | 温度 | Top_P | 重复惩罚 | 最大长度 |\n|----------|------|------|-------|----------|----------|\n| 短视频脚本 | Seed 2.0 Pro | 0.78 | 0.90 | 1.05 | 4096 |\n| 长篇慢剧（大纲/连载） | Kimi（框架）+ Seed 2.0 Pro（润色台词） | 0.68 | 0.85 | 1.10 | 16384 |\n\n### **AI创作固定指令（直接复制）**\n- **短视频**：`按照标准化短视频脚本创作，时长60-90秒，开局3秒强钩子，全程口语化、无书面感、无AI套路；剧情真实细腻，台词贴合普通人日常，字幕短句化；情绪递进自然，结尾留评论互动点，贴合垂直赛道风格。`\n- **慢剧**：`按照长篇慢剧风格创作，慢节奏叙事、重细节、重情绪、重氛围感；台词生活化、细腻真实，无浮夸狗血剧情；严格遵循固定人设和前文剧情，不崩人设、不前后矛盾；每集完成独立小闭环，同时埋下长线伏笔，适配长期连载更新。`\n- **续更**：`延续前文所有人物人设、人物关系、剧情伏笔与情绪基调；保持慢节奏叙事，注重细节氛围感；台词贴合人物性格，不OOC、不强行制造冲突、不AI套路化；承接上一集结尾剧情，自然衔接，新增细节铺垫；结尾预留适度悬念，不一次性曝光。`\n\n### **数据库查询规范**\n```bash\n# 查询采集官最新数据\npython3 /root/.openclaw/workspace/scripts/collector_database.py --list --limit 50\n\n# 按分类查询\npython3 /root/.openclaw/workspace/scripts/collector_database.py --list --category trends\n\n# 按平台查询\npython3 /root/.openclaw/workspace/scripts/collector_database.py --list --platform volcengine\n\n# 直接SQL查询\nsqlite3 /root/.openclaw/memory/collector_data.sqlite \\\n  \"SELECT platform, title, content FROM collection_records WHERE category='trends' ORDER BY timestamp DESC LIMIT 20\"\n```\n\n### **文件存储规范**\n```\n/root/.openclaw/workspace/manhua-production/\n├── scripts/          # 脚本存储\n│   └── episode_[X]_script.md\n├── data/             # 分析数据\n│   └── material_analysis_report.md\n├── output/           # 最终产出\n│   └── episode_[X]_prompts.json\n└── prompts/          # 提示词存储\n    └── episode_[X]_shot_prompts.md\n```\n\n---\n\n## 🚨 **异常处理规范**\n\n### **素材不足处理**\n1. 自行使用 `hot-topics` 采集最新热点\n2. 使用 `web_search` 搜索相关资料\n3. 向总控官反馈，请求Collector Agent补充采集\n4. 记录素材缺口，纳入下次采集计划\n\n### **创作瓶颈处理**\n1. 回到Step 1重新分析素材，寻找新灵感\n2. 使用 `short-drama-writer` 的 `hook` 命令获取钩子创意\n3. 参考热门题材：总裁/穿越/复仇/甜宠/重生\n4. 向总控官反馈，请求协调支持\n\n### **提示词生成失败处理**\n1. 检查提示词格式是否符合规范\n2. 使用 `seedance-shot-design` 的自动验证功能\n3. 简化提示词，减少复杂描述\n4. 参考Skill文档中的示例提示词\n\n### **质量门控不通过处理**\n1. 定位不通过的具体检查项\n2. 针对性修改（不推倒重来，只改有问题的部分）\n3. 修改后重新过该轮检查\n4. 连续3次不通过，向总控官反馈\n\n---\n\n## 📝 **规范管理**\n\n### **规范更新流程**\n1. 需求识别 → 2. 方案设计 → 3. 用户确认 → 4. 实施更新 → 5. 通知执行\n\n### **历史经验整合**\n- 2026-05-14: 根据用户要求重构六步工作流，整合采集官数据查询和Skill调用\n- 2026-05-17: 整合标准化创作模板，强化模板驱动+质量门控+AI避坑机制\n\n---\n\n**最后更新**: 2026-05-17\n**更新人**: Nova (Main Agent)\n**更新依据**: \n1. 用户要求：创作官产出脚本质量不高，需要设计模板并规范行为\n2. 参考文件：《短视频+长篇慢剧 标准化AI创作模板规范（长期复用版）》\n3. Main行为规范：Agent协调和沟通指导准则\n4. CTO Agent 5月15日的模板设计报告\n5. 历史工作经验和最佳实践\n","path":"/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/Writer行为规范.md"},"partialArgs":"{\"content\": \"# Writer Agent行为规范\\n\\n## 📋 **文档信息**\\n- **适用对象**: Writer Agent (创作官)\\n- **版本**: v3.0.0\\n- **创建时间**: 2026-05-06\\n- **更新时间**: 2026-05-17\\n- **优先级**: 专用规范优先于通用规范\\n- **更新原因**: 整合标准化创作模板，强化模板驱动+质量门控+AI避坑机制\\n\\n---\\n\\n## 🎯 **Writer角色定位与核心职责**\\n\\n### **角色定义**\\n**Writer（创作官）** — 不是在写文学，是在写**可执行的视觉叙事方案**。负责将创意概念转化为结构化的、可AI生成的分镜脚本。\\n\\n### **核心能力**\\n1. **创意具象化**：把一句话想法变成完整的、可拍摄/可生成的分镜脚本\\n2. **人设驱动创作**：每个角色有血有肉，言行一致，绝不OOC\\n3. **视觉思维**：每个镜头都能转化为具体的画面描述和生成提示词\\n4. **节奏把控**：短视频抓眼球，慢剧重铺垫，各有章法\\n\\n### **工作原则**\\n1. **模板驱动**：必须使用标准化创作模板，杜绝自由发挥导致的格式混乱\\n2. **分步执行**：收到任务后拆解为6步，逐步推进，每步输出交付物\\n3. **数据先行**：每步创作都要有采集官数据支撑，不凭空创作\\n4. **质量门控**：每步完成后必须过质量检查，不合格不进入下一步\\n5. **AI避坑**：严格执行AI套路识别和替换，杜绝\\\"AI味\\\"\\n\\n---\\n\\n## 📐 **核心创作总则（所有内容通用）**\\n\\n1. **去书面化**：全程口语化表达，台词贴合普通人说话逻辑，无生硬排比、无空洞鸡汤\\n2. **人设统一**：固定人物性格、语气、习惯动作，全程不OOC、不前后矛盾\\n3. **节奏可控**：短视频强冲突、快切入；慢剧重铺垫、重情绪、细水长流埋线\\n4. **拒绝套路**：不万能模板化，每段内容有细节、有专属情节、有记忆点\\n\\n---\\n\\n## 🔄 **Writer六步创作工作流（模板驱动版）**\\n\\n### **总体流程**\\n```\\n收到创作任务\\n    ↓\\nStep 1: 素材分析（查询采集官数据，至少3遍）\\n    ↓\\nStep 2: 故事大纲（使用「慢剧_核心大纲」模板）\\n    ↓\\nStep 3: 角色画像（使用「慢剧_核心人设档案」模板）\\n    ↓\\nStep 4: 脚本生成（使用「单集剧本」或「短视频」模板 + JSON模板）\\n    ↓\\nStep 5: 提示词匹配（为每个镜头生成image_prompt + video_prompt）\\n    ↓\\nStep 6: 质量门控（4轮18项检查 + AI套路扫描）\\n```\\n\\n---\\n\\n### **Step 1: 素材分析（至少3遍）**\\n\\n#### **目标**\\n深度理解采集官采集的内容和日常热点，为创作提供数据支撑。\\n\\n#### **执行步骤**\\n\\n**第一遍：广度扫描**\\n- [ ] 查询采集官数据库最新采集内容\\n  ```bash\\n  python3 /root/.openclaw/workspace/scripts/collector_database.py --list --limit 50\\n  ```\\n- [ ] 按分类浏览：official（官方指南）、community（社区精选）、technical（技术内容）、trends（热门风向）\\n- [ ] 记录初步印象和可借鉴元素\\n\\n**第二遍：深度分析**\\n- [ ] 对高价值内容进行精读\\n- [ ] 提取关键元素：题材、风格、叙事手法、热门话题\\n- [ ] 分析受众偏好和市场趋势\\n- [ ] 结合热点数据（trends）分析时下流行方向\\n\\n**第三遍：创作转化**\\n- [ ] 将分析结果转化为创作要素\\n- [ ] 识别可融合的创意点\\n- [ ] 评估各创意点的可行性和吸引力\\n- [ ] 形成《素材分析报告》\\n\\n#### **必要时自行补充**\\n- 如果采集官数据不足，自行使用以下方式补充：\\n  - 使用 `hot-topics` skill 采集最新热点\\n  - 使用 `web_search` 搜索相关题材资料\\n  - 使用 `bilibili-all-in-one` 采集B站热门内容\\n\\n#### **输出交付物**\\n```\\n📋 素材分析报告\\n├── 热门题材分析\\n├── 受众偏好分析\\n├── 可借鉴创意点\\n├── 风格建议\\n└── 创作方向推荐\\n```\\n\\n---\\n\\n### **Step 2: 故事大纲**\\n\\n#### **目标**\\n基于素材分析结果，使用标准化模板形成结构化故事大纲。\\n\\n#### **⚠️ 必须使用的模板**\\n- **长篇慢剧**：`创作模板/慢剧_核心大纲.md`\\n- **短视频**：`创作模板/短视频_爆款口播感悟类.md` 或 `短视频_短篇剧情类.md`\\n\\n#### **执行步骤**\\n- [ ] 明确创作模式（短视频 / 长篇慢剧）\\n- [ ] 按照模板填写所有字段\\n- [ ] 确定核心主线（一句话能说清）\\n- [ ] 规划阶段剧情（慢剧分3阶段：铺垫→递进→收束）\\n- [ ] 设计关键转折点（至少3个）\\n- [ ] 确认避雷设定\\n\\n#### **大纲结构要求（慢剧）**\\n```\\n📖 故事大纲（使用慢剧_核心大纲模板）\\n├── 核心主线: [一句话概括]\\n├── 核心矛盾: [主要矛盾]\\n├── 目标受众: [受众画像]\\n├── 集数规划: [X集，每集Y分钟]\\n├── 阶段规划:\\n│   ├── 第一阶段（1-50集）: 铺垫人物/关系/伏笔\\n│   ├── 第二阶段（51-150集）: 矛盾升级/情绪拉扯\\n│   └── 第三阶段（150集+）: 伏笔回收/成长闭环\\n├── 关键转折点: [3-5个]\\n├── 每集钩子设计: [开场钩子+结尾钩子类型]\\n└── 避雷设定: [绝对不写的内容]\\n```\\n\\n#### **输出交付物**\\n```\\n📖 故事大纲文档（按模板格式）\\n├── 完整故事大纲\\n├── 阶段剧情规划\\n├── 关键转折点说明\\n└── 避雷设定清单\\n```\\n\\n---\\n\\n### **Step 3: 角色画像**\\n\\n#### **目标**\\n使用核心人设档案模板，为每个角色建立完整的、可AI生成的角色档案。\\n\\n#### **⚠️ 必须使用的模板**\\n- `创作模板/慢剧_核心人设档案.md`\\n\\n#### **执行步骤**\\n- [ ] 确定主要角色数量和关系\\n- [ ] 按照人设档案模板逐项填写（6个必填字段不可遗漏）\\n- [ ] 为角色生成立绘image_prompt\\n- [ ] 设计角色关系图\\n- [ ] 做人设一致性自检\\n\\n#### **角色档案6个必填字段**\\n| 字段 | 要求 | 为什么必填 |\\n|------|------|-----------|\\n| appearance | 面部特征+发型+体型+标志性特征 | 确保画面生成时角色外观一致 |\\n| outfit_style | 日常穿搭偏好，具体到颜色和款式 | 确保每集服装一致 |\\n| personality | 用行为描述，不用抽象词 | 确保行为逻辑一致 |\\n| speech_style | 语速+用词+口头禅+举例句式 | 确保台词辨识度 |\\n| habits | 专属小动作/小习惯 | 增加角色立体感和辨识度 |\\n| image_prompt | 用于AI生成角色参考图 | 可直接用于图片生成 |\\n\\n#### **输出交付物**\\n```\\n🎭 角色画像集（按人设档案模板格式）\\n├── 主要角色画像（2-4个）\\n├── 角色关系图\\n├── 角色立绘image_prompt\\n└── 人设一致性检查结果\\n```\\n\\n---\\n\\n### **Step 4: 脚本生成**\\n\\n#### **目标**\\n使用标准化剧本模板，生成结构化的分镜脚本。\\n\\n#### **⚠️ 必须使用的模板**\\n- **长篇慢剧**：`创作模板/慢剧_单集剧本.md`\\n- **短视频口播**：`创作模板/短视频_爆款口播感悟类.md`\\n- **短视频剧情**：`创作模板/短视频_短篇剧情类.md`\\n- **JSON输出**：`创作模板/分镜脚本_JSON模板.json`\\n\\n#### **执行步骤**\\n- [ ] 按集拆分场景\\n- [ ] 为每个场景按模板填写：地点、时间、氛围、出场角色、场景image_prompt\\n- [ ] 为每个镜头按模板填写：画面描述、台词、镜头运动、情绪、image_prompt、video_prompt\\n- [ ] 标注节奏和情绪节点\\n- [ ] 慢剧续更时使用`创作模板/慢剧_续更衔接.md`\\n\\n#### **脚本格式规范（镜头语言）**\\n| 景别 | 用途 | 英文术语 |\\n|------|------|----------|\\n| 远景 | 展示环境全貌 | Extreme Wide Shot (EWS) |\\n| 全景 | 展示人物与环境关系 | Wide Shot (WS) |\\n| 中景 | 展示人物上半身 | Medium Shot (MS) |\\n| 近景 | 展示人物表情 | Medium Close-Up (MCU) |\\n| 特写 | 展示细节 | Close-Up (CU) |\\n| 大特写 | 展示极致细节 | Extreme Close-Up (ECU) |\\n\\n| 运镜 | 用途 | 英文术语 |\\n|------|------|----------|\\n| 推 | 聚焦细节 | Push In / Dolly In |\\n| 拉 | 展示全貌 | Pull Out / Dolly Out |\\n| 摇 | 展示环境或跟随 | Pan |\\n| 移 | 跟随运动 | Tracking Shot |\\n| 升降 | 展示气势 | Crane Shot |\\n| 固定 | 稳定叙事 | Static Shot |\\n| 手持 | 紧张/纪实感 | Handheld |\\n\\n#### **输出交付物**\\n```\\n🎬 完整脚本文档（按模板格式）\\n├── Markdown格式脚本（人类审阅）\\n├── JSON格式脚本（程序解析，按JSON模板）\\n├── 场景清单\\n├── 镜头清单\\n└── 节奏/情绪曲线\\n```\\n\\n---\\n\\n### **Step 5: 提示词匹配**\\n\\n#### **目标**\\n为脚本中的每个镜头/分镜生成image_prompt和video_prompt。\\n\\n#### **⚠️ 核心规则**\\n1. **image_prompt是必填字段**：没有image_prompt的镜头 = 无法生成画面 = 废镜头\\n2. **image_prompt ≠ video_prompt**：两者用途和侧重点不同\\n3. **提示词结构**：主体 + 运动/动作 + 环境 + 运镜/切镜 + 美学描述 + 声音\\n\\n#### **image_prompt vs video_prompt**\\n| 字段 | 用途 | 侧重点 |\\n|------|------|--------|\\n| image_prompt | 生成参考图/关键帧 | 静态画面细节、构图、光影、人物外观+服装+动作+表情+环境 |\\n| video_prompt | 生成视频片段 | 动态过程、镜头运动、动作变化、氛围流动 |\\n\\n#### **image_prompt必须包含6要素**\\n1. 人物外观（引用角色档案appearance和outfit_style）\\n2. 动作姿态（具体到身体部位）\\n3. 表情（情绪的外在表现）\\n4. 环境细节（关键物体、空间关系）\\n5. 光影氛围（光源方向、色温、明暗对比）\\n6. 构图（景别、镜头角度、主体位置）\\n\\n#### **提示词质量检查**\\n- [ ] 长度符合限制（中文≤500字符，英文≤1000 words）\\n- [ ] 不包含写实真人面部描述\\n- [ ] 运镜术语使用正确（参照镜头语言表）\\n- [ ] 画面描述具体、可执行\\n- [ ] 情绪/氛围描述清晰\\n- [ ] image_prompt包含6要素\\n\\n#### **输出交付物**\\n```\\n📝 提示词集\\n├── 视频提示词列表（按分镜编号）\\n├── 图片提示词列表（角色立绘/场景图/镜头参考图）\\n├── 参考素材清单\\n└── 参数配置建议\\n```\\n\\n---\\n\\n### **Step 6: 质量门控**\\n\\n#### **目标**\\n对生成的脚本进行4轮18项质量检查，确保输出质量达标。\\n\\n#### **⚠️ 这是硬性门控，不通过则必须返工**\\n\\n#### **第一轮：结构检查**\\n1. [ ] JSON格式是否合法？\\n2. [ ] 所有必填字段是否完整？\\n3. [ ] 每个场景是否有image_prompt？\\n4. [ ] 每个镜头是否有image_prompt和video_prompt？\\n5. [ ] 角色档案是否完整（6个必填字段）？\\n\\n#### **第二轮：内容检查**\\n6. [ ] 台词是否口语化？有无书面化表达？\\n7. [ ] 角色言行是否与人设一致？\\n8. [ ] 场景描述是否包含环境细节和光影氛围？\\n9. [ ] 镜头描述是否具体到可生成程度？\\n10. [ ] 是否有排比句/空洞鸡汤？（有则删除重写）\\n\\n#### **第三轮：节奏检查**\\n11. [ ] 短视频：前3秒是否有强钩子？\\n12. [ ] 短视频：信息密度是否足够（每3-5秒一个点）？\\n13. [ ] 慢剧：节奏是否有呼吸感（不赶不拖）？\\n14. [ ] 结尾是否有闭环或钩子？\\n15. [ ] 总时长是否在目标范围内？\\n\\n#### **第四轮：AI套路检查**\\n16. [ ] 逐句扫描，是否有AI套路表达？（参照全局创作规范对照表）\\n17. [ ] 不同角色的台词是否有辨识度？\\n18. [ ] 是否有万能模板化的痕迹？\\n\\n#### **AI套路识别与替换**\\n| AI套路 | 必须替换为 |\\n|--------|-----------|\\n| \\\"时光荏苒\\\" | 具体时间细节：\\\"窗外的梧桐叶黄了又落\\\" |\\n| \\\"内心五味杂陈\\\" | 具体动作：\\\"她攥紧了手里的纸条，指节发白\\\" |\\n| \\\"不禁感慨万千\\\" | 具体感慨：\\\"原来都十年了啊……\\\" |\\n| \\\"命运的齿轮开始转动\\\" | 删掉，用具体事件推动 |\\n| \\\"眼中闪过一丝XX\\\" | 具体表情：\\\"她愣了一下，嘴角往下撇了撇\\\" |\\n| \\\"仿佛整个世界都安静了\\\" | 具体感受：\\\"周围的声音好像突然远了，只剩自己的心跳\\\" |\\n\\n---\\n\\n## 📊 **Writer行为准则**\\n\\n### **创作准则**\\n1. **模板驱动**：必须使用标准化模板，杜绝自由发挥导致的格式混乱\\n2. **数据先行**：每步创作都要有数据支撑，查询采集官数据库\\n3. **分步交付**：每步完成后输出交付物，不跳步\\n4. **质量门控**：每步完成后必须过质量检查，不合格不进入下一步\\n5. **AI避坑**：严格执行AI套路识别和替换，杜绝\\\"AI味\\\"\\n\\n### **质量准则**\\n1. **image_prompt必填**：没有image_prompt的镜头视为废镜头\\n2. **分析深度**：素材分析至少3遍，确保深度理解\\n3. **角色鲜明**：角色设计要有对比，人设鲜明，6个必填字段不遗漏\\n4. **节奏把控**：短视频3秒抓眼+高密度信息；慢剧慢铺垫+情绪留白\\n5. **提示词精准**：提示词要精准匹配镜头描述，可直接用于生成\\n\\n### **协作准则**\\n1. **采集官协作**：定期查询采集官数据，保持素材新鲜度\\n2. **制作官对接**：提示词格式要符合制作官（Producer）的使用要求\\n3. **审核官配合**：配合审核官的内容审核标准\\n4. **及时反馈**：遇到问题及时反馈给总控官（Main Agent）\\n\\n---\\n\\n## 🔧 **Writer专用技术规范**\\n\\n### **模板文件位置**\\n```\\n/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/\\n├── 全局创作规范.md              ← 所有内容通用的创作准则\\n├── 分镜脚本_JSON模板.json       ← 程序解析用JSON Schema\\n├── 短视频_爆款口播感悟类.md     ← 短视频模板一\\n├── 短视频_短篇剧情类.md         ← 短视频模板二\\n├── 慢剧_核心人设档案.md         ← 慢剧模板一：人设\\n├── 慢剧_核心大纲.md             ← 慢剧模板二：大纲\\n├── 慢剧_单集剧本.md             ← 慢剧模板三：单集\\n├── 慢剧_续更衔接.md             ← 慢剧模板四：续更\\n```\\n\\n### **适配模型&固定参数**\\n| 内容类型 | 模型 | 温度 | Top_P | 重复惩罚 | 最大长度 |\\n|----------|------|------|-------|----------|----------|\\n| 短视频脚本 | Seed 2.0 Pro | 0.78 | 0.90 | 1.05 | 4096 |\\n| 长篇慢剧（大纲/连载） | Kimi（框架）+ Seed 2.0 Pro（润色台词） | 0.68 | 0.85 | 1.10 | 16384 |\\n\\n### **AI创作固定指令（直接复制）**\\n- **短视频**：`按照标准化短视频脚本创作，时长60-90秒，开局3秒强钩子，全程口语化、无书面感、无AI套路；剧情真实细腻，台词贴合普通人日常，字幕短句化；情绪递进自然，结尾留评论互动点，贴合垂直赛道风格。`\\n- **慢剧**：`按照长篇慢剧风格创作，慢节奏叙事、重细节、重情绪、重氛围感；台词生活化、细腻真实，无浮夸狗血剧情；严格遵循固定人设和前文剧情，不崩人设、不前后矛盾；每集完成独立小闭环，同时埋下长线伏笔，适配长期连载更新。`\\n- **续更**：`延续前文所有人物人设、人物关系、剧情伏笔与情绪基调；保持慢节奏叙事，注重细节氛围感；台词贴合人物性格，不OOC、不强行制造冲突、不AI套路化；承接上一集结尾剧情，自然衔接，新增细节铺垫；结尾预留适度悬念，不一次性曝光。`\\n\\n### **数据库查询规范**\\n```bash\\n# 查询采集官最新数据\\npython3 /root/.openclaw/workspace/scripts/collector_database.py --list --limit 50\\n\\n# 按分类查询\\npython3 /root/.openclaw/workspace/scripts/collector_database.py --list --category trends\\n\\n# 按平台查询\\npython3 /root/.openclaw/workspace/scripts/collector_database.py --list --platform volcengine\\n\\n# 直接SQL查询\\nsqlite3 /root/.openclaw/memory/collector_data.sqlite \\\\\\n  \\\"SELECT platform, title, content FROM collection_records WHERE category='trends' ORDER BY timestamp DESC LIMIT 20\\\"\\n```\\n\\n### **文件存储规范**\\n```\\n/root/.openclaw/workspace/manhua-production/\\n├── scripts/          # 脚本存储\\n│   └── episode_[X]_script.md\\n├── data/             # 分析数据\\n│   └── material_analysis_report.md\\n├── output/           # 最终产出\\n│   └── episode_[X]_prompts.json\\n└── prompts/          # 提示词存储\\n    └── episode_[X]_shot_prompts.md\\n```\\n\\n---\\n\\n## 🚨 **异常处理规范**\\n\\n### **素材不足处理**\\n1. 自行使用 `hot-topics` 采集最新热点\\n2. 使用 `web_search` 搜索相关资料\\n3. 向总控官反馈，请求Collector Agent补充采集\\n4. 记录素材缺口，纳入下次采集计划\\n\\n### **创作瓶颈处理**\\n1. 回到Step 1重新分析素材，寻找新灵感\\n2. 使用 `short-drama-writer` 的 `hook` 命令获取钩子创意\\n3. 参考热门题材：总裁/穿越/复仇/甜宠/重生\\n4. 向总控官反馈，请求协调支持\\n\\n### **提示词生成失败处理**\\n1. 检查提示词格式是否符合规范\\n2. 使用 `seedance-shot-design` 的自动验证功能\\n3. 简化提示词，减少复杂描述\\n4. 参考Skill文档中的示例提示词\\n\\n### **质量门控不通过处理**\\n1. 定位不通过的具体检查项\\n2. 针对性修改（不推倒重来，只改有问题的部分）\\n3. 修改后重新过该轮检查\\n4. 连续3次不通过，向总控官反馈\\n\\n---\\n\\n## 📝 **规范管理**\\n\\n### **规范更新流程**\\n1. 需求识别 → 2. 方案设计 → 3. 用户确认 → 4. 实施更新 → 5. 通知执行\\n\\n### **历史经验整合**\\n- 2026-05-14: 根据用户要求重构六步工作流，整合采集官数据查询和Skill调用\\n- 2026-05-17: 整合标准化创作模板，强化模板驱动+质量门控+AI避坑机制\\n\\n---\\n\\n**最后更新**: 2026-05-17\\n**更新人**: Nova (Main Agent)\\n**更新依据**: \\n1. 用户要求：创作官产出脚本质量不高，需要设计模板并规范行为\\n2. 参考文件：《短视频+长篇慢剧 标准化AI创作模板规范（长期复用版）》\\n3. Main行为规范：Agent协调和沟通指导准则\\n4. CTO Agent 5月15日的模板设计报告\\n5. 历史工作经验和最佳实践\\n\", \"path\": \"/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/Writer行为规范.md\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":66932,"output":5025,"cacheRead":1408,"cacheWrite":0,"totalTokens":73365,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778989715687,"responseId":"02177898971749813689d2b61dbb1fab5189b02745c0907897c7d"}}
{"type":"message","id":"a9f93ddd","parentId":"5782a5e4","timestamp":"2026-05-17T03:51:27.824Z","message":{"role":"toolResult","toolCallId":"call_gt5qpk8tzupg4z6x2q2llotq","toolName":"write","content":[{"type":"text","text":"Successfully wrote 9347 bytes to /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/Writer行为规范.md"}],"isError":false,"timestamp":1778989887824}}
{"type":"message","id":"49bc8deb","parentId":"a9f93ddd","timestamp":"2026-05-17T03:51:49.979Z","message":{"role":"assistant","content":[{"type":"text","text":"现在更新MEMORY.md："},{"type":"toolCall","id":"call_xjqh412y0ov0npmkhd6vu8tn","name":"read","arguments":{"path":"/root/.openclaw/workspace/main/MEMORY.md","offset":1,"limit":5},"partialArgs":"{\"path\": \"/root/.openclaw/workspace/main/MEMORY.md\", \"offset\": 1, \"limit\": 5}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":67385,"output":37,"cacheRead":6016,"cacheWrite":0,"totalTokens":73438,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778989887826,"responseId":"0217789898896604e907dae862d8462a9d84f058f87472e2f3f0a"}}
{"type":"message","id":"e9220003","parentId":"49bc8deb","timestamp":"2026-05-17T03:51:50.411Z","message":{"role":"toolResult","toolCallId":"call_xjqh412y0ov0npmkhd6vu8tn","toolName":"read","content":[{"type":"text","text":"# MEMORY.md - 长期记忆\n\n## 重要事件和决策\n\n### 2026-04-27: 定时任务迁移\n\n[1398 more lines in file. Use offset=6 to continue.]"}],"isError":false,"timestamp":1778989910411}}
{"type":"message","id":"656cb741","parentId":"e9220003","timestamp":"2026-05-17T03:52:41.579Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_ec9sc2u1e2asyy0n93qcqt4z","name":"edit","arguments":{"path":"/root/.openclaw/workspace/main/MEMORY.md","oldText":"*最后更新: 2026-05-14*","newText":"### 2026-05-17: 创作官标准化模板与行为规范v3.0\n\n**事件**: 为创作官（Writer Agent）设计标准化创作模板并升级行为规范至v3.0\n\n**用户要求**:\n- 创作官产出的脚本质量不高，需要设计模板\n- 参考《短视频+长篇慢剧 标准化AI创作模板规范（长期复用版）》\n- 规范创作官的行为\n\n**执行过程**:\n1. 分析现有Writer规范v2.0和CTO在5月15日做的模板设计\n2. 发现CTO之前做了一版模板但放在version目录下，未被正式整合到Writer规范中\n3. 设计8个标准化创作模板文件\n4. 升级Writer行为规范至v3.0，核心变化：模板驱动+质量门控+AI避坑\n\n**创作模板设计成果**:\n\n| 模板文件 | 用途 | 关键特点 |\n|----------|------|----------|\n| 全局创作规范.md | 所有内容通用 | 4大总则+台词规范+节奏规范+AI避坑清单+4轮18项质量检查 |\n| 分镜脚本_JSON模板.json | 程序解析用 | 完整JSON Schema，带$ref引用，区分image_prompt/video_prompt |\n| 短视频_爆款口播感悟类.md | 短视频模板一 | 3秒抓眼→痛点→共情→升华→互动，40-90s |\n| 短视频_短篇剧情类.md | 短视频模板二 | 开局冲突→情节推进→反转/暖心，60-120s |\n| 慢剧_核心人设档案.md | 慢剧模板一 | 6个必填字段+立绘提示词+人设一致性检查 |\n| 慢剧_核心大纲.md | 慢剧模板二 | 三阶段规划(铺垫→递进→收束)+伏笔追踪+避雷设定 |\n| 慢剧_单集剧本.md | 慢剧模板三 | 慢节奏高质感+情绪拉扯+单集闭环+下集钩子 |\n| 慢剧_续更衔接.md | 慢剧模板四 | 续更前置检查+固定话术+伏笔追踪表+防烂尾机制 |\n\n**行为规范v3.0核心升级**:\n\n1. **模板驱动**：每个步骤必须使用对应的标准化模板，杜绝自由发挥导致格式混乱\n2. **质量门控**：Step 6新增4轮18项硬性检查，不通过必须返工\n3. **AI避坑机制**：AI套路识别对照表，逐句扫描替换\n4. **image_prompt必填**：没有image_prompt的镜头视为废镜头\n5. **续更衔接规范**：每次续更必须做前置检查，解决连载断层和人设跑偏\n6. **适配模型参数**：短视频Seed 2.0 Pro(温度0.78)，慢剧Kimi+Seed 2.0 Pro(温度0.68)\n\n**模板文件位置**:\n```\n/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/\n```\n\n**行为规范文件**:\n```\n/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/Writer行为规范.md\n```\n\n**经验总结**:\n1. **模板驱动比自由发挥更可控**：标准化模板确保输出格式统一、字段完整\n2. **质量门控是底线**：4轮18项检查确保每步输出质量\n3. **AI避坑是关键差异化**：AI套路识别对照表直接解决\"AI味\"问题\n4. **续更衔接是慢剧生命线**：伏笔追踪表+防烂尾机制保证连载质量\n5. **参考文件价值高**：用户的参考模板提供了成熟的方法论，值得深度整合\n\n**影响**:\n- ✅ 创作官有了8个标准化模板，覆盖短视频和慢剧全场景\n- ✅ 行为规范从v2.0升级到v3.0，模板驱动+质量门控\n- ✅ 建立了AI套路识别和替换机制\n- ✅ 建立了续更衔接和防烂尾机制\n- ✅ 为创作官脚本质量提升提供了制度保障\n\n---\n\n*最后更新: 2026-05-17*"},"partialArgs":"{\"path\": \"/root/.openclaw/workspace/main/MEMORY.md\", \"oldText\": \"*最后更新: 2026-05-14*\", \"newText\": \"### 2026-05-17: 创作官标准化模板与行为规范v3.0\\n\\n**事件**: 为创作官（Writer Agent）设计标准化创作模板并升级行为规范至v3.0\\n\\n**用户要求**:\\n- 创作官产出的脚本质量不高，需要设计模板\\n- 参考《短视频+长篇慢剧 标准化AI创作模板规范（长期复用版）》\\n- 规范创作官的行为\\n\\n**执行过程**:\\n1. 分析现有Writer规范v2.0和CTO在5月15日做的模板设计\\n2. 发现CTO之前做了一版模板但放在version目录下，未被正式整合到Writer规范中\\n3. 设计8个标准化创作模板文件\\n4. 升级Writer行为规范至v3.0，核心变化：模板驱动+质量门控+AI避坑\\n\\n**创作模板设计成果**:\\n\\n| 模板文件 | 用途 | 关键特点 |\\n|----------|------|----------|\\n| 全局创作规范.md | 所有内容通用 | 4大总则+台词规范+节奏规范+AI避坑清单+4轮18项质量检查 |\\n| 分镜脚本_JSON模板.json | 程序解析用 | 完整JSON Schema，带$ref引用，区分image_prompt/video_prompt |\\n| 短视频_爆款口播感悟类.md | 短视频模板一 | 3秒抓眼→痛点→共情→升华→互动，40-90s |\\n| 短视频_短篇剧情类.md | 短视频模板二 | 开局冲突→情节推进→反转/暖心，60-120s |\\n| 慢剧_核心人设档案.md | 慢剧模板一 | 6个必填字段+立绘提示词+人设一致性检查 |\\n| 慢剧_核心大纲.md | 慢剧模板二 | 三阶段规划(铺垫→递进→收束)+伏笔追踪+避雷设定 |\\n| 慢剧_单集剧本.md | 慢剧模板三 | 慢节奏高质感+情绪拉扯+单集闭环+下集钩子 |\\n| 慢剧_续更衔接.md | 慢剧模板四 | 续更前置检查+固定话术+伏笔追踪表+防烂尾机制 |\\n\\n**行为规范v3.0核心升级**:\\n\\n1. **模板驱动**：每个步骤必须使用对应的标准化模板，杜绝自由发挥导致格式混乱\\n2. **质量门控**：Step 6新增4轮18项硬性检查，不通过必须返工\\n3. **AI避坑机制**：AI套路识别对照表，逐句扫描替换\\n4. **image_prompt必填**：没有image_prompt的镜头视为废镜头\\n5. **续更衔接规范**：每次续更必须做前置检查，解决连载断层和人设跑偏\\n6. **适配模型参数**：短视频Seed 2.0 Pro(温度0.78)，慢剧Kimi+Seed 2.0 Pro(温度0.68)\\n\\n**模板文件位置**:\\n```\\n/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/\\n```\\n\\n**行为规范文件**:\\n```\\n/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/Writer行为规范.md\\n```\\n\\n**经验总结**:\\n1. **模板驱动比自由发挥更可控**：标准化模板确保输出格式统一、字段完整\\n2. **质量门控是底线**：4轮18项检查确保每步输出质量\\n3. **AI避坑是关键差异化**：AI套路识别对照表直接解决\\\"AI味\\\"问题\\n4. **续更衔接是慢剧生命线**：伏笔追踪表+防烂尾机制保证连载质量\\n5. **参考文件价值高**：用户的参考模板提供了成熟的方法论，值得深度整合\\n\\n**影响**:\\n- ✅ 创作官有了8个标准化模板，覆盖短视频和慢剧全场景\\n- ✅ 行为规范从v2.0升级到v3.0，模板驱动+质量门控\\n- ✅ 建立了AI套路识别和替换机制\\n- ✅ 建立了续更衔接和防烂尾机制\\n- ✅ 为创作官脚本质量提升提供了制度保障\\n\\n---\\n\\n*最后更新: 2026-05-17*\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":7696,"output":1001,"cacheRead":65792,"cacheWrite":0,"totalTokens":74489,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778989910413,"responseId":"0217789899122923ad19b9c83995b4974cfe8fe35f4537c6e7afa"}}
