chore: fix .gitignore paths and purge 269 runtime files from git cache

- Fixed .gitignore: data/system/ prefix was missing (old paths never matched) - Removed tracked memory/, conversations/, uploads/, .tasks/ files - Only memory.md and hippocampus.md are kept (template skeleton)

chore: fix .gitignore paths and purge 269 runtime files from git cache
- Fixed .gitignore: data/system/ prefix was missing (old paths never matched) - Removed tracked memory/, conversations/, uploads/, .tasks/ files - Only memory.md and hippocampus.md are kept (template skeleton)
yibo
1 parent 110869fb
Showing 270 changed files with 15 additions and 25390 deletions
.gitignore
data/system/agents/.tasks/T-mnbu7kcy.json
data/system/agents/.tasks/T-mnbuaomt.json
data/system/agents/.tasks/T-mnbudwvk.json
data/system/agents/.tasks/T-mnbue6qb.json
data/system/agents/.tasks/T-mnbuer1r.json
data/system/agents/.tasks/T-mnbvk2xx.json
data/system/agents/.tasks/T-mnbvndl4.json
data/system/agents/.tasks/T-mnbvretc.json
data/system/agents/.tasks/T-mnbvsnda.json
data/system/agents/.tasks/T-mncavo9p.json
data/system/agents/.tasks/T-mncl1sti.json
data/system/agents/.tasks/T-mncl5hxs.json
data/system/agents/.tasks/T-mncl612p.json
data/system/agents/.tasks/T-mnclaj64.json
data/system/agents/.tasks/T-mnclb0ik.json
data/system/agents/.tasks/T-mnclclls.json
data/system/agents/.tasks/T-mnclju66.json
data/system/agents/.tasks/T-mncln07j.json
data/system/agents/.tasks/T-mnclnjfq.json
@@ -14,17 +14,23 @@ bot.log
 .logs/
 .data/
  
-# System agents runtime data (platform-shared, root-level system/)
-system/agents/*/conversations/
-system/agents/*/logs/
-system/agents/*/workspace/
-system/agents/*/uploads/
-system/agents/*/memory/
-system/agents/.tasks/
+# System agents runtime data (under data/system/)
+data/system/agents/*/conversations/
+data/system/agents/*/logs/
+data/system/agents/*/workspace/
+data/system/agents/*/uploads/
+data/system/agents/*/memory/reflections/
+data/system/agents/*/memory/short-term/
+data/system/agents/*/memory/daily/
+data/system/agents/*/memory/cerebellum/
+data/system/agents/*/memory/hippocampus-index.json
+data/system/agents/.tasks/
  
 # System templates runtime data
-system/templates/*/memory/
-system/templates/*/workspace/
+data/system/templates/*/memory/reflections/
+data/system/templates/*/memory/short-term/
+data/system/templates/*/memory/daily/
+data/system/templates/*/memory/hippocampus-index.json
  
 # Company instance data (tenant-specific, under data/)
 data/companies/
-{
-  "taskId": "T-mnbu7kcy",
-  "from": "innovator",
-  "to": "hunter",
-  "task": "对 lemonv_drama 技能进行增强型调研。需要获取：1) interactive-video-selector 选题 JSON 数据结构格式；2) 剧本上传 API 的接口规范（端点、参数、认证方式）。targetAgentId: lemonv_drama。请执行：研究 interactive-video-selector 的 workspace 输出格式 → 查找剧本上传 API 文档 → 确认 user_id=1 的配置方式。产出写入 workspace/lemonv_drama/hunt/",
-  "priority": "P0",
-  "goalId": "G-lemonv-drama-enhance",
-  "nodeId": "G-lemonv-drama-enhance.1",
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mnbu7kcy",
-  "createdAt": "2026-03-29T14:11:13.236Z",
-  "status": "done",
-  "completedAt": "2026-03-29T14:12:55.148Z",
-  "summary": "已完成 lemonv_drama 增强型调研。产出了3个核心信息：1) interactive-video-selector 选题 JSON 结构（train_dataset.json 格式）；2) 剧本上传 API 规范（insert-drama.js 脚本、generate-images.js 生图脚本）；3) user_id=1 配置方式（JSON 显式传入或默认逻辑）。报告写入 workspace/lemonv_drama/hunt/enhance_research.md"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mnbuaomt",
-  "from": "innovator",
-  "to": "digester",
-  "task": "消化 lemonv_drama 的增强调研数据。skillName: lemonv_drama, targetAgentId: lemonv_drama。输入: workspace/hunt/ (含 enhance_research.md)。执行：审查增强调研产出 → 提取API规范和JSON结构 → 整合到现有 benchmark。产出到 workspace/digest/",
-  "priority": "P0",
-  "goalId": "G-lemonv-drama-v6",
-  "nodeId": "G-lemonv-drama-v6.2",
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mnbuaomt",
-  "createdAt": "2026-03-29T14:13:38.742Z",
-  "status": "done",
-  "completedAt": "2026-03-29T14:15:29.056Z",
-  "summary": "消化 lemonv_drama 增强调研数据完成。产出: benchmark_v2.md (在v1基础上整合技术实现维度: 数据格式/ API接口/ 数据库表结构) + knowhow_v2.md (新增技术规范: 数据流转/输入输出格式/环境变量)"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mnbudwvk",
-  "from": "master",
-  "to": "innovator",
-  "task": "【新训练任务】lemonv_drama v6 训练 - 集成 interactive-video-selector<br><br>## 核心需求（必须严格实现）<br>1. 部署到 company 后，要读取 company 的 interactive-video-selector 的 workspace 的结果（JSON格式的选题数据）<br>2. 根据选题结果进行 lemonv_drama 古风互动剧本的撰写<br>3. 生成剧本后上传到远端服务器（需要调用 API）<br>4. user_id 固定设为 1<br><br>## 输入参考<br>- 当前最佳版本: data/system/templates/lemonv_drama/workspace/evolve/v5/skill.md (v5.0.0, 94.25分)<br>- docs 目录: data/system/templates/lemonv_drama/docs/<br>- interactive-video-selector 模板: data/system/templates/interactive-video-selector/<br><br>## 训练要求<br>1. 基于 v5 版本继续迭代，增加读取 interactive-video-selector 结果的能力<br>2. 技能评分 >= 95 分（10分制>=9.5）<br>3. 产出 v6 版本到 workspace/evolve/v6/<br><br>## 输出<br>- v6/skill.md<br>- v6/score.json (包含分数)<br>- 更新 evolve_log.md",
-  "priority": "P1",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mnbudwvk",
-  "createdAt": "2026-03-29T14:16:09.393Z",
-  "status": "dispatched"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mnbue6qb",
-  "from": "innovator",
-  "to": "evolver",
-  "task": "训练 lemonv_drama 的 {skillName} 技能。targetAgentId: lemonv_drama。输入: workspace/digest/ (benchmark_v2.md, good_testdata/) + workspace/hunt/ (knowhow.md, toplist.md)。执行 Small Loop：执行→评分→分析→改写→重复，passThreshold: 90, maxIterations: 10。注意：benchmark_v2.md 新增了技术实现维度（选题读取能力10%+API上传能力10%），需评估候选剧本在这些技术维度上的表现。产出到 workspace/evolve/",