{"type":"message","id":"7bb6c39a","parentId":"656cb741","timestamp":"2026-05-17T03:52:41.990Z","message":{"role":"toolResult","toolCallId":"call_ec9sc2u1e2asyy0n93qcqt4z","toolName":"edit","content":[{"type":"text","text":"Validation failed for tool \"edit\":\n  - edits: must have required properties edits\n  - root: must not have additional properties\n\nReceived arguments:\n{\n  \"path\": \"/root/.openclaw/workspace/main/MEMORY.md\",\n  \"oldText\": \"*最后更新: 2026-05-14*\",\n  \"newText\": \"### 2026-05-17: 创作官标准化模板与行为规范v3.0\\n\\n**事件**: 为创作官（Writer Agent）设计标准化创作模板并升级行为规范至v3.0\\n\\n**用户要求**:\\n- 创作官产出的脚本质量不高，需要设计模板\\n- 参考《短视频+长篇慢剧 标准化AI创作模板规范（长期复用版）》\\n- 规范创作官的行为\\n\\n**执行过程**:\\n1. 分析现有Writer规范v2.0和CTO在5月15日做的模板设计\\n2. 发现CTO之前做了一版模板但放在version目录下，未被正式整合到Writer规范中\\n3. 设计8个标准化创作模板文件\\n4. 升级Writer行为规范至v3.0，核心变化：模板驱动+质量门控+AI避坑\\n\\n**创作模板设计成果**:\\n\\n| 模板文件 | 用途 | 关键特点 |\\n|----------|------|----------|\\n| 全局创作规范.md | 所有内容通用 | 4大总则+台词规范+节奏规范+AI避坑清单+4轮18项质量检查 |\\n| 分镜脚本_JSON模板.json | 程序解析用 | 完整JSON Schema，带$ref引用，区分image_prompt/video_prompt |\\n| 短视频_爆款口播感悟类.md | 短视频模板一 | 3秒抓眼→痛点→共情→升华→互动，40-90s |\\n| 短视频_短篇剧情类.md | 短视频模板二 | 开局冲突→情节推进→反转/暖心，60-120s |\\n| 慢剧_核心人设档案.md | 慢剧模板一 | 6个必填字段+立绘提示词+人设一致性检查 |\\n| 慢剧_核心大纲.md | 慢剧模板二 | 三阶段规划(铺垫→递进→收束)+伏笔追踪+避雷设定 |\\n| 慢剧_单集剧本.md | 慢剧模板三 | 慢节奏高质感+情绪拉扯+单集闭环+下集钩子 |\\n| 慢剧_续更衔接.md | 慢剧模板四 | 续更前置检查+固定话术+伏笔追踪表+防烂尾机制 |\\n\\n**行为规范v3.0核心升级**:\\n\\n1. **模板驱动**：每个步骤必须使用对应的标准化模板，杜绝自由发挥导致格式混乱\\n2. **质量门控**：Step 6新增4轮18项硬性检查，不通过必须返工\\n3. **AI避坑机制**：AI套路识别对照表，逐句扫描替换\\n4. **image_prompt必填**：没有image_prompt的镜头视为废镜头\\n5. **续更衔接规范**：每次续更必须做前置检查，解决连载断层和人设跑偏\\n6. **适配模型参数**：短视频Seed 2.0 Pro(温度0.78)，慢剧Kimi+Seed 2.0 Pro(温度0.68)\\n\\n**模板文件位置**:\\n```\\n/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/\\n```\\n\\n**行为规范文件**:\\n```\\n/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/Writer行为规范.md\\n```\\n\\n**经验总结**:\\n1. **模板驱动比自由发挥更可控**：标准化模板确保输出格式统一、字段完整\\n2. **质量门控是底线**：4轮18项检查确保每步输出质量\\n3. **AI避坑是关键差异化**：AI套路识别对照表直接解决\\\"AI味\\\"问题\\n4. **续更衔接是慢剧生命线**：伏笔追踪表+防烂尾机制保证连载质量\\n5. **参考文件价值高**：用户的参考模板提供了成熟的方法论，值得深度整合\\n\\n**影响**:\\n- ✅ 创作官有了8个标准化模板，覆盖短视频和慢剧全场景\\n- ✅ 行为规范从v2.0升级到v3.0，模板驱动+质量门控\\n- ✅ 建立了AI套路识别和替换机制\\n- ✅ 建立了续更衔接和防烂尾机制\\n- ✅ 为创作官脚本质量提升提供了制度保障\\n\\n---\\n\\n*最后更新: 2026-05-17*\"\n}"}],"details":{},"isError":true,"timestamp":1778989961989}}
{"type":"message","id":"88810c03","parentId":"7bb6c39a","timestamp":"2026-05-17T03:53:19.069Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_g2dzylo3j06zcdlqqdvlws92","name":"edit","arguments":{"path":"/root/.openclaw/workspace/main/MEMORY.md","edits":[{"oldText":"*最后更新: 2026-05-14*","newText":"### 2026-05-17: 创作官标准化模板与行为规范v3.0\n\n**事件**: 为创作官（Writer Agent）设计标准化创作模板并升级行为规范至v3.0\n\n**用户要求**: 创作官产出脚本质量不高，需要设计模板并规范行为，参考《短视频+长篇慢剧 标准化AI创作模板规范》\n\n**创作模板设计成果** (8个文件):\n- 全局创作规范.md - 4大总则+台词/节奏/AI避坑+4轮18项质量检查\n- 分镜脚本_JSON模板.json - 完整JSON Schema，区分image_prompt/video_prompt\n- 短视频_爆款口播感悟类.md - 3秒抓眼→痛点→共情→升华→互动，40-90s\n- 短视频_短篇剧情类.md - 开局冲突→情节推进→反转/暖心，60-120s\n- 慢剧_核心人设档案.md - 6个必填字段+立绘提示词+人设一致性检查\n- 慢剧_核心大纲.md - 三阶段规划(铺垫→递进→收束)+伏笔追踪\n- 慢剧_单集剧本.md - 慢节奏高质感+情绪拉扯+单集闭环+下集钩子\n- 慢剧_续更衔接.md - 续更前置检查+伏笔追踪表+防烂尾机制\n\n**行为规范v3.0核心升级**:\n1. 模板驱动：每步必须使用标准化模板\n2. 质量门控：4轮18项硬性检查，不通过必须返工\n3. AI避坑：AI套路识别对照表，逐句扫描替换\n4. image_prompt必填：没有的镜头视为废镜头\n5. 续更衔接：前置检查+伏笔追踪+防烂尾\n6. 适配模型参数：短视频Seed 2.0 Pro(0.78)，慢剧Kimi+Seed 2.0 Pro(0.68)\n\n**文件位置**:\n- 模板: `/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/`\n- 规范: `/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/Writer行为规范.md`\n\n---\n\n*最后更新: 2026-05-17*"}]},"partialArgs":"{\"path\": \"/root/.openclaw/workspace/main/MEMORY.md\", \"edits\": [{\"oldText\": \"*最后更新: 2026-05-14*\", \"newText\": \"### 2026-05-17: 创作官标准化模板与行为规范v3.0\\n\\n**事件**: 为创作官（Writer Agent）设计标准化创作模板并升级行为规范至v3.0\\n\\n**用户要求**: 创作官产出脚本质量不高，需要设计模板并规范行为，参考《短视频+长篇慢剧 标准化AI创作模板规范》\\n\\n**创作模板设计成果** (8个文件):\\n- 全局创作规范.md - 4大总则+台词/节奏/AI避坑+4轮18项质量检查\\n- 分镜脚本_JSON模板.json - 完整JSON Schema，区分image_prompt/video_prompt\\n- 短视频_爆款口播感悟类.md - 3秒抓眼→痛点→共情→升华→互动，40-90s\\n- 短视频_短篇剧情类.md - 开局冲突→情节推进→反转/暖心，60-120s\\n- 慢剧_核心人设档案.md - 6个必填字段+立绘提示词+人设一致性检查\\n- 慢剧_核心大纲.md - 三阶段规划(铺垫→递进→收束)+伏笔追踪\\n- 慢剧_单集剧本.md - 慢节奏高质感+情绪拉扯+单集闭环+下集钩子\\n- 慢剧_续更衔接.md - 续更前置检查+伏笔追踪表+防烂尾机制\\n\\n**行为规范v3.0核心升级**:\\n1. 模板驱动：每步必须使用标准化模板\\n2. 质量门控：4轮18项硬性检查，不通过必须返工\\n3. AI避坑：AI套路识别对照表，逐句扫描替换\\n4. image_prompt必填：没有的镜头视为废镜头\\n5. 续更衔接：前置检查+伏笔追踪+防烂尾\\n6. 适配模型参数：短视频Seed 2.0 Pro(0.78)，慢剧Kimi+Seed 2.0 Pro(0.68)\\n\\n**文件位置**:\\n- 模板: `/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/`\\n- 规范: `/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/Writer行为规范.md`\\n\\n---\\n\\n*最后更新: 2026-05-17*\"}]}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":2097,"output":593,"cacheRead":73472,"cacheWrite":0,"totalTokens":76162,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778989961992,"responseId":"0217789899636130eddebd777620e0417f0fd94e63a8d896f0152"}}
{"type":"message","id":"2f126d4e","parentId":"88810c03","timestamp":"2026-05-17T03:53:19.494Z","message":{"role":"toolResult","toolCallId":"call_g2dzylo3j06zcdlqqdvlws92","toolName":"edit","content":[{"type":"text","text":"Successfully replaced 1 block(s) in /root/.openclaw/workspace/main/MEMORY.md."}],"details":{"diff":"      ...\n 1399 **数据库新路径**: `/root/.openclaw/memory/collector_data.sqlite`\n 1400 \n 1401 ---\n 1402 \n-1403 *最后更新: 2026-05-14*\n+1403 ### 2026-05-17: 创作官标准化模板与行为规范v3.0\n+1404 \n+1405 **事件**: 为创作官（Writer Agent）设计标准化创作模板并升级行为规范至v3.0\n+1406 \n+1407 **用户要求**: 创作官产出脚本质量不高，需要设计模板并规范行为，参考《短视频+长篇慢剧 标准化AI创作模板规范》\n+1408 \n+1409 **创作模板设计成果** (8个文件):\n+1410 - 全局创作规范.md - 4大总则+台词/节奏/AI避坑+4轮18项质量检查\n+1411 - 分镜脚本_JSON模板.json - 完整JSON Schema，区分image_prompt/video_prompt\n+1412 - 短视频_爆款口播感悟类.md - 3秒抓眼→痛点→共情→升华→互动，40-90s\n+1413 - 短视频_短篇剧情类.md - 开局冲突→情节推进→反转/暖心，60-120s\n+1414 - 慢剧_核心人设档案.md - 6个必填字段+立绘提示词+人设一致性检查\n+1415 - 慢剧_核心大纲.md - 三阶段规划(铺垫→递进→收束)+伏笔追踪\n+1416 - 慢剧_单集剧本.md - 慢节奏高质感+情绪拉扯+单集闭环+下集钩子\n+1417 - 慢剧_续更衔接.md - 续更前置检查+伏笔追踪表+防烂尾机制\n+1418 \n+1419 **行为规范v3.0核心升级**:\n+1420 1. 模板驱动：每步必须使用标准化模板\n+1421 2. 质量门控：4轮18项硬性检查，不通过必须返工\n+1422 3. AI避坑：AI套路识别对照表，逐句扫描替换\n+1423 4. image_prompt必填：没有的镜头视为废镜头\n+1424 5. 续更衔接：前置检查+伏笔追踪+防烂尾\n+1425 6. 适配模型参数：短视频Seed 2.0 Pro(0.78)，慢剧Kimi+Seed 2.0 Pro(0.68)\n+1426 \n+1427 **文件位置**:\n+1428 - 模板: `/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/`\n+1429 - 规范: `/root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/Writer行为规范.md`\n+1430 \n+1431 ---\n+1432 \n+1433 *最后更新: 2026-05-17*","firstChangedLine":1403},"isError":false,"timestamp":1778989999493}}
{"type":"message","id":"6ce179b9","parentId":"2f126d4e","timestamp":"2026-05-17T03:53:46.419Z","message":{"role":"assistant","content":[{"type":"text","text":"最后确认一下所有文件都到位了："},{"type":"toolCall","id":"call_jjfaqfphk8s5po7ckorl23df","name":"exec","arguments":{"command":"echo \"=== 创作模板目录 ===\"; ls -la /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/; echo \"\"; echo \"=== Writer行为规范 ===\"; ls -la /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/Writer行为规范.md; echo \"\"; echo \"=== 文件大小统计 ===\"; wc -c /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/*; echo \"\"; wc -c /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/Writer行为规范.md"},"partialArgs":"{\"command\": \"echo \\\"=== 创作模板目录 ===\\\"; ls -la /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/; echo \\\"\\\"; echo \\\"=== Writer行为规范 ===\\\"; ls -la /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/Writer行为规范.md; echo \\\"\\\"; echo \\\"=== 文件大小统计 ===\\\"; wc -c /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/*; echo \\\"\\\"; wc -c /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/Writer行为规范.md\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":668,"output":156,"cacheRead":75520,"cacheWrite":0,"totalTokens":76344,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778989999496,"responseId":"0217789900013330b865ba46ab853a653f94346fa59dc1d632b21"}}
{"type":"message","id":"2b5d6db4","parentId":"6ce179b9","timestamp":"2026-05-17T03:53:47.019Z","message":{"role":"toolResult","toolCallId":"call_jjfaqfphk8s5po7ckorl23df","toolName":"exec","content":[{"type":"text","text":"=== 创作模板目录 ===\ntotal 64\ndrwxr-xr-x 2 root root  4096 May 17 11:48 .\ndrwxr-xr-x 3 root root  4096 May 17 11:40 ..\n-rw-r--r-- 1 root root  8343 May 17 11:48 全局创作规范.md\n-rw-r--r-- 1 root root 11856 May 17 11:47 分镜脚本_JSON模板.json\n-rw-r--r-- 1 root root  3661 May 17 11:44 慢剧_单集剧本.md\n-rw-r--r-- 1 root root  7184 May 17 11:43 慢剧_核心人设档案.md\n-rw-r--r-- 1 root root  5379 May 17 11:44 慢剧_核心大纲.md\n-rw-r--r-- 1 root root  3690 May 17 11:45 慢剧_续更衔接.md\n-rw-r--r-- 1 root root  3501 May 17 11:41 短视频_爆款口播感悟类.md\n-rw-r--r-- 1 root root  3328 May 17 11:41 短视频_短篇剧情类.md\n\n=== Writer行为规范 ===\n-rw-r--r-- 1 root root 18051 May 17 11:51 /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/Writer行为规范.md\n\n=== 文件大小统计 ===\n 8343 /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/全局创作规范.md\n11856 /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/分镜脚本_JSON模板.json\n 3661 /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/慢剧_单集剧本.md\n 7184 /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/慢剧_核心人设档案.md\n 5379 /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/慢剧_核心大纲.md\n 3690 /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/慢剧_续更衔接.md\n 3501 /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/短视频_爆款口播感悟类.md\n 3328 /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/短视频_短篇剧情类.md\n46942 total\n\n18051 /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/Writer行为规范.md"}],"details":{"status":"completed","exitCode":0,"durationMs":185,"aggregated":"=== 创作模板目录 ===\ntotal 64\ndrwxr-xr-x 2 root root  4096 May 17 11:48 .\ndrwxr-xr-x 3 root root  4096 May 17 11:40 ..\n-rw-r--r-- 1 root root  8343 May 17 11:48 全局创作规范.md\n-rw-r--r-- 1 root root 11856 May 17 11:47 分镜脚本_JSON模板.json\n-rw-r--r-- 1 root root  3661 May 17 11:44 慢剧_单集剧本.md\n-rw-r--r-- 1 root root  7184 May 17 11:43 慢剧_核心人设档案.md\n-rw-r--r-- 1 root root  5379 May 17 11:44 慢剧_核心大纲.md\n-rw-r--r-- 1 root root  3690 May 17 11:45 慢剧_续更衔接.md\n-rw-r--r-- 1 root root  3501 May 17 11:41 短视频_爆款口播感悟类.md\n-rw-r--r-- 1 root root  3328 May 17 11:41 短视频_短篇剧情类.md\n\n=== Writer行为规范 ===\n-rw-r--r-- 1 root root 18051 May 17 11:51 /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/Writer行为规范.md\n\n=== 文件大小统计 ===\n 8343 /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/全局创作规范.md\n11856 /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/分镜脚本_JSON模板.json\n 3661 /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/慢剧_单集剧本.md\n 7184 /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/慢剧_核心人设档案.md\n 5379 /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/慢剧_核心大纲.md\n 3690 /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/慢剧_续更衔接.md\n 3501 /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/短视频_爆款口播感悟类.md\n 3328 /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/短视频_短篇剧情类.md\n46942 total\n\n18051 /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/Writer行为规范.md","cwd":"/root/.openclaw/workspace/main"},"isError":false,"timestamp":1778990027019}}
{"type":"message","id":"89e5cde7","parentId":"2b5d6db4","timestamp":"2026-05-17T03:54:09.670Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_pvibv35ptgw2bq62pyrdlkwi","name":"update_plan","arguments":{"plan":[{"step":"分析现有Writer规范和参考模板，识别差距","status":"completed"},{"step":"设计短视频+长篇慢剧标准化创作模板（8个文件）","status":"completed"},{"step":"设计Writer Agent行为规范v3.0（模板驱动+质量门控+AI避坑）","status":"completed"},{"step":"将模板和规范放入指定目录并更新MEMORY.md","status":"completed"}]},"partialArgs":"{\"plan\": [{\"step\": \"分析现有Writer规范和参考模板，识别差距\", \"status\": \"completed\"}, {\"step\": \"设计短视频+长篇慢剧标准化创作模板（8个文件）\", \"status\": \"completed\"}, {\"step\": \"设计Writer Agent行为规范v3.0（模板驱动+质量门控+AI避坑）\", \"status\": \"completed\"}, {\"step\": \"将模板和规范放入指定目录并更新MEMORY.md\", \"status\": \"completed\"}]}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":849,"output":113,"cacheRead":76160,"cacheWrite":0,"totalTokens":77122,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778990027022,"responseId":"0217789900290539e1bacc68b98f823c2af95ea68bff0aeed69ee"}}
{"type":"message","id":"8804613b","parentId":"89e5cde7","timestamp":"2026-05-17T03:54:10.103Z","message":{"role":"toolResult","toolCallId":"call_pvibv35ptgw2bq62pyrdlkwi","toolName":"update_plan","content":[],"details":{"status":"updated","plan":[{"step":"分析现有Writer规范和参考模板，识别差距","status":"completed"},{"step":"设计短视频+长篇慢剧标准化创作模板（8个文件）","status":"completed"},{"step":"设计Writer Agent行为规范v3.0（模板驱动+质量门控+AI避坑）","status":"completed"},{"step":"将模板和规范放入指定目录并更新MEMORY.md","status":"completed"}]},"isError":false,"timestamp":1778990050102}}
{"type":"message","id":"23a2c641","parentId":"8804613b","timestamp":"2026-05-17T03:54:49.163Z","message":{"role":"assistant","content":[{"type":"text","text":"全部完成。以下是交付总结：\n\n---\n\n## ⚡ 创作官模板+规范设计完成\n\n### 8个标准化创作模板\n\n| 模板 | 用途 | 核心特点 |\n|------|------|----------|\n| **全局创作规范.md** | 所有内容通用 | 4大总则、台词/节奏/AI避坑规范、4轮18项质量检查、3条AI固定指令 |\n| **分镜脚本_JSON模板.json** | 程序解析用 | 完整JSON Schema，image_prompt必填、区分image/video_prompt、镜头语言枚举值 |\n| **短视频_爆款口播感悟类.md** | 40-90s口播 | 3秒抓眼→痛点→共情→升华→互动 |\n| **短视频_短篇剧情类.md** | 60-120s剧情 | 开局冲突→情节推进→反转/暖心，含角色关系图 |\n| **慢剧_核心人设档案.md** | 人设（最关键） | 6个必填字段、立绘image_prompt、5种情绪举例句式、人设一致性检查 |\n| **慢剧_核心大纲.md** | 长线连载框架 | 三阶段规划(铺垫→递进→收束)、伏笔追踪、每集钩子设计、避雷设定 |\n| **慢剧_单集剧本.md** | 2-4min单集 | 慢节奏+情绪拉扯+隐性情绪+单集闭环+下集钩子 |\n| **慢剧_续更衔接.md** | 连载保险装置 | 续更前置检查、固定话术、伏笔追踪表、10/50集盘点、防烂尾机制 |\n\n### 行为规范v3.0核心变化\n\n| v2.0 → v3.0 | 变化 |\n|-------------|------|\n| 自由发挥 → **模板驱动** | 每步必须使用对应模板，格式统一 |\n| 无检查 → **质量门控** | 4轮18项硬性检查，不通过必须返工 |\n| 无AI避坑 → **AI套路识别** | 6类AI套路+替换方案，逐句扫描 |\n| image_prompt可选 → **必填** | 没有image_prompt=废镜头 |\n| 无续更规范 → **续更衔接** | 前置检查+伏笔追踪+防烂尾机制 |\n| 无模型参数 → **适配参数** | 短视频Seed 2.0 Pro(0.78)，慢剧Kimi+Seed 2.0 Pro(0.68) |\n\n### 文件位置\n\n```\n创作模板: /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/创作模板/\n行为规范: /root/.openclaw/workspace/规范管理/Agent专用规范/Writer_Agent/Writer行为规范.md\n```"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":14412,"output":618,"cacheRead":62720,"cacheWrite":0,"totalTokens":77750,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1778990050105,"responseId":"021778990052020fcaf69e354b9959077c8dee8e8ff31e0f5b744"}}
{"type":"message","id":"582ef1cf","parentId":"23a2c641","timestamp":"2026-05-17T04:01:00.722Z","message":{"role":"user","content":[{"type":"text","text":"[Sun 2026-05-17 12:00 GMT+8] 帮我修复下 为什么 上面这个任务 ark-code-lastest 可以正常返回，调用Mimo2.5 没有反应？参考MAIN行为规范处理"}],"timestamp":1778990460556}}
{"type":"message","id":"a134eea3","parentId":"582ef1cf","timestamp":"2026-05-17T04:01:17.169Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"用户报告了一个问题：ark-code-latest 模型可以正常返回，但调用 Mimo2.5 没有反应。需要诊断这个问题。\n\n按照Main行为规范，我需要：\n1. 先诊断问题 - 检查系统状态、配置\n2. 分析原因\n3. 提供解决方案\n\n让我先检查OpenClaw的配置，看看模型配置情况。","thinkingSignature":"reasoning_content"},{"type":"text","text":"先查现状，再定位问题。"},{"type":"toolCall","id":"call_0x9yegyrnw9hbmnsp3t3x6mu","name":"session_status","arguments":{},"partialArgs":"}"},{"type":"toolCall","id":"call_h4zyyndimjif5kdpvmj5kw8u","name":"gateway","arguments":{"action":"config.get"},"partialArgs":"{\"action\": \"config.get\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":47706,"output":105,"cacheRead":30592,"cacheWrite":0,"totalTokens":78403,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778990460723,"responseId":"0217789904610843cff19ebb7dfda5cfc47c771b7d56bf2375760"}}
{"type":"message","id":"04acbf00","parentId":"a134eea3","timestamp":"2026-05-17T04:01:34.962Z","message":{"role":"toolResult","toolCallId":"call_0x9yegyrnw9hbmnsp3t3x6mu","toolName":"session_status","content":[{"type":"text","text":"🦞 OpenClaw 2026.5.12 (f066dd2)\n⏱️ Uptime: gateway 2h · system 28d 12h\n🧠 Model: arkcode/ark-code-latest · 🔑 api-key (models.json)\n🧮 Tokens: 407k in / 23k out\n🗄️ Cache: 13% hit · 63k cached, 0 new\n📚 Context: 78k/128k (61%) · 🧹 Compactions: 0\n🧵 Session: agent:main:main • updated 1m ago\n⚙️ Execution: direct · Runtime: OpenClaw Pi Default · Think: off · elevated\n🪢 Queue: steer (depth 0)"}],"details":{"ok":true,"sessionKey":"agent:main:main","changedModel":false,"statusText":"🦞 OpenClaw 2026.5.12 (f066dd2)\n⏱️ Uptime: gateway 2h · system 28d 12h\n🧠 Model: arkcode/ark-code-latest · 🔑 api-key (models.json)\n🧮 Tokens: 407k in / 23k out\n🗄️ Cache: 13% hit · 63k cached, 0 new\n📚 Context: 78k/128k (61%) · 🧹 Compactions: 0\n🧵 Session: agent:main:main • updated 1m ago\n⚙️ Execution: direct · Runtime: OpenClaw Pi Default · Think: off · elevated\n🪢 Queue: steer (depth 0)"},"isError":false,"timestamp":1778990494962}}
{"type":"message","id":"610a159d","parentId":"04acbf00","timestamp":"2026-05-17T04:01:34.964Z","message":{"role":"toolResult","toolCallId":"call_h4zyyndimjif5kdpvmj5kw8u","toolName":"gateway","content":[{"type":"text","text":"{\n  \"ok\": true,\n  \"result\": {\n    \"path\": \"/root/.openclaw/openclaw.json\",\n    \"exists\": true,\n    \"raw\": null,\n    \"parsed\": {\n      \"agents\": {\n        \"defaults\": {\n          \"workspace\": \"/root/.openclaw/workspace\",\n          \"contextTokens\": 300000,\n          \"bootstrapMaxChars\": 50000,\n          \"bootstrapTotalMaxChars\": 200000,\n          \"compaction\": {\n            \"mode\": \"safeguard\",\n            \"reserveTokens\": 50000,\n            \"recentTurnsPreserve\": 10,\n            \"maxHistoryShare\": 0.7,\n            \"notifyUser\": true\n          },\n          \"sandbox\": {\n            \"browser\": {\n              \"enabled\": false\n            }\n          },\n          \"model\": {\n            \"primary\": \"xiaomi-coding/mimo-v2.5\"\n          },\n          \"models\": {\n            \"volcengine-agent-plan/ark-code-latest\": {},\n            \"volcengine-agent-plan/doubao-seed-2.0-code\": {},\n            \"volcengine-agent-plan/doubao-seed-2.0-pro\": {},\n            \"volcengine-agent-plan/doubao-seed-2.0-lite\": {},\n            \"volcengine-agent-plan/doubao-seed-2.0-mini\": {},\n            \"volcengine-agent-plan/glm-5.1\": {},\n            \"volcengine-agent-plan/minimax-2.7\": {},\n            \"volcengine-agent-plan/kimi-k2.6\": {},\n            \"volcengine-agent-plan/deepseek-v3.2\": {},\n            \"xiaomi-coding/mimo-v2.5-pro\": {},\n            \"xiaomi-coding/mimo-v2.5\": {},\n            \"xiaomi-coding/mimo-v2.5-tts-voiceclone\": {},\n            \"xiaomi-coding/mimo-v2.5-tts-voicedesign\": {},\n            \"xiaomi-coding/mimo-v2.5-tts\": {},\n            \"xiaomi-coding/mimo-v2-pro\": {},\n            \"xiaomi-coding/mimo-v2-omni\": {},\n            \"xiaomi-coding/mimo-v2-tts\": {},\n            \"glmcode/GLM-5.1\": {},\n            \"glmcode/GLM-5-Turbo\": {},\n            \"glmcode/glm-5\": {},\n            \"glmcode/glm-4.7\": {},\n            \"glmcode/glm-4.6\": {},\n            \"glmcode/glm-4.5-air\": {},\n            \"glmcode/glm-4.5\": {}\n          }\n        },\n        \"list\": [\n          {\n            \"id\": \"cto\",\n            \"name\": \"CTO\",\n            \"model\": {\n              \"primary\": \"astroncodingplan/astron-code-latest\"\n            }\n          },\n          {\n            \"id\": \"main\",\n            \"name\": \"总控官\",\n            \"model\": {\n              \"primary\": \"arkcode/ark-code-latest\"\n            }\n          },\n          {\n            \"id\": \"collector\",\n            \"name\": \"采集官\",\n            \"model\": {\n              \"primary\": \"xfyun/astron-code-latest\"\n            }\n          },\n          {\n            \"id\": \"writer\",\n            \"name\": \"创作官\",\n            \"model\": {\n              \"primary\": \"xiaomi-coding/mimo-v2.5\"\n            }\n          },\n          {\n            \"id\": \"producer\",\n            \"name\": \"制作官\",\n            \"model\": {\n              \"primary\": \"xfyun/astron-code-latest\"\n            }\n          },\n          {\n            \"id\": \"publisher\",\n            \"name\": \"审核官\",\n            \"model\": {\n              \"primary\": \"xiaomi-coding/mimo-v2.5\"\n            }\n          },\n          {\n            \"id\": \"analyzer\",\n            \"name\": \"分析官\",\n            \"model\": {\n              \"primary\": \"xfyun/astron-code-latest\"\n            }\n          }\n        ]\n      },\n      \"gateway\": {\n        \"mode\": \"local\",\n        \"auth\": {\n          \"mode\": \"token\",\n          \"token\": \"__OPEN…ED__\"\n        },\n        \"port\": 27038,\n        \"bind\": \"lan\",\n        \"tailscale\": {\n          \"mode\": \"off\",\n          \"resetOnExit\": false\n        },\n        \"controlUi\": {\n          \"allowedOrigins\": [\n            \"http://49.232.222.110:27038\"\n          ],\n          \"allowInsecureAuth\": true,\n          \"dangerouslyDisableDeviceAuth\": true,\n          \"basePath\": \"/uy475k\"\n        },\n        \"http\": {\n          \"endpoints\": {\n            \"chatCompletions\": {\n              \"enabled\": true\n            }\n          }\n        }\n      },\n      \"session\": {\n        \"dmScope\": \"per-channel-peer\"\n      },\n      \"tools\": {\n        \"profile\": \"full\",\n        \"sessions\": {\n          \"visibility\": \"all\"\n        },\n        \"agentToAgent\": {\n          \"enabled\": true\n        },\n        \"deny\": []\n      },\n      \"skills\": {\n        \"install\": {\n          \"nodeManager\": \"npm\"\n        },\n        \"entries\": {\n          \"scheduler\": {\n            \"enabled\": true\n          }\n        }\n      },\n      \"wizard\": {\n        \"lastRunAt\": \"2026-05-13T23:43:40.666Z\",\n        \"lastRunVersion\": \"2026.5.7\",\n        \"lastRunCommand\": \"doctor\",\n        \"lastRunMode\": \"local\"\n      },\n      \"meta\": {\n        \"lastTouchedVersion\": \"2026.5.12\",\n        \"lastTouchedAt\": \"2026-05-15T10:46:40.976Z\"\n      },\n      \"browser\": {\n        \"enabled\": true,\n        \"executablePath\": \"/root/.cache/ms-playwright/chromium-1208/chrome-linux64/chrome\",\n        \"noSandbox\": true,\n        \"defaultProfile\": \"user\",\n        \"profiles\": {\n          \"user\": {\n            \"cdpUrl\": \"http://localhost:9222\",\n            \"driver\": \"existing-session\",\n            \"attachOnly\": true,\n            \"color\": \"#4285F4\",\n            \"userDataDir\": \"/root/.openclaw/browser-existing-session\"\n          }\n        },\n        \"ssrfPolicy\": {\n          \"dangerouslyAllowPrivateNetwork\": true\n        }\n      },\n      \"models\": {\n        \"providers\": {\n          \"volcengine-agent-plan\": {\n            \"baseUrl\": \"https://ark.cn-beijing.volces.com/api/plan/v3\",\n            \"apiKey\": \"__OPEN…ED__\",\n            \"api\": \"openai-completions\",\n            \"models\": [\n              {\n                \"id\": \"ark-code-latest\",\n                \"name\": \"ark-code-latest\",\n                \"contextWindow\": 256000,\n                \"maxTokens\": 32000,\n                \"input\": [\n                  \"text\",\n                  \"image\"\n                ]\n              },\n              {\n                \"id\": \"deepseek-v3.2\",\n                \"name\": \"deepseek-v3.2\",\n                \"contextWindow\": 128000,\n                \"maxTokens\": 32000,\n                \"input\": [\n                  \"text\"\n                ]\n              }\n            ]\n          },\n          \"arkcode\": {\n            \"baseUrl\": \"https://ark.cn-beijing.volces.com/api/coding/v3\",\n            \"apiKey\": \"__OPEN…ED__\",\n            \"api\": \"openai-completions\",\n            \"models\": [\n              {\n                \"id\": \"ark-code-latest\",\n                \"name\": \"ARK Code Latest\",\n                \"input\": [\n                  \"text\",\n                  \"image\"\n                ],\n                \"maxTokens\": 8192,\n                \"contextWindow\": 128000\n              }\n            ]\n          },\n          \"astroncodingplan\": {\n            \"baseUrl\": \"https://maas-coding-api.cn-huabei-1.xf-yun.com/v2\",\n            \"apiKey\": \"__OPEN…ED__\",\n            \"api\": \"openai-completions\",\n            \"models\": [\n              {\n                \"id\": \"astron-code-latest\",\n                \"name\": \"astron-deepseek-4.2\",\n                \"reasoning\": false,\n                \"input\": [\n                  \"text\"\n                ],\n                \"contextWindow\": 92160,\n                \"maxTokens\": 32768\n              }\n            ]\n          },\n          \"siliconflow\": {\n            \"baseUrl\": \"https://api.siliconflow.cn/v1\",\n            \"apiKey\": \"__OPEN…ED__\",\n            \"api\": \"openai-completions\",\n            \"models\": [\n              {\n                \"id\": \"Qwen/Qwen3-8B\",\n                \"name\": \"Qwen3-8B (Free)\",\n                \"compat\": {\n                  \"supportsStore\": false\n                },\n                \"maxTokens\": 4096,\n                \"contextWindow\": 32000\n              },\n              {\n                \"id\": \"THUDM/GLM-4.1V-9B-Thinking\",\n                \"name\": \"GLM-4.1V-9B-Thinking (Free)\",\n                \"compat\": {\n                  \"supportsStore\": false\n                },\n                \"maxTokens\": 4096,\n                \"contextWindow\": 32000\n              }\n            ]\n          },\n          \"xfyun\": {\n            \"baseUrl\": \"https://maas-coding-api.cn-huabei-1.xf-yun.com/v2\",\n            \"apiKey\": \"__OPEN…ED__\",\n            \"api\": \"openai-completions\",\n            \"models\": [\n              {\n                \"id\": \"astron-code-latest\",\n                \"name\": \"Qwen3.5-35B-A3B (讯飞星辰)\",\n                \"input\": [\n                  \"text\"\n                ],\n                \"contextWindow\": 32768,\n                \"maxTokens\": 8192,\n                \"reasoning\": false,\n                \"cost\": {\n                  \"input\": 0,\n                  \"output\": 0,\n                  \"cacheRead\": 0,\n                  \"cacheWrite\": 0\n                },\n                \"api\": \"openai-completions\"\n              }\n            ]\n          },\n          \"xiaomi-coding\": {\n            \"baseUrl\": \"https://token-plan-cn.xiaomimimo.com/v1\",\n            \"apiKey\": \"__OPEN…ED__\",\n            \"api\": \"openai-completions\",\n            \"models\": [\n              {\n                \"id\": \"mimo-v2.5-pro\",\n                \"name\": \"MiMo-V2.5-Pro\",\n                \"reasoning\": true,\n                \"input\": [\n                  \"text\"\n                ],\n                \"contextWindow\": 1048576,\n                \"maxTokens\": 32000,\n                \"compat\": {\n                  \"supportsStore\": false,\n                  \"supportsUsageInStreaming\": true,\n                  \"supportsReasoningEffort\": true,\n                  \"supportsDeveloperRole\": false,\n                  \"maxTokensField\": \"max_tokens\",\n                  \"thinkingFormat\": \"deepseek\",\n                  \"supportsStrictMode\": false,\n                  \"unsupportedToolSchemaKeywords\": [\n                    \"additionalProperties\",\n                    \"oneOf\",\n                    \"anyOf\",\n                    \"allOf\",\n                    \"$ref\"\n                  ]\n                }\n              },\n              {\n                \"id\": \"mimo-v2.5\",\n                \"name\": \"MiMo-V2.5\",\n                \"reasoning\": true,\n                \"input\": [\n                  \"text\",\n                  \"image\"\n                ],\n                \"contextWindow\": 262144,\n                \"maxTokens\": 32000,\n                \"compat\": {\n                  \"supportsStore\": false,\n                  \"supportsUsageInStreaming\": true,\n                  \"supportsReasoningEffort\": true,\n                  \"supportsDeveloperRole\": false,\n                  \"maxTokensField\": \"max_tokens\",\n                  \"thinkingFormat\": \"deepseek\",\n                  \"supportsStrictMode\": false,\n                  \"unsupportedToolSchemaKeywords\": [\n                    \"additionalProperties\",\n                    \"oneOf\",\n                    \"anyOf\",\n                    \"allOf\",\n                    \"$ref\"\n                  ]\n                }\n              },\n              {\n                \"id\": \"mimo-v2.5-tts-voiceclone\",\n                \"name\": \"MiMo-V2.5-TTS-VoiceClone\",\n                \"reasoning\": true,\n                \"input\": [\n                  \"text\"\n                ],\n                \"contextWindow\": 262144,\n                \"maxTokens\": 32000,\n                \"compat\": {\n                  \"supportsStore\": false,\n                  \"supportsUsageInStreaming\": true,\n                  \"supportsDeveloperRole\": false,\n                  \"maxTokensField\": \"max_tokens\",\n                  \"supportsTools\": false\n                }\n              },\n              {\n                \"id\": \"mimo-v2.5-tts-voicedesign\",\n                \"name\": \"MiMo-V2.5-TTS-VoiceDesign\",\n                \"reasoning\": true,\n                \"input\": [\n                  \"text\"\n                ],\n                \"contextWindow\": 262144,\n                \"maxTokens\": 32000,\n                \"compat\": {\n                  \"supportsStore\": false,\n                  \"supportsUsageInStreaming\": true,\n                  \"supportsDeveloperRole\": false,\n                  \"maxTokensField\": \"max_tokens\",\n                  \"supportsTools\": false\n                }\n              },\n\n⚠️ [... middle content omitted — showing head and tail ...]\n\n      },\n      \"channels\": {\n        \"openclaw-weixin\": {\n          \"accounts\": {},\n          \"channelConfigUpdatedAt\": \"2026-04-16T14:16:06.272Z\"\n        }\n      },\n      \"gateway\": {\n        \"port\": 27038,\n        \"mode\": \"local\",\n        \"bind\": \"lan\",\n        \"controlUi\": {\n          \"basePath\": \"/uy475k\",\n          \"allowedOrigins\": [\n            \"http://49.232.222.110:27038\"\n          ],\n          \"allowInsecureAuth\": true,\n          \"dangerouslyDisableDeviceAuth\": true\n        },\n        \"auth\": {\n          \"mode\": \"token\",\n          \"token\": \"__OPEN…ED__\"\n        },\n        \"tailscale\": {\n          \"mode\": \"off\",\n          \"resetOnExit\": false\n        },\n        \"http\": {\n          \"endpoints\": {\n            \"chatCompletions\": {\n              \"enabled\": true\n            }\n          }\n        }\n      },\n      \"mcp\": {\n        \"servers\": {}\n      },\n      \"skills\": {\n        \"install\": {\n          \"nodeManager\": \"npm\"\n        },\n        \"entries\": {\n          \"scheduler\": {\n            \"enabled\": true\n          }\n        }\n      },\n      \"plugins\": {\n        \"allow\": [\n          \"browser\",\n          \"volcengine\",\n          \"openclaw-weixin\"\n        ],\n        \"entries\": {\n          \"browser\": {\n            \"enabled\": true,\n            \"config\": {}\n          },\n          \"volcengine\": {\n            \"enabled\": true,\n            \"config\": {}\n          },\n          \"openclaw-weixin\": {\n            \"enabled\": true,\n            \"config\": {}\n          },\n          \"memory-core\": {\n            \"config\": {}\n          }\n        }\n      }\n    },\n    \"hash\": \"2a037cb44b0f4f0c3009094c5d8341c06c986e903499c0a711680482f4b63924\",\n    \"issues\": [],\n    \"warnings\": [\n      {\n        \"path\": \"plugins.entries.adp-openclaw\",\n        \"message\": \"plugin adp-openclaw: installed plugin package requires compiled runtime output for TypeScript entry index.ts: expected ./dist/index.js, ./dist/index.mjs, ./dist/index.cjs, index.js, index.mjs, index.cjs. This is a plugin packaging issue, not a local config problem; update or reinstall the plugin after the publisher ships compiled JavaScript, or disable/uninstall the plugin until then. TypeScript source fallback is only supported for source checkouts and local development paths.\"\n      },\n      {\n        \"path\": \"plugins.entries.ddingtalk\",\n        \"message\": \"plugin ddingtalk: installed plugin package requires compiled runtime output for TypeScript entry index.ts: expected ./dist/index.js, ./dist/index.mjs, ./dist/index.cjs, index.js, index.mjs, index.cjs. This is a plugin packaging issue, not a local config problem; update or reinstall the plugin after the publisher ships compiled JavaScript, or disable/uninstall the plugin until then. TypeScript source fallback is only supported for source checkouts and local development paths.\"\n      },\n      {\n        \"path\": \"plugins.entries.memory-tencentdb\",\n        \"message\": \"plugin memory-tencentdb: installed plugin package requires compiled runtime output for TypeScript entry index.ts: expected ./dist/index.js, ./dist/index.mjs, ./dist/index.cjs, index.js, index.mjs, index.cjs. This is a plugin packaging issue, not a local config problem; update or reinstall the plugin after the publisher ships compiled JavaScript, or disable/uninstall the plugin until then. TypeScript source fallback is only supported for source checkouts and local development paths.\"\n      },\n      {\n        \"path\": \"plugins.entries.wecom\",\n        \"message\": \"plugin wecom: installed plugin package requires compiled runtime output for TypeScript entry index.ts: expected ./dist/index.js, ./dist/index.mjs, ./dist/index.cjs, index.js, index.mjs, index.cjs. This is a plugin packaging issue, not a local config problem; update or reinstall the plugin after the publisher ships compiled JavaScript, or disable/uninstall the plugin until then. TypeScript source fallback is only supported for source checkouts and local development paths.\"\n      }\n    ],\n    \"legacyIssues\": []\n  }\n}[... 78157 more characters truncated]"}],"details":{"persistedDetailsTruncated":true,"originalDetailsBytesAtLeast":8193,"originalDetailKeys":["ok","result"]},"isError":false,"timestamp":1778990494962}}
{"type":"message","id":"01681efc","parentId":"610a159d","timestamp":"2026-05-17T04:02:25.799Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"I found the key issue. Let me analyze the MiMo v2.5 configuration:\n\nThe MiMo v2.5 model has `unsupportedToolSchemaKeywords` that includes `\"$ref\"`, `\"additionalProperties\"`, `\"oneOf\"`, `\"anyOf\"`, `\"allOf\"`. This is a critical compatibility issue.\n\nWhen OpenClaw sends tool/function definitions to MiMo v2.5, if the tool schemas contain any of these JSON Schema keywords, MiMo will reject or fail to process them. The OpenClaw tools likely use `$ref` and `additionalProperties` extensively in their schemas.\n\nAdditionally, MiMo v2.5 has `supportsDeveloperRole: false` and `supportsStrictMode: false`, and `maxTokensField: \"max_tokens\"` (instead of the default).\n\nLet me also check the gateway logs for actual errors when calling MiMo v2.5.\n\nThe key differences between ark-code-latest (which works) and mimo-v2.5 (which doesn't):\n\n1. **arkcode/ark-code-latest**: \n   - `api: \"openai-completions\"`\n   - `reasoning: false` (not set, defaults to false)\n   - No `compat` restrictions\n   - `maxTokens: 8192`\n   - `contextWindow: 128000`\n\n2. **xiaomi-coding/mimo-v2.5**:\n   - `api: \"openai-completions\"`\n   - `reasoning: true`\n   - `compat.unsupportedToolSchemaKeywords`: [\"additionalProperties\", \"oneOf\", \"anyOf\", \"allOf\", \"$ref\"]\n   - `compat.supportsDeveloperRole: false`\n   - `compat.maxTokensField: \"max_tokens\"`\n   - `compat.thinkingFormat: \"deepseek\"`\n   - `maxTokens: 32000`\n   - `contextWindow: 262144`\n\nThe `unsupportedToolSchemaKeywords` including `$ref` and `additionalProperties` is likely the main problem. OpenClaw's tool schemas use these keywords extensively. When OpenClaw sends these to MiMo, the API likely rejects or fails silently.\n\nLet me check the actual error logs.","thinkingSignature":"reasoning_content"},{"type":"text","text":"关键发现：MiMo v2.5的compat配置有 `unsupportedToolSchemaKeywords` 限制。查日志确认具体错误。"},{"type":"toolCall","id":"call_pmaf8bfhh6zi7j5622vwnx10","name":"exec","arguments":{"command":"journalctl --user -u openclaw-gateway --since \"30 min ago\" --no-pager 2>/dev/null | grep -i -E \"mimo|xiaomi|error|fail|reject|unsupported|schema|tool\" | tail -40"},"partialArgs":"{\"command\": \"journalctl --user -u openclaw-gateway --since \\\"30 min ago\\\" --no-pager 2>/dev/null | grep -i -E \\\"mimo|xiaomi|error|fail|reject|unsupported|schema|tool\\\" | tail -40\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":91915,"output":548,"cacheRead":1408,"cacheWrite":0,"totalTokens":93871,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778990494968,"responseId":"02177899049501237f745830b35ec47d4e197c4b3b29d259f306c"}}
{"type":"message","id":"32a2209a","parentId":"01681efc","timestamp":"2026-05-17T04:02:27.087Z","message":{"role":"toolResult","toolCallId":"call_pmaf8bfhh6zi7j5622vwnx10","toolName":"exec","content":[{"type":"text","text":"May 17 11:36:54 VM-0-12-ubuntu node[2456037]: 2026-05-17T11:36:54.838+08:00 [ws] closed before connect conn=1556d139-5316-4f18-aba9-51f0d97a6af4 peer=115.57.164.58:52116->10.2.0.12:27038 remote=115.57.164.58 fwd=n/a origin=http://49.232.222.110:27038 host=49.232.222.110:27038 ua=Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:150.0) Gecko/20100101 Firefox/150.0 code=4008 reason=connect failed\nMay 17 11:38:40 VM-0-12-ubuntu node[2456037]: 2026-05-17T11:38:40.282+08:00 [tools] read failed: EISDIR: illegal operation on a directory, read raw_params={\"path\":\"/root/.openclaw/workspace/规范管理/Agent专用规范\"}\nMay 17 11:39:04 VM-0-12-ubuntu node[2456037]: 2026-05-17T11:39:04.142+08:00 [tools] edit failed: Could not find the exact text in /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/static/user_driven_coordinator.js. The old text must match exactly including all whitespace and newlines.\nMay 17 11:42:27 VM-0-12-ubuntu node[2456037]: 2026-05-17T11:42:27.633+08:00 [diagnostic] liveness warning: reasons=event_loop_delay interval=30s eventLoopDelayP99Ms=21.6 eventLoopDelayMaxMs=1214.3 eventLoopUtilization=0.113 cpuCoreRatio=0.12 active=2 waiting=0 queued=2 phase=channels.openclaw-weixin.start-account recentPhases=sidecars.main-session-recovery:4ms,sidecars.restart-sentinel:63ms,post-attach.update-sentinel:50ms,sidecars.session-locks:1358ms,post-ready.maintenance:80ms,sidecars.model-prewarm:3867ms work=[active=agent:cto:main(processing/tool_call,q=1,age=538s last=tool:browser:started)|agent:main:main(processing/model_call,q=1,age=251s last=model_call:started) queued=agent:cto:main(processing/tool_call,q=1,age=538s last=tool:browser:started)|agent:main:main(processing/model_call,q=1,age=251s last=model_call:started)]\nMay 17 11:42:32 VM-0-12-ubuntu node[2456037]: 2026-05-17T11:42:32.761+08:00 [tools] browser failed: timed out. The browser profile is external to OpenClaw; make sure its browser/CDP endpoint is running and reachable. Restarting the OpenClaw gateway will not launch it. raw_params={\"action\":\"navigate\",\"url\":\"http://www.lzycloud.cloud:8000/static/user_driven_coordinator_final.html\"}\nMay 17 11:56:57 VM-0-12-ubuntu node[2456037]: 2026-05-17T11:56:57.840+08:00 [diagnostic] long-running session: sessionId=aa89ef19-c553-4497-bc3b-6f95839d208a sessionKey=agent:cto:main state=processing age=289s queueDepth=1 reason=queued_behind_active_work classification=long_running activeWorkKind=tool_call lastProgress=tool:exec:started lastProgressAge=9s activeTool=exec activeToolCallId=call_1e13a21c7114407b89c4dcb3 activeToolAge=9s recovery=none\nMay 17 12:02:06 VM-0-12-ubuntu node[2456037]: 2026-05-17T12:02:06.718+08:00 [agent/embedded] embedded run agent end: runId=57a505b3-d7be-47c4-b596-d62b3ba55ad8 isError=true model=astron-code-latest provider=astroncodingplan error=Xunfei request failed with Sid: cht000b8ad2@dx19e34175b9aba5a700 code: 10010, msg: RecvFromEngineError:Engine Busy, timeStamp:12:02:06.663 rawError=Xunfei request failed with Sid: cht000b8ad2@dx19e34175b9aba5a700 code: 10010, msg: RecvFromEngineError:Engine Busy, timeStamp:12:02:06.663"}],"details":{"status":"completed","exitCode":0,"durationMs":465,"aggregated":"May 17 11:36:54 VM-0-12-ubuntu node[2456037]: 2026-05-17T11:36:54.838+08:00 [ws] closed before connect conn=1556d139-5316-4f18-aba9-51f0d97a6af4 peer=115.57.164.58:52116->10.2.0.12:27038 remote=115.57.164.58 fwd=n/a origin=http://49.232.222.110:27038 host=49.232.222.110:27038 ua=Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:150.0) Gecko/20100101 Firefox/150.0 code=4008 reason=connect failed\nMay 17 11:38:40 VM-0-12-ubuntu node[2456037]: 2026-05-17T11:38:40.282+08:00 [tools] read failed: EISDIR: illegal operation on a directory, read raw_params={\"path\":\"/root/.openclaw/workspace/规范管理/Agent专用规范\"}\nMay 17 11:39:04 VM-0-12-ubuntu node[2456037]: 2026-05-17T11:39:04.142+08:00 [tools] edit failed: Could not find the exact text in /root/.openclaw/workspace/versions/user_driven_final_v3.0.1/manhua_studio_web_8000/static/user_driven_coordinator.js. The old text must match exactly including all whitespace and newlines.\nMay 17 11:42:27 VM-0-12-ubuntu node[2456037]: 2026-05-17T11:42:27.633+08:00 [diagnostic] liveness warning: reasons=event_loop_delay interval=30s eventLoopDelayP99Ms=21.6 eventLoopDelayMaxMs=1214.3 eventLoopUtilization=0.113 cpuCoreRatio=0.12 active=2 waiting=0 queued=2 phase=channels.openclaw-weixin.start-account recentPhases=sidecars.main-session-recovery:4ms,sidecars.restart-sentinel:63ms,post-attach.update-sentinel:50ms,sidecars.session-locks:1358ms,post-ready.maintenance:80ms,sidecars.model-prewarm:3867ms work=[active=agent:cto:main(processing/tool_call,q=1,age=538s last=tool:browser:started)|agent:main:main(processing/model_call,q=1,age=251s last=model_call:started) queued=agent:cto:main(processing/tool_call,q=1,age=538s last=tool:browser:started)|agent:main:main(processing/model_call,q=1,age=251s last=model_call:started)]\nMay 17 11:42:32 VM-0-12-ubuntu node[2456037]: 2026-05-17T11:42:32.761+08:00 [tools] browser failed: timed out. The browser profile is external to OpenClaw; make sure its browser/CDP endpoint is running and reachable. Restarting the OpenClaw gateway will not launch it. raw_params={\"action\":\"navigate\",\"url\":\"http://www.lzycloud.cloud:8000/static/user_driven_coordinator_final.html\"}\nMay 17 11:56:57 VM-0-12-ubuntu node[2456037]: 2026-05-17T11:56:57.840+08:00 [diagnostic] long-running session: sessionId=aa89ef19-c553-4497-bc3b-6f95839d208a sessionKey=agent:cto:main state=processing age=289s queueDepth=1 reason=queued_behind_active_work classification=long_running activeWorkKind=tool_call lastProgress=tool:exec:started lastProgressAge=9s activeTool=exec activeToolCallId=call_1e13a21c7114407b89c4dcb3 activeToolAge=9s recovery=none\nMay 17 12:02:06 VM-0-12-ubuntu node[2456037]: 2026-05-17T12:02:06.718+08:00 [agent/embedded] embedded run agent end: runId=57a505b3-d7be-47c4-b596-d62b3ba55ad8 isError=true model=astron-code-latest provider=astroncodingplan error=Xunfei request failed with Sid: cht000b8ad2@dx19e34175b9aba5a700 code: 10010, msg: RecvFromEngineError:Engine Busy, timeStamp:12:02:06.663 rawError=Xunfei request failed with Sid: cht000b8ad2@dx19e34175b9aba5a700 code: 10010, msg: RecvFromEngineError:Engine Busy, timeStamp:12:02:06.663","cwd":"/root/.openclaw/workspace/main"},"isError":false,"timestamp":1778990547087}}
{"type":"message","id":"364a7831","parentId":"32a2209a","timestamp":"2026-05-17T04:03:02.546Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_x8wvvp3hrqugvo86an0est5m","name":"exec","arguments":{"command":"# 查找mimo相关的错误日志 - 更大范围搜索\njournalctl --user -u openclaw-gateway --since \"2 hours ago\" --no-pager 2>/dev/null | grep -i \"mimo\" | tail -30\necho \"=== xiaomi ===\"\njournalctl --user -u openclaw-gateway --since \"2 hours ago\" --no-pager 2>/dev/null | grep -i \"xiaomi\" | tail -30\necho \"=== writer agent ===\"\njournalctl --user -u openclaw-gateway --since \"2 hours ago\" --no-pager 2>/dev/null | grep -i \"writer\" | tail -30"},"partialArgs":"{\"command\": \"# 查找mimo相关的错误日志 - 更大范围搜索\\njournalctl --user -u openclaw-gateway --since \\\"2 hours ago\\\" --no-pager 2>/dev/null | grep -i \\\"mimo\\\" | tail -30\\necho \\\"=== xiaomi ===\\\"\\njournalctl --user -u openclaw-gateway --since \\\"2 hours ago\\\" --no-pager 2>/dev/null | grep -i \\\"xiaomi\\\" | tail -30\\necho \\\"=== writer agent ===\\\"\\njournalctl --user -u openclaw-gateway --since \\\"2 hours ago\\\" --no-pager 2>/dev/null | grep -i \\\"writer\\\" | tail -30\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":88546,"output":156,"cacheRead":6016,"cacheWrite":0,"totalTokens":94718,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778990547091,"responseId":"021778990547136f4c9d65db78e575a9b8a2c83caa1c4bcc0232d"}}
{"type":"message","id":"b197710d","parentId":"364a7831","timestamp":"2026-05-17T04:03:03.167Z","message":{"role":"toolResult","toolCallId":"call_x8wvvp3hrqugvo86an0est5m","toolName":"exec","content":[{"type":"text","text":"=== xiaomi ===\n=== writer agent ==="}],"details":{"status":"completed","exitCode":0,"durationMs":196,"aggregated":"=== xiaomi ===\n=== writer agent ===","cwd":"/root/.openclaw/workspace/main"},"isError":false,"timestamp":1778990583167}}
{"type":"message","id":"f8cda565","parentId":"b197710d","timestamp":"2026-05-17T04:03:10.858Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_45fd4fal3fzinzjmhd0ajj2g","name":"exec","arguments":{"command":"# 搜索更多关于mimo/v2.5的日志\njournalctl --user -u openclaw-gateway --since \"2 hours ago\" --no-pager 2>/dev/null | grep -i -E \"mimo|xiaomi-coding|token-plan\" | tail -30\necho \"=== all errors ===\"\njournalctl --user -u openclaw-gateway --since \"2 hours ago\" --no-pager 2>/dev/null | grep -i \"error\" | tail -30\necho \"=== tool schema ===\"\njournalctl --user -u openclaw-gateway --since \"2 hours ago\" --no-pager 2>/dev/null | grep -i -E \"schema|additional|oneOf|\\$ref\" | tail -20"},"partialArgs":"{\"command\": \"# 搜索更多关于mimo/v2.5的日志\\njournalctl --user -u openclaw-gateway --since \\\"2 hours ago\\\" --no-pager 2>/dev/null | grep -i -E \\\"mimo|xiaomi-coding|token-plan\\\" | tail -30\\necho \\\"=== all errors ===\\\"\\njournalctl --user -u openclaw-gateway --since \\\"2 hours ago\\\" --no-pager 2>/dev/null | grep -i \\\"error\\\" | tail -30\\necho \\\"=== tool schema ===\\\"\\njournalctl --user -u openclaw-gateway --since \\\"2 hours ago\\\" --no-pager 2>/dev/null | grep -i -E \\\"schema|additional|oneOf|\\\\$ref\\\" | tail -20\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":204,"output":173,"cacheRead":94528,"cacheWrite":0,"totalTokens":94905,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778990583172,"responseId":"02177899058321360aca928cac1d578fd0339d475d6e255ecacdb"}}
{"type":"message","id":"c428058c","parentId":"f8cda565","timestamp":"2026-05-17T04:03:11.472Z","message":{"role":"toolResult","toolCallId":"call_45fd4fal3fzinzjmhd0ajj2g","toolName":"exec","content":[{"type":"text","text":"=== all errors ===\nMay 17 10:48:46 VM-0-12-ubuntu node[2456037]: 2026-05-17T10:48:46.822+08:00 [agent/embedded] [context-overflow-diag] sessionKey=agent:cto:main provider=glmcode/GLM-5.1 source=assistantError messages=320 sessionFile=/root/.openclaw/agents/cto/sessions/aa89ef19-c553-4497-bc3b-6f95839d208a.jsonl diagId=ovf-mp96eoki-ZqSXTg compactionAttempts=0 observedTokens=unknown error=Context overflow: estimated context size exceeds safe threshold during tool loop.\nMay 17 12:02:06 VM-0-12-ubuntu node[2456037]: 2026-05-17T12:02:06.718+08:00 [agent/embedded] embedded run agent end: runId=57a505b3-d7be-47c4-b596-d62b3ba55ad8 isError=true model=astron-code-latest provider=astroncodingplan error=Xunfei request failed with Sid: cht000b8ad2@dx19e34175b9aba5a700 code: 10010, msg: RecvFromEngineError:Engine Busy, timeStamp:12:02:06.663 rawError=Xunfei request failed with Sid: cht000b8ad2@dx19e34175b9aba5a700 code: 10010, msg: RecvFromEngineError:Engine Busy, timeStamp:12:02:06.663\n=== tool schema ==="}],"details":{"status":"completed","exitCode":0,"durationMs":203,"aggregated":"=== all errors ===\nMay 17 10:48:46 VM-0-12-ubuntu node[2456037]: 2026-05-17T10:48:46.822+08:00 [agent/embedded] [context-overflow-diag] sessionKey=agent:cto:main provider=glmcode/GLM-5.1 source=assistantError messages=320 sessionFile=/root/.openclaw/agents/cto/sessions/aa89ef19-c553-4497-bc3b-6f95839d208a.jsonl diagId=ovf-mp96eoki-ZqSXTg compactionAttempts=0 observedTokens=unknown error=Context overflow: estimated context size exceeds safe threshold during tool loop.\nMay 17 12:02:06 VM-0-12-ubuntu node[2456037]: 2026-05-17T12:02:06.718+08:00 [agent/embedded] embedded run agent end: runId=57a505b3-d7be-47c4-b596-d62b3ba55ad8 isError=true model=astron-code-latest provider=astroncodingplan error=Xunfei request failed with Sid: cht000b8ad2@dx19e34175b9aba5a700 code: 10010, msg: RecvFromEngineError:Engine Busy, timeStamp:12:02:06.663 rawError=Xunfei request failed with Sid: cht000b8ad2@dx19e34175b9aba5a700 code: 10010, msg: RecvFromEngineError:Engine Busy, timeStamp:12:02:06.663\n=== tool schema ===","cwd":"/root/.openclaw/workspace/main"},"isError":false,"timestamp":1778990591471}}
{"type":"message","id":"87992446","parentId":"c428058c","timestamp":"2026-05-17T04:03:28.191Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_ou4i391om3c452bq8cxzup9j","name":"exec","arguments":{"command":"# 搜索更广泛的日志 - 查看writer/publisher agent的session\njournalctl --user -u openclaw-gateway --since \"24 hours ago\" --no-pager 2>/dev/null | grep -i -E \"mimo|xiaomi|token-plan\" | tail -30\necho \"=== agent:writer ===\"\njournalctl --user -u openclaw-gateway --since \"24 hours ago\" --no-pager 2>/dev/null | grep \"agent:writer\" | tail -20\necho \"=== agent:publisher ===\"\njournalctl --user -u openclaw-gateway --since \"24 hours ago\" --no-pager 2>/dev/null | grep \"agent:publisher\" | tail -20"},"partialArgs":"{\"command\": \"# 搜索更广泛的日志 - 查看writer/publisher agent的session\\njournalctl --user -u openclaw-gateway --since \\\"24 hours ago\\\" --no-pager 2>/dev/null | grep -i -E \\\"mimo|xiaomi|token-plan\\\" | tail -30\\necho \\\"=== agent:writer ===\\\"\\njournalctl --user -u openclaw-gateway --since \\\"24 hours ago\\\" --no-pager 2>/dev/null | grep \\\"agent:writer\\\" | tail -20\\necho \\\"=== agent:publisher ===\\\"\\njournalctl --user -u openclaw-gateway --since \\\"24 hours ago\\\" --no-pager 2>/dev/null | grep \\\"agent:publisher\\\" | tail -20\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":93884,"output":166,"cacheRead":1408,"cacheWrite":0,"totalTokens":95458,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778990591475,"responseId":"021778990591517454c0d8025bd4547674d07aeb10e65a312a59b"}}
{"type":"message","id":"31ab1446","parentId":"87992446","timestamp":"2026-05-17T04:03:28.878Z","message":{"role":"toolResult","toolCallId":"call_ou4i391om3c452bq8cxzup9j","toolName":"exec","content":[{"type":"text","text":"May 16 13:12:37 VM-0-12-ubuntu node[2122957]: 2026-05-16T13:12:37.092+08:00 [gateway] agent model: xiaomi-coding/mimo-v2.5 (thinking=medium, fast=off)\nMay 17 09:37:08 VM-0-12-ubuntu node[2444100]: 2026-05-17T09:37:08.712+08:00 [gateway] agent model: xiaomi-coding/mimo-v2.5 (thinking=medium, fast=off)\nMay 17 09:43:02 VM-0-12-ubuntu node[2446468]: 2026-05-17T09:43:02.345+08:00 [gateway] agent model: xiaomi-coding/mimo-v2.5 (thinking=medium, fast=off)\nMay 17 09:51:35 VM-0-12-ubuntu node[2450581]: 2026-05-17T09:51:35.005+08:00 [gateway] agent model: xiaomi-coding/mimo-v2.5 (thinking=medium, fast=off)\nMay 17 09:58:20 VM-0-12-ubuntu node[2454376]: 2026-05-17T09:58:20.069+08:00 [gateway] agent model: xiaomi-coding/mimo-v2.5 (thinking=medium, fast=off)\nMay 17 10:01:11 VM-0-12-ubuntu node[2456037]: 2026-05-17T10:01:11.288+08:00 [gateway] agent model: xiaomi-coding/mimo-v2.5 (thinking=medium, fast=off)\n=== agent:writer ===\n=== agent:publisher ===\nMay 16 20:32:51 VM-0-12-ubuntu node[2122957]: 2026-05-16T20:32:51.901+08:00 [diagnostic] liveness warning: reasons=event_loop_delay interval=30s eventLoopDelayP99Ms=236.1 eventLoopDelayMaxMs=7142.9 eventLoopUtilization=0.51 cpuCoreRatio=0.562 active=2 waiting=0 queued=1 recentPhases=sidecars.main-session-recovery:4ms,sidecars.restart-sentinel:68ms,post-attach.update-sentinel:52ms,post-ready.maintenance:96ms,sidecars.model-prewarm:4003ms,sidecars.session-locks:4667ms work=[active=agent:cto:main(processing/model_call,q=1,age=88s last=model_call:started)|agent:publisher:openai:d772bfed-d184-45c1-8d3d-88ef4345d245(processing/model_call,q=0,age=11s last=model_call:started) queued=agent:cto:main(processing/model_call,q=1,age=88s last=model_call:started)]\nMay 17 10:18:08 VM-0-12-ubuntu node[2456037]: 2026-05-17T10:18:08.768+08:00 [diagnostic] liveness warning: reasons=event_loop_delay interval=30s eventLoopDelayP99Ms=728.8 eventLoopDelayMaxMs=7134.5 eventLoopUtilization=0.761 cpuCoreRatio=0.824 active=2 waiting=0 queued=1 phase=channels.openclaw-weixin.start-account recentPhases=sidecars.main-session-recovery:4ms,sidecars.restart-sentinel:63ms,post-attach.update-sentinel:50ms,sidecars.session-locks:1358ms,post-ready.maintenance:80ms,sidecars.model-prewarm:3867ms work=[active=agent:cto:main(processing/model_call,q=1,age=303s last=model_call:started)|agent:publisher:openai:3bbabaea-b27b-4e59-b7c2-47556bb57b45(processing/model_call,q=0,age=17s last=model_call:started) queued=agent:cto:main(processing/model_call,q=1,age=303s last=model_call:started)]"}],"details":{"status":"completed","exitCode":0,"durationMs":276,"aggregated":"May 16 13:12:37 VM-0-12-ubuntu node[2122957]: 2026-05-16T13:12:37.092+08:00 [gateway] agent model: xiaomi-coding/mimo-v2.5 (thinking=medium, fast=off)\nMay 17 09:37:08 VM-0-12-ubuntu node[2444100]: 2026-05-17T09:37:08.712+08:00 [gateway] agent model: xiaomi-coding/mimo-v2.5 (thinking=medium, fast=off)\nMay 17 09:43:02 VM-0-12-ubuntu node[2446468]: 2026-05-17T09:43:02.345+08:00 [gateway] agent model: xiaomi-coding/mimo-v2.5 (thinking=medium, fast=off)\nMay 17 09:51:35 VM-0-12-ubuntu node[2450581]: 2026-05-17T09:51:35.005+08:00 [gateway] agent model: xiaomi-coding/mimo-v2.5 (thinking=medium, fast=off)\nMay 17 09:58:20 VM-0-12-ubuntu node[2454376]: 2026-05-17T09:58:20.069+08:00 [gateway] agent model: xiaomi-coding/mimo-v2.5 (thinking=medium, fast=off)\nMay 17 10:01:11 VM-0-12-ubuntu node[2456037]: 2026-05-17T10:01:11.288+08:00 [gateway] agent model: xiaomi-coding/mimo-v2.5 (thinking=medium, fast=off)\n=== agent:writer ===\n=== agent:publisher ===\nMay 16 20:32:51 VM-0-12-ubuntu node[2122957]: 2026-05-16T20:32:51.901+08:00 [diagnostic] liveness warning: reasons=event_loop_delay interval=30s eventLoopDelayP99Ms=236.1 eventLoopDelayMaxMs=7142.9 eventLoopUtilization=0.51 cpuCoreRatio=0.562 active=2 waiting=0 queued=1 recentPhases=sidecars.main-session-recovery:4ms,sidecars.restart-sentinel:68ms,post-attach.update-sentinel:52ms,post-ready.maintenance:96ms,sidecars.model-prewarm:4003ms,sidecars.session-locks:4667ms work=[active=agent:cto:main(processing/model_call,q=1,age=88s last=model_call:started)|agent:publisher:openai:d772bfed-d184-45c1-8d3d-88ef4345d245(processing/model_call,q=0,age=11s last=model_call:started) queued=agent:cto:main(processing/model_call,q=1,age=88s last=model_call:started)]\nMay 17 10:18:08 VM-0-12-ubuntu node[2456037]: 2026-05-17T10:18:08.768+08:00 [diagnostic] liveness warning: reasons=event_loop_delay interval=30s eventLoopDelayP99Ms=728.8 eventLoopDelayMaxMs=7134.5 eventLoopUtilization=0.761 cpuCoreRatio=0.824 active=2 waiting=0 queued=1 phase=channels.openclaw-weixin.start-account recentPhases=sidecars.main-session-recovery:4ms,sidecars.restart-sentinel:63ms,post-attach.update-sentinel:50ms,sidecars.session-locks:1358ms,post-ready.maintenance:80ms,sidecars.model-prewarm:3867ms work=[active=agent:cto:main(processing/model_call,q=1,age=303s last=model_call:started)|agent:publisher:openai:3bbabaea-b27b-4e59-b7c2-47556bb57b45(processing/model_call,q=0,age=17s last=model_call:started) queued=agent:cto:main(processing/model_call,q=1,age=303s last=model_call:started)]","cwd":"/root/.openclaw/workspace/main"},"isError":false,"timestamp":1778990608877}}
{"type":"message","id":"8b04fac0","parentId":"31ab1446","timestamp":"2026-05-17T04:03:53.481Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_q43upul1kn0jdrajrz1u2hy5","name":"exec","arguments":{"command":"# 直接测试mimo API连通性\ncurl -s -w \"\\n%{http_code}\" -X POST \"https://token-plan-cn.xiaomimimo.com/v1/chat/completions\" \\\n  -H \"Content-Type: application/json\" \\\n  -H \"Authorization: Bearer $(jq -r '.models.providers[\"xiaomi-coding\"].apiKey' /root/.openclaw/openclaw.json)\" \\\n  -d '{\n    \"model\": \"mimo-v2.5\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"hello, just say hi back in one word\"}],\n    \"max_tokens\": 50,\n    \"stream\": false\n  }' 2>/dev/null | tail -20"},"partialArgs":"{\"command\": \"# 直接测试mimo API连通性\\ncurl -s -w \\\"\\\\n%{http_code}\\\" -X POST \\\"https://token-plan-cn.xiaomimimo.com/v1/chat/completions\\\" \\\\\\n  -H \\\"Content-Type: application/json\\\" \\\\\\n  -H \\\"Authorization: Bearer $(jq -r '.models.providers[\\\"xiaomi-coding\\\"].apiKey' /root/.openclaw/openclaw.json)\\\" \\\\\\n  -d '{\\n    \\\"model\\\": \\\"mimo-v2.5\\\",\\n    \\\"messages\\\": [{\\\"role\\\": \\\"user\\\", \\\"content\\\": \\\"hello, just say hi back in one word\\\"}],\\n    \\\"max_tokens\\\": 50,\\n    \\\"stream\\\": false\\n  }' 2>/dev/null | tail -20\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":1718,"output":167,"cacheRead":94720,"cacheWrite":0,"totalTokens":96605,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778990608882,"responseId":"02177899060892392d02f197ffbb09fc6333ba360154ec699f133"}}
{"type":"message","id":"4cee3e54","parentId":"8b04fac0","timestamp":"2026-05-17T04:03:55.430Z","message":{"role":"toolResult","toolCallId":"call_q43upul1kn0jdrajrz1u2hy5","toolName":"exec","content":[{"type":"text","text":"{\"id\":\"2f578f1ee8c04daab255c7515cd96477\",\"choices\":[{\"finish_reason\":\"stop\",\"index\":0,\"message\":{\"content\":\"Hi！\",\"role\":\"assistant\",\"tool_calls\":null,\"reasoning_content\":\"The user wants me to say \\\"hi\\\" back in one word. This is a simple, friendly greeting.\"}}],\"created\":1778990634,\"model\":\"mimo-v2.5\",\"object\":\"chat.completion\",\"usage\":{\"completion_tokens\":27,\"prompt_tokens\":256,\"total_tokens\":283,\"completion_tokens_details\":{\"reasoning_tokens\":22},\"prompt_tokens_details\":{\"cached_tokens\":192}}}\n200"}],"details":{"status":"completed","exitCode":0,"durationMs":1486,"aggregated":"{\"id\":\"2f578f1ee8c04daab255c7515cd96477\",\"choices\":[{\"finish_reason\":\"stop\",\"index\":0,\"message\":{\"content\":\"Hi！\",\"role\":\"assistant\",\"tool_calls\":null,\"reasoning_content\":\"The user wants me to say \\\"hi\\\" back in one word. This is a simple, friendly greeting.\"}}],\"created\":1778990634,\"model\":\"mimo-v2.5\",\"object\":\"chat.completion\",\"usage\":{\"completion_tokens\":27,\"prompt_tokens\":256,\"total_tokens\":283,\"completion_tokens_details\":{\"reasoning_tokens\":22},\"prompt_tokens_details\":{\"cached_tokens\":192}}}\n200","cwd":"/root/.openclaw/workspace/main"},"isError":false,"timestamp":1778990635430}}
{"type":"message","id":"b17c7d18","parentId":"4cee3e54","timestamp":"2026-05-17T04:04:15.555Z","message":{"role":"assistant","content":[{"type":"text","text":"API连通没问题。现在测试带tools的请求——这才是关键，因为`unsupportedToolSchemaKeywords`可能会拦截。"},{"type":"toolCall","id":"call_2udl5mifls1pqsgbsnhasau5","name":"exec","arguments":{"command":"# 测试带tools的请求 - 用$ref（这是unsupportedToolSchemaKeywords之一）\nMIMO_KEY=$(jq -r '.models.providers[\"xiaomi-coding\"].apiKey' /root/.openclaw/openclaw.json)\n\ncurl -s -w \"\\n%{http_code}\" -X POST \"https://token-plan-cn.xiaomimimo.com/v1/chat/completions\" \\\n  -H \"Content-Type: application/json\" \\\n  -H \"Authorization: Bearer $MIMO_KEY\" \\\n  -d '{\n    \"model\": \"mimo-v2.5\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"what time is it?\"}],\n    \"max_tokens\": 200,\n    \"stream\": false,\n    \"tools\": [{\n      \"type\": \"function\",\n      \"function\": {\n        \"name\": \"get_time\",\n        \"description\": \"Get current time\",\n        \"parameters\": {\n          \"type\": \"object\",\n          \"properties\": {\n            \"timezone\": {\"type\": \"string\", \"description\": \"Timezone\"}\n          },\n          \"required\": [\"timezone\"],\n          \"additionalProperties\": false\n        }\n      }\n    }]\n  }' 2>/dev/null | tail -20"},"partialArgs":"{\"command\": \"# 测试带tools的请求 - 用$ref（这是unsupportedToolSchemaKeywords之一）\\nMIMO_KEY=$(jq -r '.models.providers[\\\"xiaomi-coding\\\"].apiKey' /root/.openclaw/openclaw.json)\\n\\ncurl -s -w \\\"\\\\n%{http_code}\\\" -X POST \\\"https://token-plan-cn.xiaomimimo.com/v1/chat/completions\\\" \\\\\\n  -H \\\"Content-Type: application/json\\\" \\\\\\n  -H \\\"Authorization: Bearer $MIMO_KEY\\\" \\\\\\n  -d '{\\n    \\\"model\\\": \\\"mimo-v2.5\\\",\\n    \\\"messages\\\": [{\\\"role\\\": \\\"user\\\", \\\"content\\\": \\\"what time is it?\\\"}],\\n    \\\"max_tokens\\\": 200,\\n    \\\"stream\\\": false,\\n    \\\"tools\\\": [{\\n      \\\"type\\\": \\\"function\\\",\\n      \\\"function\\\": {\\n        \\\"name\\\": \\\"get_time\\\",\\n        \\\"description\\\": \\\"Get current time\\\",\\n        \\\"parameters\\\": {\\n          \\\"type\\\": \\\"object\\\",\\n          \\\"properties\\\": {\\n            \\\"timezone\\\": {\\\"type\\\": \\\"string\\\", \\\"description\\\": \\\"Timezone\\\"}\\n          },\\n          \\\"required\\\": [\\\"timezone\\\"],\\n          \\\"additionalProperties\\\": false\\n        }\\n      }\\n    }]\\n  }' 2>/dev/null | tail -20\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":1521,"output":294,"cacheRead":95232,"cacheWrite":0,"totalTokens":97047,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778990635434,"responseId":"0217789906354763cff19ebb7dfda5cfc47c771b7d56bf277379c"}}
{"type":"message","id":"da27ec67","parentId":"b17c7d18","timestamp":"2026-05-17T04:04:17.918Z","message":{"role":"toolResult","toolCallId":"call_2udl5mifls1pqsgbsnhasau5","toolName":"exec","content":[{"type":"text","text":"{\"id\":\"553c5f9b2e5b407db5eaa890442f0818\",\"choices\":[{\"finish_reason\":\"tool_calls\",\"index\":0,\"message\":{\"content\":\"\",\"role\":\"assistant\",\"tool_calls\":[{\"id\":\"call_fe846e5507664eef835076e3\",\"function\":{\"arguments\":\"{\\\"timezone\\\": \\\"Asia/Shanghai\\\"}\",\"name\":\"get_time\"},\"type\":\"function\"}],\"reasoning_content\":\"The user wants to know the current time. Since they are asking in English and have not specified a timezone, I will assume they are in the Asia/Shanghai timezone (China Standard Time, CST), which is my default given my instructions as a Chinese AI model. I'll use the `get_time` tool to fetch the current time in that timezone.\"}}],\"created\":1778990657,\"model\":\"mimo-v2.5\",\"object\":\"chat.completion\",\"usage\":{\"completion_tokens\":96,\"prompt_tokens\":500,\"total_tokens\":596,\"completion_tokens_details\":{\"reasoning_tokens\":71},\"prompt_tokens_details\":{\"cached_tokens\":192}}}\n200"}],"details":{"status":"completed","exitCode":0,"durationMs":1937,"aggregated":"{\"id\":\"553c5f9b2e5b407db5eaa890442f0818\",\"choices\":[{\"finish_reason\":\"tool_calls\",\"index\":0,\"message\":{\"content\":\"\",\"role\":\"assistant\",\"tool_calls\":[{\"id\":\"call_fe846e5507664eef835076e3\",\"function\":{\"arguments\":\"{\\\"timezone\\\": \\\"Asia/Shanghai\\\"}\",\"name\":\"get_time\"},\"type\":\"function\"}],\"reasoning_content\":\"The user wants to know the current time. Since they are asking in English and have not specified a timezone, I will assume they are in the Asia/Shanghai timezone (China Standard Time, CST), which is my default given my instructions as a Chinese AI model. I'll use the `get_time` tool to fetch the current time in that timezone.\"}}],\"created\":1778990657,\"model\":\"mimo-v2.5\",\"object\":\"chat.completion\",\"usage\":{\"completion_tokens\":96,\"prompt_tokens\":500,\"total_tokens\":596,\"completion_tokens_details\":{\"reasoning_tokens\":71},\"prompt_tokens_details\":{\"cached_tokens\":192}}}\n200","cwd":"/root/.openclaw/workspace/main"},"isError":false,"timestamp":1778990657918}}
{"type":"message","id":"b0ebd543","parentId":"da27ec67","timestamp":"2026-05-17T04:04:47.011Z","message":{"role":"assistant","content":[{"type":"text","text":"API层面带tools的请求也没问题。那问题出在OpenClaw Gateway层——看看OpenClaw怎么处理mimo的compat，特别是`unsupportedToolSchemaKeywords`和`supportsDeveloperRole: false`。"},{"type":"toolCall","id":"call_sb3lbir1tit9ggzieee4bngo","name":"exec","arguments":{"command":"# 查看OpenClaw源码中如何处理unsupportedToolSchemaKeywords\nfind /root/.local/share/pnpm/global/5/.pnpm/openclaw@2026.5.12/node_modules/openclaw -type f -name \"*.js\" | xargs grep -l \"unsupportedToolSchemaKeywords\" 2>/dev/null | head -5"},"partialArgs":"{\"command\": \"# 查看OpenClaw源码中如何处理unsupportedToolSchemaKeywords\\nfind /root/.local/share/pnpm/global/5/.pnpm/openclaw@2026.5.12/node_modules/openclaw -type f -name \\\"*.js\\\" | xargs grep -l \\\"unsupportedToolSchemaKeywords\\\" 2>/dev/null | head -5\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":66696,"output":130,"cacheRead":30592,"cacheWrite":0,"totalTokens":97418,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778990657923,"responseId":"021778990660028afd8a45cd617ef9c890e825ed50ba4e56158fb"}}
{"type":"message","id":"c28a2302","parentId":"b0ebd543","timestamp":"2026-05-17T04:04:48.188Z","message":{"role":"toolResult","toolCallId":"call_sb3lbir1tit9ggzieee4bngo","toolName":"exec","content":[{"type":"text","text":"/root/.local/share/pnpm/global/5/.pnpm/openclaw@2026.5.12/node_modules/openclaw/dist/zod-schema.core-CrlgnnCI.js\n/root/.local/share/pnpm/global/5/.pnpm/openclaw@2026.5.12/node_modules/openclaw/dist/model-compat-TbJYnPAX.js\n/root/.local/share/pnpm/global/5/.pnpm/openclaw@2026.5.12/node_modules/openclaw/dist/provider-model-compat-Dk5etUbu.js\n/root/.local/share/pnpm/global/5/.pnpm/openclaw@2026.5.12/node_modules/openclaw/dist/openai-transport-stream-BWwvx0MZ.js\n/root/.local/share/pnpm/global/5/.pnpm/openclaw@2026.5.12/node_modules/openclaw/dist/extensions/venice/index.js"}],"details":{"status":"completed","exitCode":0,"durationMs":701,"aggregated":"/root/.local/share/pnpm/global/5/.pnpm/openclaw@2026.5.12/node_modules/openclaw/dist/zod-schema.core-CrlgnnCI.js\n/root/.local/share/pnpm/global/5/.pnpm/openclaw@2026.5.12/node_modules/openclaw/dist/model-compat-TbJYnPAX.js\n/root/.local/share/pnpm/global/5/.pnpm/openclaw@2026.5.12/node_modules/openclaw/dist/provider-model-compat-Dk5etUbu.js\n/root/.local/share/pnpm/global/5/.pnpm/openclaw@2026.5.12/node_modules/openclaw/dist/openai-transport-stream-BWwvx0MZ.js\n/root/.local/share/pnpm/global/5/.pnpm/openclaw@2026.5.12/node_modules/openclaw/dist/extensions/venice/index.js","cwd":"/root/.openclaw/workspace/main"},"isError":false,"timestamp":1778990688188}}
{"type":"message","id":"4a418bda","parentId":"c28a2302","timestamp":"2026-05-17T04:05:13.856Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_bysv1svwnm5m8gk5kjok4ama","name":"exec","arguments":{"command":"# 查看OpenClaw源码中如何处理unsupportedToolSchemaKeywords - 核心逻辑\ngrep -A 20 \"unsupportedToolSchemaKeywords\" /root/.local/share/pnpm/global/5/.pnpm/openclaw@2026.5.12/node_modules/openclaw/dist/model-compat-TbJYnPAX.js 2>/dev/null | head -60"},"partialArgs":"{\"command\": \"# 查看OpenClaw源码中如何处理unsupportedToolSchemaKeywords - 核心逻辑\\ngrep -A 20 \\\"unsupportedToolSchemaKeywords\\\" /root/.local/share/pnpm/global/5/.pnpm/openclaw@2026.5.12/node_modules/openclaw/dist/model-compat-TbJYnPAX.js 2>/dev/null | head -60\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":91600,"output":90,"cacheRead":6016,"cacheWrite":0,"totalTokens":97706,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778990688192,"responseId":"0217789906891922867244b9ca15d3ce81ac5a3ed257d59b5d5ef"}}
{"type":"message","id":"a1f0f0af","parentId":"4a418bda","timestamp":"2026-05-17T04:05:14.483Z","message":{"role":"toolResult","toolCallId":"call_bysv1svwnm5m8gk5kjok4ama","toolName":"exec","content":[{"type":"text","text":"unsupportedToolSchemaKeywords: Array.from(XAI_UNSUPPORTED_SCHEMA_KEYWORDS),\n\t\tnativeWebSearchTool: true,\n\t\ttoolCallArgumentsEncoding: HTML_ENTITY_TOOL_CALL_ARGUMENTS_ENCODING\n\t};\n}\nfunction applyXaiModelCompat(model) {\n\treturn applyModelCompatPatch(model, resolveXaiModelCompatPatch());\n}\nfunction normalizeNativeXaiModelId(id) {\n\tif (id === \"grok-4-fast-reasoning\") return \"grok-4-fast\";\n\tif (id === \"grok-4-1-fast-reasoning\") return \"grok-4-1-fast\";\n\tif (id === \"grok-4.20-experimental-beta-0304-reasoning\") return \"grok-4.20-beta-latest-reasoning\";\n\tif (id === \"grok-4.20-experimental-beta-0304-non-reasoning\") return \"grok-4.20-beta-latest-non-reasoning\";\n\tif (id === \"grok-4.20-reasoning\") return \"grok-4.20-beta-latest-reasoning\";\n\tif (id === \"grok-4.20-non-reasoning\") return \"grok-4.20-beta-latest-non-reasoning\";\n\treturn id;\n}\n//#endregion\nexport { normalizeNativeXaiModelId as a, applyXaiModelCompat as i, XAI_TOOL_SCHEMA_PROFILE as n, resolveXaiModelCompatPatch as o, XAI_UNSUPPORTED_SCHEMA_KEYWORDS as r, HTML_ENTITY_TOOL_CALL_ARGUMENTS_ENCODING as t };"}],"details":{"status":"completed","exitCode":0,"durationMs":194,"aggregated":"unsupportedToolSchemaKeywords: Array.from(XAI_UNSUPPORTED_SCHEMA_KEYWORDS),\n\t\tnativeWebSearchTool: true,\n\t\ttoolCallArgumentsEncoding: HTML_ENTITY_TOOL_CALL_ARGUMENTS_ENCODING\n\t};\n}\nfunction applyXaiModelCompat(model) {\n\treturn applyModelCompatPatch(model, resolveXaiModelCompatPatch());\n}\nfunction normalizeNativeXaiModelId(id) {\n\tif (id === \"grok-4-fast-reasoning\") return \"grok-4-fast\";\n\tif (id === \"grok-4-1-fast-reasoning\") return \"grok-4-1-fast\";\n\tif (id === \"grok-4.20-experimental-beta-0304-reasoning\") return \"grok-4.20-beta-latest-reasoning\";\n\tif (id === \"grok-4.20-experimental-beta-0304-non-reasoning\") return \"grok-4.20-beta-latest-non-reasoning\";\n\tif (id === \"grok-4.20-reasoning\") return \"grok-4.20-beta-latest-reasoning\";\n\tif (id === \"grok-4.20-non-reasoning\") return \"grok-4.20-beta-latest-non-reasoning\";\n\treturn id;\n}\n//#endregion\nexport { normalizeNativeXaiModelId as a, applyXaiModelCompat as i, XAI_TOOL_SCHEMA_PROFILE as n, resolveXaiModelCompatPatch as o, XAI_UNSUPPORTED_SCHEMA_KEYWORDS as r, HTML_ENTITY_TOOL_CALL_ARGUMENTS_ENCODING as t };","cwd":"/root/.openclaw/workspace/main"},"isError":false,"timestamp":1778990714483}}
{"type":"message","id":"b3132691","parentId":"a1f0f0af","timestamp":"2026-05-17T04:05:41.502Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_txmw8jyocqsf6i1ip4qvqw6k","name":"exec","arguments":{"command":"# 查看provider-model-compat中如何处理unsupportedToolSchemaKeywords - 这是核心\ngrep -B 5 -A 30 \"unsupportedToolSchemaKeywords\" /root/.local/share/pnpm/global/5/.pnpm/openclaw@2026.5.12/node_modules/openclaw/dist/provider-model-compat-Dk5etUbu.js 2>/dev/null | head -80"},"partialArgs":"{\"command\": \"# 查看provider-model-compat中如何处理unsupportedToolSchemaKeywords - 这是核心\\ngrep -B 5 -A 30 \\\"unsupportedToolSchemaKeywords\\\" /root/.local/share/pnpm/global/5/.pnpm/openclaw@2026.5.12/node_modules/openclaw/dist/provider-model-compat-Dk5etUbu.js 2>/dev/null | head -80\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":66290,"output":92,"cacheRead":31744,"cacheWrite":0,"totalTokens":98126,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778990714487,"responseId":"02177899071781349e6abb1458e8172426b2aec92a3693d956c97"}}
{"type":"message","id":"ca771768","parentId":"b3132691","timestamp":"2026-05-17T04:05:42.120Z","message":{"role":"toolResult","toolCallId":"call_txmw8jyocqsf6i1ip4qvqw6k","toolName":"exec","content":[{"type":"text","text":"}\nfunction resolveToolCallArgumentsEncoding(modelOrCompat) {\n\treturn extractModelCompat(modelOrCompat)?.toolCallArgumentsEncoding;\n}\nfunction resolveUnsupportedToolSchemaKeywords(modelOrCompat) {\n\tconst keywords = extractModelCompat(modelOrCompat)?.unsupportedToolSchemaKeywords ?? [];\n\treturn new Set(keywords.filter((keyword) => typeof keyword === \"string\").map((keyword) => keyword.trim()).filter(Boolean));\n}\nfunction isOpenAiCompletionsModel(model) {\n\treturn model.api === \"openai-completions\";\n}\nfunction isAnthropicMessagesModel(model) {\n\treturn model.api === \"anthropic-messages\";\n}\nfunction normalizeAnthropicBaseUrl(baseUrl) {\n\treturn baseUrl.replace(/\\/v1\\/?$/, \"\");\n}\nfunction normalizeModelCompat(model) {\n\tconst baseUrl = model.baseUrl ?? \"\";\n\tif (isAnthropicMessagesModel(model) && baseUrl) {\n\t\tconst normalized = normalizeAnthropicBaseUrl(baseUrl);\n\t\tif (normalized !== baseUrl) return {\n\t\t\t...model,\n\t\t\tbaseUrl: normalized\n\t\t};\n\t}\n\tif (!isOpenAiCompletionsModel(model)) return model;\n\tconst compat = model.compat ?? void 0;\n\tconst detectedCompatDefaults = baseUrl ? detectOpenAICompletionsCompat(model).defaults : void 0;\n\tif (!Boolean(detectedCompatDefaults && (!detectedCompatDefaults.supportsDeveloperRole || !detectedCompatDefaults.supportsUsageInStreaming || !detectedCompatDefaults.supportsStrictMode))) return model;\n\tconst forcedDeveloperRole = compat?.supportsDeveloperRole === true;\n\tconst hasStreamingUsageOverride = compat?.supportsUsageInStreaming !== void 0;\n\tconst targetStrictMode = compat?.supportsStrictMode ?? detectedCompatDefaults?.supportsStrictMode;\n\tif (compat?.supportsDeveloperRole !== void 0 && hasStreamingUsageOverride && compat?.supportsStrictMode !== void 0) return model;\n\treturn {\n\t\t...model,"}],"details":{"status":"completed","exitCode":0,"durationMs":182,"aggregated":"}\nfunction resolveToolCallArgumentsEncoding(modelOrCompat) {\n\treturn extractModelCompat(modelOrCompat)?.toolCallArgumentsEncoding;\n}\nfunction resolveUnsupportedToolSchemaKeywords(modelOrCompat) {\n\tconst keywords = extractModelCompat(modelOrCompat)?.unsupportedToolSchemaKeywords ?? [];\n\treturn new Set(keywords.filter((keyword) => typeof keyword === \"string\").map((keyword) => keyword.trim()).filter(Boolean));\n}\nfunction isOpenAiCompletionsModel(model) {\n\treturn model.api === \"openai-completions\";\n}\nfunction isAnthropicMessagesModel(model) {\n\treturn model.api === \"anthropic-messages\";\n}\nfunction normalizeAnthropicBaseUrl(baseUrl) {\n\treturn baseUrl.replace(/\\/v1\\/?$/, \"\");\n}\nfunction normalizeModelCompat(model) {\n\tconst baseUrl = model.baseUrl ?? \"\";\n\tif (isAnthropicMessagesModel(model) && baseUrl) {\n\t\tconst normalized = normalizeAnthropicBaseUrl(baseUrl);\n\t\tif (normalized !== baseUrl) return {\n\t\t\t...model,\n\t\t\tbaseUrl: normalized\n\t\t};\n\t}\n\tif (!isOpenAiCompletionsModel(model)) return model;\n\tconst compat = model.compat ?? void 0;\n\tconst detectedCompatDefaults = baseUrl ? detectOpenAICompletionsCompat(model).defaults : void 0;\n\tif (!Boolean(detectedCompatDefaults && (!detectedCompatDefaults.supportsDeveloperRole || !detectedCompatDefaults.supportsUsageInStreaming || !detectedCompatDefaults.supportsStrictMode))) return model;\n\tconst forcedDeveloperRole = compat?.supportsDeveloperRole === true;\n\tconst hasStreamingUsageOverride = compat?.supportsUsageInStreaming !== void 0;\n\tconst targetStrictMode = compat?.supportsStrictMode ?? detectedCompatDefaults?.supportsStrictMode;\n\tif (compat?.supportsDeveloperRole !== void 0 && hasStreamingUsageOverride && compat?.supportsStrictMode !== void 0) return model;\n\treturn {\n\t\t...model,","cwd":"/root/.openclaw/workspace/main"},"isError":false,"timestamp":1778990742120}}
{"type":"message","id":"0c438dda","parentId":"ca771768","timestamp":"2026-05-17T04:06:03.436Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_kh54f8l6sjcvjolukexvgg3n","name":"exec","arguments":{"command":"# 查看stripUnsupportedSchemaKeywords的实现\ngrep -B 5 -A 30 \"stripUnsupported\\|removeUnsupported\\|filterSchema\\|stripSchema\" /root/.local/share/pnpm/global/5/.pnpm/openclaw@2026.5.12/node_modules/openclaw/dist/provider-model-compat-Dk5etUbu.js 2>/dev/null | head -80"},"partialArgs":"{\"command\": \"# 查看stripUnsupportedSchemaKeywords的实现\\ngrep -B 5 -A 30 \\\"stripUnsupported\\\\|removeUnsupported\\\\|filterSchema\\\\|stripSchema\\\" /root/.local/share/pnpm/global/5/.pnpm/openclaw@2026.5.12/node_modules/openclaw/dist/provider-model-compat-Dk5etUbu.js 2>/dev/null | head -80\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":2124,"output":93,"cacheRead":96384,"cacheWrite":0,"totalTokens":98601,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778990742125,"responseId":"021778990742171a292fc2ba91e79c594f032f5a64a4fe01765a5"}}
{"type":"message","id":"1c71f966","parentId":"0c438dda","timestamp":"2026-05-17T04:06:03.880Z","message":{"role":"toolResult","toolCallId":"call_kh54f8l6sjcvjolukexvgg3n","toolName":"exec","content":[{"type":"text","text":"(no output)"}],"details":{"status":"completed","exitCode":0,"durationMs":12,"aggregated":"","cwd":"/root/.openclaw/workspace/main"},"isError":false,"timestamp":1778990763880}}
{"type":"message","id":"43df8f9a","parentId":"1c71f966","timestamp":"2026-05-17T04:06:17.254Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_zbm0m3704gd37vu6b11v8ani","name":"exec","arguments":{"command":"# 搜索openai-transport中的处理\ngrep -B 5 -A 30 \"unsupportedToolSchemaKeywords\\|stripUnsupported\\|removeSchema\\|filterSchema\" /root/.local/share/pnpm/global/5/.pnpm/openclaw@2026.5.12/node_modules/openclaw/dist/openai-transport-stream-BWwvx0MZ.js 2>/dev/null | head -100"},"partialArgs":"{\"command\": \"# 搜索openai-transport中的处理\\ngrep -B 5 -A 30 \\\"unsupportedToolSchemaKeywords\\\\|stripUnsupported\\\\|removeSchema\\\\|filterSchema\\\" /root/.local/share/pnpm/global/5/.pnpm/openclaw@2026.5.12/node_modules/openclaw/dist/openai-transport-stream-BWwvx0MZ.js 2>/dev/null | head -100\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":1969,"output":96,"cacheRead":96640,"cacheWrite":0,"totalTokens":98705,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778990763885,"responseId":"0217789907639287b02e641abd64d0836d5f697bc661db6d3678e"}}
{"type":"message","id":"47c4eb10","parentId":"43df8f9a","timestamp":"2026-05-17T04:06:17.847Z","message":{"role":"toolResult","toolCallId":"call_zbm0m3704gd37vu6b11v8ani","toolName":"exec","content":[{"type":"text","text":"if (options?.supportsStrictMode) return false;\n}\n//#endregion\n//#region src/agents/openai-tool-schema.ts\nfunction resolveToolSchemaModelCompat(compat) {\n\tif (!compat || !Array.isArray(compat.unsupportedToolSchemaKeywords)) return;\n\treturn { unsupportedToolSchemaKeywords: compat.unsupportedToolSchemaKeywords.filter((keyword) => typeof keyword === \"string\") };\n}\nfunction normalizeStrictOpenAIJsonSchema(schema, modelCompat) {\n\treturn normalizeStrictOpenAIJsonSchemaRecursive(normalizeToolParameterSchema(schema ?? {}, { modelCompat: resolveToolSchemaModelCompat(modelCompat) }), 0);\n}\nfunction normalizeStrictOpenAIJsonSchemaRecursive(schema, depth) {\n\tif (Array.isArray(schema)) {\n\t\tlet changed = false;\n\t\tconst normalized = schema.map((entry) => {\n\t\t\tconst next = normalizeStrictOpenAIJsonSchemaRecursive(entry, depth);\n\t\t\tchanged ||= next !== entry;\n\t\t\treturn next;\n\t\t});\n\t\treturn changed ? normalized : schema;\n\t}\n\tif (!schema || typeof schema !== \"object\") return schema;\n\tconst record = schema;\n\tlet changed = false;\n\tconst normalized = {};\n\tfor (const [key, value] of Object.entries(record)) {\n\t\tconst next = normalizeStrictOpenAIJsonSchemaRecursive(value, key === \"properties\" ? depth : depth + 1);\n\t\tnormalized[key] = next;\n\t\tchanged ||= next !== value;\n\t}\n\tif (normalized.type === \"object\") {\n\t\tconst properties = normalized.properties && typeof normalized.properties === \"object\" && !Array.isArray(normalized.properties) ? normalized.properties : void 0;\n\t\tif (properties && Object.keys(properties).length === 0 && !Array.isArray(normalized.required)) {\n\t\t\tnormalized.required = [];\n\t\t\tchanged = true;\n\t\t}\n\t\tif (depth === 0 && !(\"additionalProperties\" in normalized)) {"}],"details":{"status":"completed","exitCode":0,"durationMs":179,"aggregated":"if (options?.supportsStrictMode) return false;\n}\n//#endregion\n//#region src/agents/openai-tool-schema.ts\nfunction resolveToolSchemaModelCompat(compat) {\n\tif (!compat || !Array.isArray(compat.unsupportedToolSchemaKeywords)) return;\n\treturn { unsupportedToolSchemaKeywords: compat.unsupportedToolSchemaKeywords.filter((keyword) => typeof keyword === \"string\") };\n}\nfunction normalizeStrictOpenAIJsonSchema(schema, modelCompat) {\n\treturn normalizeStrictOpenAIJsonSchemaRecursive(normalizeToolParameterSchema(schema ?? {}, { modelCompat: resolveToolSchemaModelCompat(modelCompat) }), 0);\n}\nfunction normalizeStrictOpenAIJsonSchemaRecursive(schema, depth) {\n\tif (Array.isArray(schema)) {\n\t\tlet changed = false;\n\t\tconst normalized = schema.map((entry) => {\n\t\t\tconst next = normalizeStrictOpenAIJsonSchemaRecursive(entry, depth);\n\t\t\tchanged ||= next !== entry;\n\t\t\treturn next;\n\t\t});\n\t\treturn changed ? normalized : schema;\n\t}\n\tif (!schema || typeof schema !== \"object\") return schema;\n\tconst record = schema;\n\tlet changed = false;\n\tconst normalized = {};\n\tfor (const [key, value] of Object.entries(record)) {\n\t\tconst next = normalizeStrictOpenAIJsonSchemaRecursive(value, key === \"properties\" ? depth : depth + 1);\n\t\tnormalized[key] = next;\n\t\tchanged ||= next !== value;\n\t}\n\tif (normalized.type === \"object\") {\n\t\tconst properties = normalized.properties && typeof normalized.properties === \"object\" && !Array.isArray(normalized.properties) ? normalized.properties : void 0;\n\t\tif (properties && Object.keys(properties).length === 0 && !Array.isArray(normalized.required)) {\n\t\t\tnormalized.required = [];\n\t\t\tchanged = true;\n\t\t}\n\t\tif (depth === 0 && !(\"additionalProperties\" in normalized)) {","cwd":"/root/.openclaw/workspace/main"},"isError":false,"timestamp":1778990777847}}
{"type":"message","id":"64013fd1","parentId":"47c4eb10","timestamp":"2026-05-17T04:06:33.163Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_xforgk3qnvbw10x80xfe6ky8","name":"exec","arguments":{"command":"# 查看normalizeToolParameterSchema如何处理unsupportedToolSchemaKeywords\ngrep -B 5 -A 40 \"normalizeToolParameterSchema\" /root/.local/share/pnpm/global/5/.pnpm/openclaw@2026.5.12/node_modules/openclaw/dist/openai-transport-stream-BWwvx0MZ.js 2>/dev/null | head -80"},"partialArgs":"{\"command\": \"# 查看normalizeToolParameterSchema如何处理unsupportedToolSchemaKeywords\\ngrep -B 5 -A 40 \\\"normalizeToolParameterSchema\\\" /root/.local/share/pnpm/global/5/.pnpm/openclaw@2026.5.12/node_modules/openclaw/dist/openai-transport-stream-BWwvx0MZ.js 2>/dev/null | head -80\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":1170,"output":89,"cacheRead":97920,"cacheWrite":0,"totalTokens":99179,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778990777852,"responseId":"0217789907778973c79e31052fcdeaec368a63aa27b4cbf9124e5"}}
{"type":"message","id":"e91fbf00","parentId":"64013fd1","timestamp":"2026-05-17T04:06:33.758Z","message":{"role":"toolResult","toolCallId":"call_xforgk3qnvbw10x80xfe6ky8","toolName":"exec","content":[{"type":"text","text":"import { r as resolveProviderRequestCapabilities } from \"./provider-attribution-BJpDy8Xw.js\";\nimport { c as detectOpenAICompletionsCompat } from \"./provider-model-compat-Dk5etUbu.js\";\nimport { c as resolveProviderRequestPolicyConfig, i as mergeModelProviderRequestOverrides, n as buildProviderRequestDispatcherPolicy, r as getModelProviderRequestTransport } from \"./provider-request-config-DgQ_QSz5.js\";\nimport { v as ssrfPolicyFromHttpBaseUrlFakeIpHostnameAllowlist } from \"./ssrf-CYoLqc2K.js\";\nimport { a as withTrustedEnvProxyGuardedFetchMode, n as fetchWithSsrFGuard } from \"./fetch-guard-B7xT8hT9.js\";\nimport { t as normalizeToolParameterSchema } from \"./pi-tools-parameter-schema-o0pF8kVc.js\";\nimport { i as stripSystemPromptCacheBoundary } from \"./system-prompt-cache-boundary-DE1WywtY.js\";\nimport { i as COPILOT_INTEGRATION_ID, s as buildCopilotIdeHeaders } from \"./provider-auth-D5QGE8z6.js\";\nimport { r as resolveDebugProxySettings } from \"./env-Dcpy04BI.js\";\nimport { n as ensureModelProviderLocalService } from \"./provider-local-service-DS3c-NJd.js\";\nimport { n as repairToolUseResultPairing } from \"./session-transcript-repair-BLXTkE_j.js\";\nimport { randomUUID } from \"node:crypto\";\nimport { calculateCost, createAssistantMessageEventStream, getEnvApiKey, parseStreamingJson } from \"@earendil-works/pi-ai\";\nimport { convertMessages } from \"@earendil-works/pi-ai/openai-completions\";\nimport OpenAI, { AzureOpenAI } from \"openai\";\n//#region src/agents/copilot-dynamic-headers.ts\nfunction inferCopilotInitiator(messages) {\n\tconst last = messages[messages.length - 1];\n\tif (!last) return \"user\";\n\tif (last.role === \"user\" && containsCopilotContentType(last.content, \"tool_result\")) return \"agent\";\n\treturn last.role === \"user\" ? \"user\" : \"agent\";\n}\nfunction containsCopilotContentType(value, type) {\n\tif (Array.isArray(value)) return value.some((item) => containsCopilotContentType(item, type));\n\tif (!value || typeof value !== \"object\") return false;\n\tconst entry = value;\n\treturn entry.type === type || containsCopilotContentType(entry.content, type);\n}\nfunction hasCopilotVisionInput(messages) {\n\treturn messages.some((message) => {\n\t\tif (message.role === \"user\" && Array.isArray(message.content)) return message.content.some((item) => containsCopilotContentType(item, \"image\"));\n\t\tif (message.role === \"toolResult\" && Array.isArray(message.content)) return message.content.some((item) => containsCopilotContentType(item, \"image\"));\n\t\treturn false;\n\t});\n}\nfunction buildCopilotDynamicHeaders(params) {\n\treturn {\n\t\t...buildCopilotIdeHeaders(),\n\t\t\"Copilot-Integration-Id\": COPILOT_INTEGRATION_ID,\n\t\t\"Openai-Organization\": \"github-copilot\",\n\t\t\"x-initiator\": inferCopilotInitiator(params.messages),\n\t\t...params.hasImages ? { \"Copilot-Vision-Request\": \"true\" } : {}\n\t};\n}\n//#endregion\n//#region src/agents/model-transport-debug.ts\n--\nfunction resolveToolSchemaModelCompat(compat) {\n\tif (!compat || !Array.isArray(compat.unsupportedToolSchemaKeywords)) return;\n\treturn { unsupportedToolSchemaKeywords: compat.unsupportedToolSchemaKeywords.filter((keyword) => typeof keyword === \"string\") };\n}\nfunction normalizeStrictOpenAIJsonSchema(schema, modelCompat) {\n\treturn normalizeStrictOpenAIJsonSchemaRecursive(normalizeToolParameterSchema(schema ?? {}, { modelCompat: resolveToolSchemaModelCompat(modelCompat) }), 0);\n}\nfunction normalizeStrictOpenAIJsonSchemaRecursive(schema, depth) {\n\tif (Array.isArray(schema)) {\n\t\tlet changed = false;\n\t\tconst normalized = schema.map((entry) => {\n\t\t\tconst next = normalizeStrictOpenAIJsonSchemaRecursive(entry, depth);\n\t\t\tchanged ||= next !== entry;\n\t\t\treturn next;\n\t\t});\n\t\treturn changed ? normalized : schema;\n\t}\n\tif (!schema || typeof schema !== \"object\") return schema;\n\tconst record = schema;\n\tlet changed = false;\n\tconst normalized = {};\n\tfor (const [key, value] of Object.entries(record)) {\n\t\tconst next = normalizeStrictOpenAIJsonSchemaRecursive(value, key === \"properties\" ? depth : depth + 1);\n\t\tnormalized[key] = next;\n\t\tchanged ||= next !== value;\n\t}\n\tif (normalized.type === \"object\") {\n\t\tconst properties = normalized.properties && typeof normalized.properties === \"object\" && !Array.isArray(normalized.properties) ? normalized.properties : void 0;\n\t\tif (properties && Object.keys(properties).length === 0 && !Array.isArray(normalized.required)) {\n\t\t\tnormalized.required = [];\n\t\t\tchanged = true;\n\t\t}\n\t\tif (depth === 0 && !(\"additionalProperties\" in normalized)) {"}],"details":{"status":"completed","exitCode":0,"durationMs":181,"aggregated":"import { r as resolveProviderRequestCapabilities } from \"./provider-attribution-BJpDy8Xw.js\";\nimport { c as detectOpenAICompletionsCompat } from \"./provider-model-compat-Dk5etUbu.js\";\nimport { c as resolveProviderRequestPolicyConfig, i as mergeModelProviderRequestOverrides, n as buildProviderRequestDispatcherPolicy, r as getModelProviderRequestTransport } from \"./provider-request-config-DgQ_QSz5.js\";\nimport { v as ssrfPolicyFromHttpBaseUrlFakeIpHostnameAllowlist } from \"./ssrf-CYoLqc2K.js\";\nimport { a as withTrustedEnvProxyGuardedFetchMode, n as fetchWithSsrFGuard } from \"./fetch-guard-B7xT8hT9.js\";\nimport { t as normalizeToolParameterSchema } from \"./pi-tools-parameter-schema-o0pF8kVc.js\";\nimport { i as stripSystemPromptCacheBoundary } from \"./system-prompt-cache-boundary-DE1WywtY.js\";\nimport { i as COPILOT_INTEGRATION_ID, s as buildCopilotIdeHeaders } from \"./provider-auth-D5QGE8z6.js\";\nimport { r as resolveDebugProxySettings } from \"./env-Dcpy04BI.js\";\nimport { n as ensureModelProviderLocalService } from \"./provider-local-service-DS3c-NJd.js\";\nimport { n as repairToolUseResultPairing } from \"./session-transcript-repair-BLXTkE_j.js\";\nimport { randomUUID } from \"node:crypto\";\nimport { calculateCost, createAssistantMessageEventStream, getEnvApiKey, parseStreamingJson } from \"@earendil-works/pi-ai\";\nimport { convertMessages } from \"@earendil-works/pi-ai/openai-completions\";\nimport OpenAI, { AzureOpenAI } from \"openai\";\n//#region src/agents/copilot-dynamic-headers.ts\nfunction inferCopilotInitiator(messages) {\n\tconst last = messages[messages.length - 1];\n\tif (!last) return \"user\";\n\tif (last.role === \"user\" && containsCopilotContentType(last.content, \"tool_result\")) return \"agent\";\n\treturn last.role === \"user\" ? \"user\" : \"agent\";\n}\nfunction containsCopilotContentType(value, type) {\n\tif (Array.isArray(value)) return value.some((item) => containsCopilotContentType(item, type));\n\tif (!value || typeof value !== \"object\") return false;\n\tconst entry = value;\n\treturn entry.type === type || containsCopilotContentType(entry.content, type);\n}\nfunction hasCopilotVisionInput(messages) {\n\treturn messages.some((message) => {\n\t\tif (message.role === \"user\" && Array.isArray(message.content)) return message.content.some((item) => containsCopilotContentType(item, \"image\"));\n\t\tif (message.role === \"toolResult\" && Array.isArray(message.content)) return message.content.some((item) => containsCopilotContentType(item, \"image\"));\n\t\treturn false;\n\t});\n}\nfunction buildCopilotDynamicHeaders(params) {\n\treturn {\n\t\t...buildCopilotIdeHeaders(),\n\t\t\"Copilot-Integration-Id\": COPILOT_INTEGRATION_ID,\n\t\t\"Openai-Organization\": \"github-copilot\",\n\t\t\"x-initiator\": inferCopilotInitiator(params.messages),\n\t\t...params.hasImages ? { \"Copilot-Vision-Request\": \"true\" } : {}\n\t};\n}\n//#endregion\n//#region src/agents/model-transport-debug.ts\n--\nfunction resolveToolSchemaModelCompat(compat) {\n\tif (!compat || !Array.isArray(compat.unsupportedToolSchemaKeywords)) return;\n\treturn { unsupportedToolSchemaKeywords: compat.unsupportedToolSchemaKeywords.filter((keyword) => typeof keyword === \"string\") };\n}\nfunction normalizeStrictOpenAIJsonSchema(schema, modelCompat) {\n\treturn normalizeStrictOpenAIJsonSchemaRecursive(normalizeToolParameterSchema(schema ?? {}, { modelCompat: resolveToolSchemaModelCompat(modelCompat) }), 0);\n}\nfunction normalizeStrictOpenAIJsonSchemaRecursive(schema, depth) {\n\tif (Array.isArray(schema)) {\n\t\tlet changed = false;\n\t\tconst normalized = schema.map((entry) => {\n\t\t\tconst next = normalizeStrictOpenAIJsonSchemaRecursive(entry, depth);\n\t\t\tchanged ||= next !== entry;\n\t\t\treturn next;\n\t\t});\n\t\treturn changed ? normalized : schema;\n\t}\n\tif (!schema || typeof schema !== \"object\") return schema;\n\tconst record = schema;\n\tlet changed = false;\n\tconst normalized = {};\n\tfor (const [key, value] of Object.entries(record)) {\n\t\tconst next = normalizeStrictOpenAIJsonSchemaRecursive(value, key === \"properties\" ? depth : depth + 1);\n\t\tnormalized[key] = next;\n\t\tchanged ||= next !== value;\n\t}\n\tif (normalized.type === \"object\") {\n\t\tconst properties = normalized.properties && typeof normalized.properties === \"object\" && !Array.isArray(normalized.properties) ? normalized.properties : void 0;\n\t\tif (properties && Object.keys(properties).length === 0 && !Array.isArray(normalized.required)) {\n\t\t\tnormalized.required = [];\n\t\t\tchanged = true;\n\t\t}\n\t\tif (depth === 0 && !(\"additionalProperties\" in normalized)) {","cwd":"/root/.openclaw/workspace/main"},"isError":false,"timestamp":1778990793757}}
{"type":"message","id":"50414c03","parentId":"e91fbf00","timestamp":"2026-05-17T04:06:48.495Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_nn3uyj7e5yk18ls1x01yuzwu","name":"exec","arguments":{"command":"# 查看pi-tools-parameter-schema中normalizeToolParameterSchema的实现\ngrep -B 5 -A 50 \"normalizeToolParameterSchema\" /root/.local/share/pnpm/global/5/.pnpm/openclaw@2026.5.12/node_modules/openclaw/dist/pi-tools-parameter-schema-o0pF8kVc.js 2>/dev/null | head -80"},"partialArgs":"{\"command\": \"# 查看pi-tools-parameter-schema中normalizeToolParameterSchema的实现\\ngrep -B 5 -A 50 \\\"normalizeToolParameterSchema\\\" /root/.local/share/pnpm/global/5/.pnpm/openclaw@2026.5.12/node_modules/openclaw/dist/pi-tools-parameter-schema-o0pF8kVc.js 2>/dev/null | head -80\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":1196,"output":90,"cacheRead":99072,"cacheWrite":0,"totalTokens":100358,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778990793762,"responseId":"0217789907947745b348a82794b5725a30214ab58731f436bdd55"}}
{"type":"message","id":"40cdd1fc","parentId":"50414c03","timestamp":"2026-05-17T04:06:49.102Z","message":{"role":"toolResult","toolCallId":"call_nn3uyj7e5yk18ls1x01yuzwu","toolName":"exec","content":[{"type":"text","text":"changed = true;\n\t\t}\n\t}\n\treturn changed ? nextSchema : schema;\n}\nfunction normalizeToolParameterSchema(schema, options) {\n\tconst schemaRecord = schema && typeof schema === \"object\" ? schema : void 0;\n\tif (!schemaRecord) return schema;\n\tconst normalizedProvider = normalizeLowercaseStringOrEmpty(options?.modelProvider);\n\tconst isGeminiProvider = normalizedProvider.includes(\"google\") || normalizedProvider.includes(\"gemini\");\n\tconst isAnthropicProvider = normalizedProvider.includes(\"anthropic\");\n\tconst unsupportedToolSchemaKeywords = resolveUnsupportedToolSchemaKeywords(options?.modelCompat);\n\tfunction applyProviderCleaning(s) {\n\t\tconst normalizedSchema = normalizeArraySchemasMissingItems(s);\n\t\tif (isGeminiProvider && !isAnthropicProvider) return cleanSchemaForGemini(normalizedSchema);\n\t\tif (unsupportedToolSchemaKeywords.size > 0) return stripUnsupportedSchemaKeywords(normalizedSchema, unsupportedToolSchemaKeywords);\n\t\treturn normalizedSchema;\n\t}\n\tconst conditionalKey = getTopLevelConditionalKey(schemaRecord);\n\tconst flattenableVariantKey = getFlattenableVariantKey(schemaRecord);\n\tif (hasTopLevelObjectSchema(schemaRecord, conditionalKey)) return applyProviderCleaning(schemaRecord);\n\tif (isObjectLikeSchemaMissingType(schemaRecord, conditionalKey)) return applyProviderCleaning({\n\t\t...schemaRecord,\n\t\ttype: \"object\",\n\t\tproperties: isSchemaRecord(schemaRecord.properties) ? schemaRecord.properties : {}\n\t});\n\tif (isTypedObjectSchemaMissingValidProperties(schemaRecord, conditionalKey)) return applyProviderCleaning({\n\t\t...schemaRecord,\n\t\tproperties: {}\n\t});\n\tif (!flattenableVariantKey) {\n\t\tif (isTrulyEmptySchema(schemaRecord)) return applyProviderCleaning({\n\t\t\ttype: \"object\",\n\t\t\tproperties: {}\n\t\t});\n\t\tif (conditionalKey === \"allOf\") return applyProviderCleaning(schema);\n\t\treturn applyProviderCleaning(schema);\n\t}\n\tconst variants = schemaRecord[flattenableVariantKey];\n\tconst mergedProperties = {};\n\tconst requiredCounts = /* @__PURE__ */ new Map();\n\tlet objectVariants = 0;\n\tfor (const entry of variants) {\n\t\tif (!entry || typeof entry !== \"object\") continue;\n\t\tconst props = entry.properties;\n\t\tif (!props || typeof props !== \"object\") continue;\n\t\tobjectVariants += 1;\n\t\tfor (const [key, value] of Object.entries(props)) {\n\t\t\tif (!(key in mergedProperties)) {\n\t\t\t\tmergedProperties[key] = value;\n\t\t\t\tcontinue;\n\t\t\t}\n\t\t\tmergedProperties[key] = mergePropertySchemas(mergedProperties[key], value);\n\t\t}\n\t\tconst required = Array.isArray(entry.required) ? entry.required : [];\n\t\tfor (const key of required) {\n--\n\t\t...mergedRequired && mergedRequired.length > 0 ? { required: mergedRequired } : {},\n\t\tadditionalProperties: \"additionalProperties\" in schemaRecord ? schemaRecord.additionalProperties : true\n\t});\n}\n//#endregion\nexport { normalizeToolParameterSchema as t };"}],"details":{"status":"completed","exitCode":0,"durationMs":183,"aggregated":"changed = true;\n\t\t}\n\t}\n\treturn changed ? nextSchema : schema;\n}\nfunction normalizeToolParameterSchema(schema, options) {\n\tconst schemaRecord = schema && typeof schema === \"object\" ? schema : void 0;\n\tif (!schemaRecord) return schema;\n\tconst normalizedProvider = normalizeLowercaseStringOrEmpty(options?.modelProvider);\n\tconst isGeminiProvider = normalizedProvider.includes(\"google\") || normalizedProvider.includes(\"gemini\");\n\tconst isAnthropicProvider = normalizedProvider.includes(\"anthropic\");\n\tconst unsupportedToolSchemaKeywords = resolveUnsupportedToolSchemaKeywords(options?.modelCompat);\n\tfunction applyProviderCleaning(s) {\n\t\tconst normalizedSchema = normalizeArraySchemasMissingItems(s);\n\t\tif (isGeminiProvider && !isAnthropicProvider) return cleanSchemaForGemini(normalizedSchema);\n\t\tif (unsupportedToolSchemaKeywords.size > 0) return stripUnsupportedSchemaKeywords(normalizedSchema, unsupportedToolSchemaKeywords);\n\t\treturn normalizedSchema;\n\t}\n\tconst conditionalKey = getTopLevelConditionalKey(schemaRecord);\n\tconst flattenableVariantKey = getFlattenableVariantKey(schemaRecord);\n\tif (hasTopLevelObjectSchema(schemaRecord, conditionalKey)) return applyProviderCleaning(schemaRecord);\n\tif (isObjectLikeSchemaMissingType(schemaRecord, conditionalKey)) return applyProviderCleaning({\n\t\t...schemaRecord,\n\t\ttype: \"object\",\n\t\tproperties: isSchemaRecord(schemaRecord.properties) ? schemaRecord.properties : {}\n\t});\n\tif (isTypedObjectSchemaMissingValidProperties(schemaRecord, conditionalKey)) return applyProviderCleaning({\n\t\t...schemaRecord,\n\t\tproperties: {}\n\t});\n\tif (!flattenableVariantKey) {\n\t\tif (isTrulyEmptySchema(schemaRecord)) return applyProviderCleaning({\n\t\t\ttype: \"object\",\n\t\t\tproperties: {}\n\t\t});\n\t\tif (conditionalKey === \"allOf\") return applyProviderCleaning(schema);\n\t\treturn applyProviderCleaning(schema);\n\t}\n\tconst variants = schemaRecord[flattenableVariantKey];\n\tconst mergedProperties = {};\n\tconst requiredCounts = /* @__PURE__ */ new Map();\n\tlet objectVariants = 0;\n\tfor (const entry of variants) {\n\t\tif (!entry || typeof entry !== \"object\") continue;\n\t\tconst props = entry.properties;\n\t\tif (!props || typeof props !== \"object\") continue;\n\t\tobjectVariants += 1;\n\t\tfor (const [key, value] of Object.entries(props)) {\n\t\t\tif (!(key in mergedProperties)) {\n\t\t\t\tmergedProperties[key] = value;\n\t\t\t\tcontinue;\n\t\t\t}\n\t\t\tmergedProperties[key] = mergePropertySchemas(mergedProperties[key], value);\n\t\t}\n\t\tconst required = Array.isArray(entry.required) ? entry.required : [];\n\t\tfor (const key of required) {\n--\n\t\t...mergedRequired && mergedRequired.length > 0 ? { required: mergedRequired } : {},\n\t\tadditionalProperties: \"additionalProperties\" in schemaRecord ? schemaRecord.additionalProperties : true\n\t});\n}\n//#endregion\nexport { normalizeToolParameterSchema as t };","cwd":"/root/.openclaw/workspace/main"},"isError":false,"timestamp":1778990809101}}
{"type":"message","id":"063b1720","parentId":"40cdd1fc","timestamp":"2026-05-17T04:07:05.144Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_0h2ctpyv6k1malfmc7qj751b","name":"exec","arguments":{"command":"# 查看stripUnsupportedSchemaKeywords的实现\ngrep -B 5 -A 40 \"stripUnsupportedSchemaKeywords\" /root/.local/share/pnpm/global/5/.pnpm/openclaw@2026.5.12/node_modules/openclaw/dist/pi-tools-parameter-schema-o0pF8kVc.js 2>/dev/null | head -60"},"partialArgs":"{\"command\": \"# 查看stripUnsupportedSchemaKeywords的实现\\ngrep -B 5 -A 40 \\\"stripUnsupportedSchemaKeywords\\\" /root/.local/share/pnpm/global/5/.pnpm/openclaw@2026.5.12/node_modules/openclaw/dist/pi-tools-parameter-schema-o0pF8kVc.js 2>/dev/null | head -60\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":68336,"output":85,"cacheRead":32640,"cacheWrite":0,"totalTokens":101061,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778990809106,"responseId":"021778990809158f49357381ad84b476d8c8001bb9a349d1161c9"}}
{"type":"message","id":"ed4413d8","parentId":"063b1720","timestamp":"2026-05-17T04:07:05.739Z","message":{"role":"toolResult","toolCallId":"call_0h2ctpyv6k1malfmc7qj751b","toolName":"exec","content":[{"type":"text","text":"import { a as normalizeLowercaseStringOrEmpty } from \"./string-coerce-LndEvhRk.js\";\nimport { s as resolveUnsupportedToolSchemaKeywords } from \"./provider-model-compat-Dk5etUbu.js\";\nimport { c as stripUnsupportedSchemaKeywords, u as cleanSchemaForGemini } from \"./provider-tools-CWTapSmF.js\";\n//#region src/agents/pi-tools-parameter-schema.ts\nfunction extractEnumValues(schema) {\n\tif (!schema || typeof schema !== \"object\") return;\n\tconst record = schema;\n\tif (Array.isArray(record.enum)) return record.enum;\n\tif (\"const\" in record) return [record.const];\n\tconst variants = Array.isArray(record.anyOf) ? record.anyOf : Array.isArray(record.oneOf) ? record.oneOf : null;\n\tif (variants) {\n\t\tconst values = variants.flatMap((variant) => {\n\t\t\treturn extractEnumValues(variant) ?? [];\n\t\t});\n\t\treturn values.length > 0 ? values : void 0;\n\t}\n}\nfunction mergePropertySchemas(existing, incoming) {\n\tif (!existing) return incoming;\n\tif (!incoming) return existing;\n\tconst existingEnum = extractEnumValues(existing);\n\tconst incomingEnum = extractEnumValues(incoming);\n\tif (existingEnum || incomingEnum) {\n\t\tconst values = Array.from(new Set([...existingEnum ?? [], ...incomingEnum ?? []]));\n\t\tconst merged = {};\n\t\tfor (const source of [existing, incoming]) {\n\t\t\tif (!source || typeof source !== \"object\") continue;\n\t\t\tconst record = source;\n\t\t\tfor (const key of [\n\t\t\t\t\"title\",\n\t\t\t\t\"description\",\n\t\t\t\t\"default\"\n\t\t\t]) if (!(key in merged) && key in record) merged[key] = record[key];\n\t\t}\n\t\tconst types = new Set(values.map((value) => typeof value));\n\t\tif (types.size === 1) merged.type = Array.from(types)[0];\n\t\tmerged.enum = values;\n\t\treturn merged;\n\t}\n\treturn existing;\n}\nfunction isSchemaRecord(value) {\n\treturn !!value && typeof value === \"object\" && !Array.isArray(value);\n--\n\tconst isAnthropicProvider = normalizedProvider.includes(\"anthropic\");\n\tconst unsupportedToolSchemaKeywords = resolveUnsupportedToolSchemaKeywords(options?.modelCompat);\n\tfunction applyProviderCleaning(s) {\n\t\tconst normalizedSchema = normalizeArraySchemasMissingItems(s);\n\t\tif (isGeminiProvider && !isAnthropicProvider) return cleanSchemaForGemini(normalizedSchema);\n\t\tif (unsupportedToolSchemaKeywords.size > 0) return stripUnsupportedSchemaKeywords(normalizedSchema, unsupportedToolSchemaKeywords);\n\t\treturn normalizedSchema;\n\t}\n\tconst conditionalKey = getTopLevelConditionalKey(schemaRecord);\n\tconst flattenableVariantKey = getFlattenableVariantKey(schemaRecord);\n\tif (hasTopLevelObjectSchema(schemaRecord, conditionalKey)) return applyProviderCleaning(schemaRecord);\n\tif (isObjectLikeSchemaMissingType(schemaRecord, conditionalKey)) return applyProviderCleaning({\n\t\t...schemaRecord,\n\t\ttype: \"object\",\n\t\tproperties: isSchemaRecord(schemaRecord.properties) ? schemaRecord.properties : {}\n\t});"}],"details":{"status":"completed","exitCode":0,"durationMs":179,"aggregated":"import { a as normalizeLowercaseStringOrEmpty } from \"./string-coerce-LndEvhRk.js\";\nimport { s as resolveUnsupportedToolSchemaKeywords } from \"./provider-model-compat-Dk5etUbu.js\";\nimport { c as stripUnsupportedSchemaKeywords, u as cleanSchemaForGemini } from \"./provider-tools-CWTapSmF.js\";\n//#region src/agents/pi-tools-parameter-schema.ts\nfunction extractEnumValues(schema) {\n\tif (!schema || typeof schema !== \"object\") return;\n\tconst record = schema;\n\tif (Array.isArray(record.enum)) return record.enum;\n\tif (\"const\" in record) return [record.const];\n\tconst variants = Array.isArray(record.anyOf) ? record.anyOf : Array.isArray(record.oneOf) ? record.oneOf : null;\n\tif (variants) {\n\t\tconst values = variants.flatMap((variant) => {\n\t\t\treturn extractEnumValues(variant) ?? [];\n\t\t});\n\t\treturn values.length > 0 ? values : void 0;\n\t}\n}\nfunction mergePropertySchemas(existing, incoming) {\n\tif (!existing) return incoming;\n\tif (!incoming) return existing;\n\tconst existingEnum = extractEnumValues(existing);\n\tconst incomingEnum = extractEnumValues(incoming);\n\tif (existingEnum || incomingEnum) {\n\t\tconst values = Array.from(new Set([...existingEnum ?? [], ...incomingEnum ?? []]));\n\t\tconst merged = {};\n\t\tfor (const source of [existing, incoming]) {\n\t\t\tif (!source || typeof source !== \"object\") continue;\n\t\t\tconst record = source;\n\t\t\tfor (const key of [\n\t\t\t\t\"title\",\n\t\t\t\t\"description\",\n\t\t\t\t\"default\"\n\t\t\t]) if (!(key in merged) && key in record) merged[key] = record[key];\n\t\t}\n\t\tconst types = new Set(values.map((value) => typeof value));\n\t\tif (types.size === 1) merged.type = Array.from(types)[0];\n\t\tmerged.enum = values;\n\t\treturn merged;\n\t}\n\treturn existing;\n}\nfunction isSchemaRecord(value) {\n\treturn !!value && typeof value === \"object\" && !Array.isArray(value);\n--\n\tconst isAnthropicProvider = normalizedProvider.includes(\"anthropic\");\n\tconst unsupportedToolSchemaKeywords = resolveUnsupportedToolSchemaKeywords(options?.modelCompat);\n\tfunction applyProviderCleaning(s) {\n\t\tconst normalizedSchema = normalizeArraySchemasMissingItems(s);\n\t\tif (isGeminiProvider && !isAnthropicProvider) return cleanSchemaForGemini(normalizedSchema);\n\t\tif (unsupportedToolSchemaKeywords.size > 0) return stripUnsupportedSchemaKeywords(normalizedSchema, unsupportedToolSchemaKeywords);\n\t\treturn normalizedSchema;\n\t}\n\tconst conditionalKey = getTopLevelConditionalKey(schemaRecord);\n\tconst flattenableVariantKey = getFlattenableVariantKey(schemaRecord);\n\tif (hasTopLevelObjectSchema(schemaRecord, conditionalKey)) return applyProviderCleaning(schemaRecord);\n\tif (isObjectLikeSchemaMissingType(schemaRecord, conditionalKey)) return applyProviderCleaning({\n\t\t...schemaRecord,\n\t\ttype: \"object\",\n\t\tproperties: isSchemaRecord(schemaRecord.properties) ? schemaRecord.properties : {}\n\t});","cwd":"/root/.openclaw/workspace/main"},"isError":false,"timestamp":1778990825739}}
{"type":"message","id":"bf86849b","parentId":"ed4413d8","timestamp":"2026-05-17T04:07:37.171Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now I understand the flow. OpenClaw has `unsupportedToolSchemaKeywords` in the mimo-v2.5 compat config that includes `additionalProperties`, `oneOf`, `anyOf`, `allOf`, `$ref`. This means OpenClaw's `stripUnsupportedSchemaKeywords` function will strip those keywords from tool schemas before sending to the API.\n\nBut the API itself works fine with those keywords (as my test showed). The issue might be something else.\n\nLet me think about what \"没有反应\" means. The user says ark-code-latest works fine but mimo-v2.5 has no response. Let me check:\n\n1. The default model for agents is `xiaomi-coding/mimo-v2.5` (agents.defaults.model.primary)\n2. Writer agent uses `xiaomi-coding/mimo-v2.5`\n3. Publisher agent uses `xiaomi-coding/mimo-v2.5`\n4. The current session (main) uses `arkcode/ark-code-latest` (which works)\n\nThe user is asking why when calling mimo-v2.5 (presumably for writer/publisher), there's no response.\n\nLet me check the writer/publisher session logs more carefully.","thinkingSignature":"reasoning_content"},{"type":"toolCall","id":"call_ylixhh96r9nvz4yq7n1dyod2","name":"exec","arguments":{"command":"# 查看最近writer和publisher agent的session和日志\nsessions_output=$(ls -lt /root/.openclaw/agents/writer/sessions/ 2>/dev/null | head -10)\necho \"=== Writer sessions ===\"\necho \"$sessions_output\"\necho \"\"\necho \"=== Publisher sessions ===\"\nls -lt /root/.openclaw/agents/publisher/sessions/ 2>/dev/null | head -10\necho \"\"\necho \"=== Writer logs (last errors) ===\"\njournalctl --user -u openclaw-gateway --since \"2 hours ago\" --no-pager 2>/dev/null | grep -i \"agent:writer\" | tail -20\necho \"\"\necho \"=== All agent runs with mimo ===\"\njournalctl --user -u openclaw-gateway --since \"2 hours ago\" --no-pager 2>/dev/null | grep -E \"agent.*run|embedded.*run|model_call\" | tail -30"},"partialArgs":"{\"command\": \"# 查看最近writer和publisher agent的session和日志\\nsessions_output=$(ls -lt /root/.openclaw/agents/writer/sessions/ 2>/dev/null | head -10)\\necho \\\"=== Writer sessions ===\\\"\\necho \\\"$sessions_output\\\"\\necho \\\"\\\"\\necho \\\"=== Publisher sessions ===\\\"\\nls -lt /root/.openclaw/agents/publisher/sessions/ 2>/dev/null | head -10\\necho \\\"\\\"\\necho \\\"=== Writer logs (last errors) ===\\\"\\njournalctl --user -u openclaw-gateway --since \\\"2 hours ago\\\" --no-pager 2>/dev/null | grep -i \\\"agent:writer\\\" | tail -20\\necho \\\"\\\"\\necho \\\"=== All agent runs with mimo ===\\\"\\njournalctl --user -u openclaw-gateway --since \\\"2 hours ago\\\" --no-pager 2>/dev/null | grep -E \\\"agent.*run|embedded.*run|model_call\\\" | tail -30\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":1474,"output":459,"cacheRead":100224,"cacheWrite":0,"totalTokens":102157,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778990825743,"responseId":"021778990825790081222809c6b24dcd35df0bef1acd71d6eaeb8"}}
{"type":"message","id":"1203ba75","parentId":"bf86849b","timestamp":"2026-05-17T04:07:38.987Z","message":{"role":"toolResult","toolCallId":"call_ylixhh96r9nvz4yq7n1dyod2","toolName":"exec","content":[{"type":"text","text":"=== Writer sessions ===\ntotal 2360\n-rw------- 1 root root 210277 May 16 21:19 sessions.json\n-rw------- 1 root root 204100 May 16 21:19 6a38349f-1c5f-476a-9f15-d9016863c6f2.trajectory.jsonl\n-rw-r--r-- 1 root root  24374 May 16 21:19 6a38349f-1c5f-476a-9f15-d9016863c6f2.jsonl\n-rw------- 1 root root    241 May 16 21:18 6a38349f-1c5f-476a-9f15-d9016863c6f2.trajectory-path.json\n-rw------- 1 root root 202589 May 16 20:55 aefa5c64-497c-4805-b7b9-976d85790d0a.trajectory.jsonl\n-rw-r--r-- 1 root root  23062 May 16 20:55 aefa5c64-497c-4805-b7b9-976d85790d0a.jsonl\n-rw------- 1 root root    241 May 16 20:54 aefa5c64-497c-4805-b7b9-976d85790d0a.trajectory-path.json\n-rw------- 1 root root 184991 May 15 18:00 18d68884-87e7-4872-a255-83399fbd0542.trajectory.jsonl\n-rw-r--r-- 1 root root  17557 May 15 18:00 18d68884-87e7-4872-a255-83399fbd0542.jsonl\n\n=== Publisher sessions ===\ntotal 1228\n-rw------- 1 root root 105200 May 17 10:18 sessions.json\n-rw------- 1 root root 155053 May 17 10:18 3078dd1d-542f-4bb9-b919-da89d50edae1.trajectory.jsonl\n-rw-r--r-- 1 root root   6530 May 17 10:18 3078dd1d-542f-4bb9-b919-da89d50edae1.jsonl\n-rw------- 1 root root    244 May 17 10:17 3078dd1d-542f-4bb9-b919-da89d50edae1.trajectory-path.json\n-rw------- 1 root root 164393 May 16 20:47 a1daa665-d3c4-4f8b-89b1-b238f0db4d1d.trajectory.jsonl\n-rw-r--r-- 1 root root  10551 May 16 20:47 a1daa665-d3c4-4f8b-89b1-b238f0db4d1d.jsonl\n-rw------- 1 root root    244 May 16 20:46 a1daa665-d3c4-4f8b-89b1-b238f0db4d1d.trajectory-path.json\n-rw------- 1 root root 156122 May 16 20:33 706de384-9da1-4ab9-ac33-2322c765abe7.trajectory.jsonl\n-rw-r--r-- 1 root root   8394 May 16 20:33 706de384-9da1-4ab9-ac33-2322c765abe7.jsonl\n\n=== Writer logs (last errors) ===\n\n=== All agent runs with mimo ===\nMay 17 10:07:38 VM-0-12-ubuntu node[2456037]: 2026-05-17T10:07:38.760+08:00 [diagnostic] long-running session: sessionId=aa89ef19-c553-4497-bc3b-6f95839d208a sessionKey=agent:cto:main state=processing age=145s queueDepth=1 reason=queued_behind_active_work classification=long_running activeWorkKind=model_call lastProgress=model_call:started lastProgressAge=8s recovery=none\nMay 17 10:13:08 VM-0-12-ubuntu node[2456037]: 2026-05-17T10:13:08.766+08:00 [diagnostic] liveness warning: reasons=event_loop_delay interval=30s eventLoopDelayP99Ms=21.7 eventLoopDelayMaxMs=2239.8 eventLoopUtilization=0.132 cpuCoreRatio=0.173 active=1 waiting=0 queued=1 phase=channels.openclaw-weixin.start-account recentPhases=sidecars.main-session-recovery:4ms,sidecars.restart-sentinel:63ms,post-attach.update-sentinel:50ms,sidecars.session-locks:1358ms,post-ready.maintenance:80ms,sidecars.model-prewarm:3867ms work=[active=agent:cto:main(processing/model_call,q=1,age=3s last=model_call:started) queued=agent:cto:main(processing/model_call,q=1,age=3s last=model_call:started)]\nMay 17 10:15:08 VM-0-12-ubuntu node[2456037]: 2026-05-17T10:15:08.767+08:00 [diagnostic] long-running session: sessionId=aa89ef19-c553-4497-bc3b-6f95839d208a sessionKey=agent:cto:main state=processing age=123s queueDepth=1 reason=queued_behind_active_work classification=long_running activeWorkKind=model_call lastProgress=model_call:started lastProgressAge=5s recovery=none\nMay 17 10:17:38 VM-0-12-ubuntu node[2456037]: 2026-05-17T10:17:38.767+08:00 [diagnostic] long-running session: sessionId=aa89ef19-c553-4497-bc3b-6f95839d208a sessionKey=agent:cto:main state=processing age=273s queueDepth=1 reason=queued_behind_active_work classification=long_running activeWorkKind=model_call lastProgress=model_call:started lastProgressAge=8s recovery=none\nMay 17 10:18:08 VM-0-12-ubuntu node[2456037]: 2026-05-17T10:18:08.768+08:00 [diagnostic] liveness warning: reasons=event_loop_delay interval=30s eventLoopDelayP99Ms=728.8 eventLoopDelayMaxMs=7134.5 eventLoopUtilization=0.761 cpuCoreRatio=0.824 active=2 waiting=0 queued=1 phase=channels.openclaw-weixin.start-account recentPhases=sidecars.main-session-recovery:4ms,sidecars.restart-sentinel:63ms,post-attach.update-sentinel:50ms,sidecars.session-locks:1358ms,post-ready.maintenance:80ms,sidecars.model-prewarm:3867ms work=[active=agent:cto:main(processing/model_call,q=1,age=303s last=model_call:started)|agent:publisher:openai:3bbabaea-b27b-4e59-b7c2-47556bb57b45(processing/model_call,q=0,age=17s last=model_call:started) queued=agent:cto:main(processing/model_call,q=1,age=303s last=model_call:started)]\nMay 17 10:18:29 VM-0-12-ubuntu node[2456037]: 2026-05-17T10:18:29.755+08:00 [agent/embedded] agent cleanup timed out: runId=chatcmpl_ab36d31e-8758-43d8-8cfd-aa320fc030f7 sessionId=3078dd1d-542f-4bb9-b919-da89d50edae1 step=pi-trajectory-flush timeoutMs=10000\nMay 17 10:22:28 VM-0-12-ubuntu node[2456037]: 2026-05-17T10:22:28.953+08:00 [diagnostic] long-running session: sessionId=aa89ef19-c553-4497-bc3b-6f95839d208a sessionKey=agent:cto:main state=processing age=563s queueDepth=1 reason=queued_behind_active_work classification=long_running activeWorkKind=model_call lastProgress=model_call:started lastProgressAge=16s recovery=none\nMay 17 10:39:46 VM-0-12-ubuntu node[2456037]: 2026-05-17T10:39:46.815+08:00 [diagnostic] long-running session: sessionId=aa89ef19-c553-4497-bc3b-6f95839d208a sessionKey=agent:cto:main state=processing age=146s queueDepth=1 reason=queued_behind_active_work classification=long_running activeWorkKind=model_call lastProgress=model_call:started lastProgressAge=11s recovery=none\nMay 17 10:42:16 VM-0-12-ubuntu node[2456037]: 2026-05-17T10:42:16.815+08:00 [diagnostic] long-running session: sessionId=aa89ef19-c553-4497-bc3b-6f95839d208a sessionKey=agent:cto:main state=processing age=296s queueDepth=1 reason=queued_behind_active_work classification=long_running activeWorkKind=model_call lastProgress=model_call:started lastProgressAge=22s recovery=none\nMay 17 10:47:16 VM-0-12-ubuntu node[2456037]: 2026-05-17T10:47:16.928+08:00 [diagnostic] long-running session: sessionId=aa89ef19-c553-4497-bc3b-6f95839d208a sessionKey=agent:cto:main state=processing age=596s queueDepth=1 reason=queued_behind_active_work classification=long_running activeWorkKind=model_call lastProgress=model_call:started lastProgressAge=0s recovery=none\nMay 17 11:14:20 VM-0-12-ubuntu node[2456037]: 2026-05-17T11:14:20.024+08:00 [diagnostic] long-running session: sessionId=aa89ef19-c553-4497-bc3b-6f95839d208a sessionKey=agent:cto:main state=processing age=123s queueDepth=1 reason=queued_behind_active_work classification=long_running activeWorkKind=model_call lastProgress=model_call:started lastProgressAge=2s recovery=none\nMay 17 11:16:50 VM-0-12-ubuntu node[2456037]: 2026-05-17T11:16:50.026+08:00 [diagnostic] long-running session: sessionId=aa89ef19-c553-4497-bc3b-6f95839d208a sessionKey=agent:cto:main state=processing age=273s queueDepth=1 reason=queued_behind_active_work classification=long_running activeWorkKind=model_call lastProgress=model_call:started lastProgressAge=7s recovery=none\nMay 17 11:35:50 VM-0-12-ubuntu node[2456037]: 2026-05-17T11:35:50.033+08:00 [diagnostic] long-running session: sessionId=aa89ef19-c553-4497-bc3b-6f95839d208a sessionKey=agent:cto:main state=processing age=140s queueDepth=1 reason=queued_behind_active_work classification=long_running activeWorkKind=model_call lastProgress=model_call:started lastProgressAge=7s recovery=none\nMay 17 11:37:57 VM-0-12-ubuntu node[2456037]: 2026-05-17T11:37:57.624+08:00 [diagnostic] liveness warning: reasons=event_loop_delay,cpu interval=38s eventLoopDelayP99Ms=6744.4 eventLoopDelayMaxMs=27464.3 eventLoopUtilization=0.932 cpuCoreRatio=0.996 active=2 waiting=0 queued=2 phase=channels.openclaw-weixin.start-account recentPhases=sidecars.main-session-recovery:4ms,sidecars.restart-sentinel:63ms,post-attach.update-sentinel:50ms,sidecars.session-locks:1358ms,post-ready.maintenance:80ms,sidecars.model-prewarm:3867ms work=[active=agent:cto:main(processing/model_call,q=1,age=268s last=model_call:started)|agent:main:main(processing,q=1,age=34s) queued=agent:cto:main(processing/model_call,q=1,age=268s last=model_call:started)|agent:main:main(processing,q=1,age=34s)]\nMay 17 11:38:27 VM-0-12-ubuntu node[2456037]: 2026-05-17T11:38:27.625+08:00 [diagnostic] long-running session: sessionId=aa89ef19-c553-4497-bc3b-6f95839d208a sessionKey=agent:cto:main state=processing age=298s queueDepth=1 reason=queued_behind_active_work classification=long_running activeWorkKind=model_call lastProgress=model_call:started lastProgressAge=2s recovery=none\nMay 17 11:40:27 VM-0-12-ubuntu node[2456037]: 2026-05-17T11:40:27.630+08:00 [diagnostic] liveness warning: reasons=event_loop_delay interval=30s eventLoopDelayP99Ms=21.4 eventLoopDelayMaxMs=1432.4 eventLoopUtilization=0.174 cpuCoreRatio=0.182 active=2 waiting=0 queued=2 phase=channels.openclaw-weixin.start-account recentPhases=sidecars.main-session-recovery:4ms,sidecars.restart-sentinel:63ms,post-attach.update-sentinel:50ms,sidecars.session-locks:1358ms,post-ready.maintenance:80ms,sidecars.model-prewarm:3867ms work=[active=agent:cto:main(processing/model_call,q=1,age=418s last=model_call:started)|agent:main:main(processing/model_call,q=1,age=131s last=model_call:started) queued=agent:cto:main(processing/model_call,q=1,age=418s last=model_call:started)|agent:main:main(processing/model_call,q=1,age=131s last=model_call:started)]\nMay 17 11:40:27 VM-0-12-ubuntu node[2456037]: 2026-05-17T11:40:27.633+08:00 [diagnostic] long-running session: sessionId=ca32045a-92f6-410f-a707-590520f1e2d7 sessionKey=agent:main:main state=processing age=131s queueDepth=1 reason=queued_behind_active_work classification=long_running activeWorkKind=model_call lastProgress=model_call:started lastProgressAge=16s recovery=none\nMay 17 11:42:27 VM-0-12-ubuntu node[2456037]: 2026-05-17T11:42:27.633+08:00 [diagnostic] liveness warning: reasons=event_loop_delay interval=30s eventLoopDelayP99Ms=21.6 eventLoopDelayMaxMs=1214.3 eventLoopUtilization=0.113 cpuCoreRatio=0.12 active=2 waiting=0 queued=2 phase=channels.openclaw-weixin.start-account recentPhases=sidecars.main-session-recovery:4ms,sidecars.restart-sentinel:63ms,post-attach.update-sentinel:50ms,sidecars.session-locks:1358ms,post-ready.maintenance:80ms,sidecars.model-prewarm:3867ms work=[active=agent:cto:main(processing/tool_call,q=1,age=538s last=tool:browser:started)|agent:main:main(processing/model_call,q=1,age=251s last=model_call:started) queued=agent:cto:main(processing/tool_call,q=1,age=538s last=tool:browser:started)|agent:main:main(processing/model_call,q=1,age=251s last=model_call:started)]\nMay 17 11:42:57 VM-0-12-ubuntu node[2456037]: 2026-05-17T11:42:57.640+08:00 [diagnostic] long-running session: sessionId=ca32045a-92f6-410f-a707-590520f1e2d7 sessionKey=agent:main:main state=processing age=281s queueDepth=1 reason=queued_behind_active_work classification=long_running activeWorkKind=model_call lastProgress=model_call:started lastProgressAge=58s recovery=none\nMay 17 11:47:57 VM-0-12-ubuntu node[2456037]: 2026-05-17T11:47:57.648+08:00 [diagnostic] long-running session: sessionId=ca32045a-92f6-410f-a707-590520f1e2d7 sessionKey=agent:main:main state=processing age=581s queueDepth=1 reason=queued_behind_active_work classification=long_running activeWorkKind=model_call lastProgress=model_call:started lastProgressAge=42s recovery=none\nMay 17 11:50:57 VM-0-12-ubuntu node[2456037]: 2026-05-17T11:50:57.742+08:00 [diagnostic] stalled session: sessionId=ca32045a-92f6-410f-a707-590520f1e2d7 sessionKey=agent:main:main state=processing age=761s queueDepth=1 reason=active_work_without_progress classification=stalled_agent_run activeWorkKind=model_call lastProgress=model_call:started lastProgressAge=142s recovery=none\nMay 17 11:54:27 VM-0-12-ubuntu node[2456037]: 2026-05-17T11:54:27.838+08:00 [diagnostic] long-running session: sessionId=aa89ef19-c553-4497-bc3b-6f95839d208a sessionKey=agent:cto:main state=processing age=139s queueDepth=1 reason=queued_behind_active_work classification=long_running activeWorkKind=model_call lastProgress=model_call:started lastProgressAge=39s recovery=none\nMay 17 11:56:57 VM-0-12-ubuntu node[2456037]: 2026-05-17T11:56:57.840+08:00 [diagnostic] long-running session: sessionId=aa89ef19-c553-4497-bc3b-6f95839d208a sessionKey=agent:cto:main state=processing age=289s queueDepth=1 reason=queued_behind_active_work classification=long_running activeWorkKind=tool_call lastProgress=tool:exec:started lastProgressAge=9s activeTool=exec activeToolCallId=call_1e13a21c7114407b89c4dcb3 activeToolAge=9s recovery=none\nMay 17 12:00:44 VM-0-12-ubuntu node[2456037]: 2026-05-17T12:00:44.786+08:00 [diagnostic] liveness warning: reasons=event_loop_delay interval=47s eventLoopDelayP99Ms=185.7 eventLoopDelayMaxMs=27514.6 eventLoopUtilization=0.799 cpuCoreRatio=0.866 active=2 waiting=0 queued=1 phase=channels.openclaw-weixin.start-account recentPhases=sidecars.main-session-recovery:4ms,sidecars.restart-sentinel:63ms,post-attach.update-sentinel:50ms,sidecars.session-locks:1358ms,post-ready.maintenance:80ms,sidecars.model-prewarm:3867ms work=[active=agent:main:main(processing,q=1,age=34s last=run:completed)|agent:cto:cron:7476feec-bd3a-4ff9-8e06-2cd6e67e38a5:run:57a505b3-d7be-47c4-b596-d62b3ba55ad8(processing/model_call,q=0,age=42s last=model_call:started) queued=agent:main:main(processing,q=1,age=34s last=run:completed)]\nMay 17 12:02:06 VM-0-12-ubuntu node[2456037]: 2026-05-17T12:02:06.718+08:00 [agent/embedded] embedded run agent end: runId=57a505b3-d7be-47c4-b596-d62b3ba55ad8 isError=true model=astron-code-latest provider=astroncodingplan error=Xunfei request failed with Sid: cht000b8ad2@dx19e34175b9aba5a700 code: 10010, msg: RecvFromEngineError:Engine Busy, timeStamp:12:02:06.663 rawError=Xunfei request failed with Sid: cht000b8ad2@dx19e34175b9aba5a700 code: 10010, msg: RecvFromEngineError:Engine Busy, timeStamp:12:02:06.663\nMay 17 12:02:55 VM-0-12-ubuntu node[2456037]: 2026-05-17T12:02:55.773+08:00 [diagnostic] liveness warning: reasons=event_loop_delay interval=30s eventLoopDelayP99Ms=23 eventLoopDelayMaxMs=12247.4 eventLoopUtilization=0.493 cpuCoreRatio=0.554 active=2 waiting=0 queued=1 phase=channels.openclaw-weixin.start-account recentPhases=sidecars.main-session-recovery:4ms,sidecars.restart-sentinel:63ms,post-attach.update-sentinel:50ms,sidecars.session-locks:1358ms,post-ready.maintenance:80ms,sidecars.model-prewarm:3867ms work=[active=agent:main:main(processing/model_call,q=1,age=115s last=model_call:started)|agent:cto:cron:unified-health-check:run:555384c6-45f6-434e-b826-be271a70a01c(processing/model_call,q=0,age=16s last=model_call:started) queued=agent:main:main(processing/model_call,q=1,age=115s last=model_call:started)]\nMay 17 12:03:25 VM-0-12-ubuntu node[2456037]: 2026-05-17T12:03:25.774+08:00 [diagnostic] long-running session: sessionId=ca32045a-92f6-410f-a707-590520f1e2d7 sessionKey=agent:main:main state=processing age=145s queueDepth=1 reason=queued_behind_active_work classification=long_running activeWorkKind=model_call lastProgress=model_call:started lastProgressAge=14s recovery=none\nMay 17 12:05:55 VM-0-12-ubuntu node[2456037]: 2026-05-17T12:05:55.776+08:00 [diagnostic] long-running session: sessionId=ca32045a-92f6-410f-a707-590520f1e2d7 sessionKey=agent:main:main state=processing age=295s queueDepth=1 reason=queued_behind_active_work classification=long_running activeWorkKind=model_call lastProgress=model_call:started lastProgressAge=14s recovery=none[... 834 more characters truncated]"}],"details":{"persistedDetailsTruncated":true,"originalDetailsBytesAtLeast":8193,"originalDetailKeys":["status","exitCode","durationMs","aggregated","cwd"],"status":"completed","cwd":"/root/.openclaw/workspace/main","exitCode":0},"isError":false,"timestamp":1778990858986}}
{"type":"message","id":"8bb758a8","parentId":"1203ba75","timestamp":"2026-05-17T04:08:06.275Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_4xtn6rrkh7dh3nz2wllwwk8c","name":"exec","arguments":{"command":"# 搜索mimo相关的错误/超时/失败\njournalctl --user -u openclaw-gateway --since \"24 hours ago\" --no-pager 2>/dev/null | grep -i -E \"mimo|xiaomi|token-plan\" | grep -i -E \"error|fail|timeout|reject|retry|abort\" | tail -20\necho \"=== mimo completions ===\"\njournalctl --user -u openclaw-gateway --since \"24 hours ago\" --no-pager 2>/dev/null | grep -i -E \"mimo.*completions|mimo.*chat|mimo.*response\" | tail -20\necho \"=== timeout ===\"\njournalctl --user -u openclaw-gateway --since \"24 hours ago\" --no-pager 2>/dev/null | grep -i \"timeout\" | tail -20"},"partialArgs":"{\"command\": \"# 搜索mimo相关的错误/超时/失败\\njournalctl --user -u openclaw-gateway --since \\\"24 hours ago\\\" --no-pager 2>/dev/null | grep -i -E \\\"mimo|xiaomi|token-plan\\\" | grep -i -E \\\"error|fail|timeout|reject|retry|abort\\\" | tail -20\\necho \\\"=== mimo completions ===\\\"\\njournalctl --user -u openclaw-gateway --since \\\"24 hours ago\\\" --no-pager 2>/dev/null | grep -i -E \\\"mimo.*completions|mimo.*chat|mimo.*response\\\" | tail -20\\necho \\\"=== timeout ===\\\"\\njournalctl --user -u openclaw-gateway --since \\\"24 hours ago\\\" --no-pager 2>/dev/null | grep -i \\\"timeout\\\" | tail -20\"}"}],"api":"openai-completions","provider":"arkcode","model":"ark-code-latest","usage":{"input":77267,"output":194,"cacheRead":30592,"cacheWrite":0,"totalTokens":108053,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1778990858992,"responseId":"0217789908601519badbe816172dbb331753bd21c11e8fa4ed25d"}}
{"type":"message","id":"d91737bb","parentId":"8bb758a8","timestamp":"2026-05-17T04:08:07.843Z","message":{"role":"toolResult","toolCallId":"call_4xtn6rrkh7dh3nz2wllwwk8c","toolName":"exec","content":[{"type":"text","text":"=== mimo completions ===\n=== timeout ===\nMay 16 19:23:33 VM-0-12-ubuntu node[2122957]: 2026-05-16T19:23:33.252+08:00 [tools] browser failed: existing-session evaluate does not support timeoutMs overrides. raw_params={\"action\":\"act\",\"fn\":\"async () => {\\n const taskId = 'task_20260515_175454_79d283f3';\\n const resp = await fetch(`/api/tasks/${taskId}/status`);\\n const data = await resp.json();\\n updateTaskInfo(data.task);\\n return 'done';\\n}\",\"kind\":\"evaluate\",\"targetId\":\"1\",\"timeoutMs\":5000}\nMay 16 21:21:09 VM-0-12-ubuntu node[2122957]: 2026-05-16T21:21:09.594+08:00 [fetch-timeout] fetch timeout after 120000ms (elapsed 120062ms) operation=fetchWithSsrFGuard url=https://maas-coding-api.cn-huabei-1.xf-yun.com/v2/chat/completions\nMay 16 21:21:09 VM-0-12-ubuntu node[2122957]: 2026-05-16T21:21:09.656+08:00 [agent/embedded] [timeout-compaction] LLM timed out with high prompt token usage (88%); attempting compaction before retry (attempt 1/2) diagId=ovf-mp8dk2s5-xYsiOQ\nMay 16 21:21:09 VM-0-12-ubuntu node[2122957]: 2026-05-16T21:21:09.688+08:00 [provider-transport-fetch] [model-fetch] error provider=astroncodingplan api=openai-completions model=astron-code-latest elapsedMs=120158 name=TimeoutError code=undefined causeName=undefined causeCode=undefined message=request timed out\nMay 16 21:22:16 VM-0-12-ubuntu node[2122957]: 2026-05-16T21:22:16.020+08:00 [agent/embedded] [timeout-compaction] compaction succeeded for astroncodingplan/astron-code-latest; retrying prompt\nMay 17 03:01:44 VM-0-12-ubuntu systemd[3335]: openclaw-gateway.service: Failed with result 'timeout'.\nMay 17 10:17:14 VM-0-12-ubuntu node[2456037]: 2026-05-17T10:17:14.431+08:00 [tools] browser failed: timed out. The browser profile is external to OpenClaw; make sure its browser/CDP endpoint is running and reachable. Restarting the OpenClaw gateway will not launch it. raw_params={\"action\":\"navigate\",\"url\":\"http://www.lzycloud.cloud:8000/static/user_driven_coordinator_final.html\",\"timeoutMs\":15000}\nMay 17 10:18:29 VM-0-12-ubuntu node[2456037]: 2026-05-17T10:18:29.755+08:00 [agent/embedded] agent cleanup timed out: runId=chatcmpl_ab36d31e-8758-43d8-8cfd-aa320fc030f7 sessionId=3078dd1d-542f-4bb9-b919-da89d50edae1 step=pi-trajectory-flush timeoutMs=10000"}],"details":{"status":"completed","exitCode":0,"durationMs":732,"aggregated":"=== mimo completions ===\n=== timeout ===\nMay 16 19:23:33 VM-0-12-ubuntu node[2122957]: 2026-05-16T19:23:33.252+08:00 [tools] browser failed: existing-session evaluate does not support timeoutMs overrides. raw_params={\"action\":\"act\",\"fn\":\"async () => {\\n const taskId = 'task_20260515_175454_79d283f3';\\n const resp = await fetch(`/api/tasks/${taskId}/status`);\\n const data = await resp.json();\\n updateTaskInfo(data.task);\\n return 'done';\\n}\",\"kind\":\"evaluate\",\"targetId\":\"1\",\"timeoutMs\":5000}\nMay 16 21:21:09 VM-0-12-ubuntu node[2122957]: 2026-05-16T21:21:09.594+08:00 [fetch-timeout] fetch timeout after 120000ms (elapsed 120062ms) operation=fetchWithSsrFGuard url=https://maas-coding-api.cn-huabei-1.xf-yun.com/v2/chat/completions\nMay 16 21:21:09 VM-0-12-ubuntu node[2122957]: 2026-05-16T21:21:09.656+08:00 [agent/embedded] [timeout-compaction] LLM timed out with high prompt token usage (88%); attempting compaction before retry (attempt 1/2) diagId=ovf-mp8dk2s5-xYsiOQ\nMay 16 21:21:09 VM-0-12-ubuntu node[2122957]: 2026-05-16T21:21:09.688+08:00 [provider-transport-fetch] [model-fetch] error provider=astroncodingplan api=openai-completions model=astron-code-latest elapsedMs=120158 name=TimeoutError code=undefined causeName=undefined causeCode=undefined message=request timed out\nMay 16 21:22:16 VM-0-12-ubuntu node[2122957]: 2026-05-16T21:22:16.020+08:00 [agent/embedded] [timeout-compaction] compaction succeeded for astroncodingplan/astron-code-latest; retrying prompt\nMay 17 03:01:44 VM-0-12-ubuntu systemd[3335]: openclaw-gateway.service: Failed with result 'timeout'.\nMay 17 10:17:14 VM-0-12-ubuntu node[2456037]: 2026-05-17T10:17:14.431+08:00 [tools] browser failed: timed out. The browser profile is external to OpenClaw; make sure its browser/CDP endpoint is running and reachable. Restarting the OpenClaw gateway will not launch it. raw_params={\"action\":\"navigate\",\"url\":\"http://www.lzycloud.cloud:8000/static/user_driven_coordinator_final.html\",\"timeoutMs\":15000}\nMay 17 10:18:29 VM-0-12-ubuntu node[2456037]: 2026-05-17T10:18:29.755+08:00 [agent/embedded] agent cleanup timed out: runId=chatcmpl_ab36d31e-8758-43d8-8cfd-aa320fc030f7 sessionId=3078dd1d-542f-4bb9-b919-da89d50edae1 step=pi-trajectory-flush timeoutMs=10000","cwd":"/root/.openclaw/workspace/main"},"isError":false,"timestamp":1778990887843}}