-  "priority": "P0",
-  "goalId": "G-lemonv-drma",
-  "nodeId": "G-lemonv-drma.3",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mnbue6qb",
-  "createdAt": "2026-03-29T14:16:22.165Z",
-  "status": "dispatched"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mnbuer1r",
-  "from": "innovator",
-  "to": "evolver",
-  "task": "验证 lemonv_drama v6.0.0 技能 Benchmark。targetAgentId: lemonv_drama。使用 workspace/digest/benchmark_v3.md 进行评分验证。新增能力测试：1) 选题读取能力：能否正确解析 interactive-video-selector 的选题 JSON；2) API 上传能力：能否正确调用远端 API 并设置 userId=1。请执行 benchmark 测试，验证新技能是否达到 95 分目标。产出写入 workspace/lemonv_drama/evolve/v6/",
-  "priority": "P0",
-  "goalId": "G-lemonv-drama-v6",
-  "nodeId": "G-lemonv-drama-v6.1",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mnbuer1r",
-  "createdAt": "2026-03-29T14:16:48.496Z",
-  "status": "dispatched"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mnbvk2xx",
-  "from": "master",
-  "to": "test:weibo-scraper",
-  "task": "爬取最新微博内容",
-  "priority": "P1",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mnbvk2xx",
-  "createdAt": "2026-03-29T14:48:56.807Z",
-  "status": "done",
-  "completedAt": "2026-03-29T14:50:25.562Z",
-  "summary": "成功爬取微博热搜榜Top50，共50条热点数据。生成文件: weibo_all_20260329_103000.json (JSON数据) + weibo_brief_20260329_103000.html (HTML简报)"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mnbvndl4",
-  "from": "master",
-  "to": "test:interactive-video-selector",
-  "task": "根据 weibo-scraper 爬取的最新微博数据 (weibo_all_20260329_103000.json)，筛选适合制作互动视频的热点选题",
-  "priority": "P0",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mnbvndl4",
-  "createdAt": "2026-03-29T14:51:30.569Z",
-  "status": "done",
-  "completedAt": "2026-03-29T14:52:14.954Z",
-  "summary": "已完成微博热搜数据筛选分析。从50条热搜中识别出4个高适配度互动视频选题，首选选题「母亲隐瞒近40岁女儿已婚骗17万彩礼」(87.5分)，其次「女生举报老师性骚扰失败状告公安机关」(84.0分)、「儿子一家去世老人骑行30年治愈自己」(81.5分)。报告已保存至 workspace/interactive-video-selector/video_topic_report_20260329.md"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mnbvretc",
-  "from": "master",
-  "to": "test:lemonv_drama",
-  "task": "根据微博热搜筛选出的互动视频选题，生成互动视频剧本。选题信息如下：<br><br>## 首选选题（87.5分）<br>- 标题：母亲隐瞒近40岁女儿已婚骗17万彩礼<br>- 形式：对话形式<br>- 互动设计：AB选择（站母亲还是站女儿）<br>- 平台建议：抖音/快手<br><br>## 备选选题1（84.0分）<br>- 标题：女生举报老师性骚扰失败状告公安机关<br>- 形式：旁白+对话混合<br>- 互动设计：投票PK（支持女生还是支持老师）<br><br>## 备选选题2（81.5分）<br>- 标题：儿子一家去世老人骑行30年治愈自己<br>- 形式：旁白形式<br>- 互动设计：结局解锁（开放式结局）<br><br>请根据首选选题生成完整的互动视频剧本，包含角色、场景、对话、互动节点设计。",
-  "priority": "P1",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mnbvretc",
-  "createdAt": "2026-03-29T14:54:38.785Z",
-  "status": "done",
-  "completedAt": "2026-03-29T14:56:11.597Z",
-  "summary": "完成古风互动剧本《绣楼姻错》创作。将微博热搜选题‘母亲隐瞒近40岁女儿已婚骗17万彩礼’改编为明代古风民间剧本，设置4个角色（周秋娘、周氏、李慕白、张员外），3个场景，采用AB选择互动设计（站母亲救父/站女儿护爱），设计4种结局（HE/NE/BE+隐藏真结局），总字数约3200字。剧本已保存至workspace/lemonv_drama/drama_20260329_001.json"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mnbvsnda",
-  "from": "master",
-  "to": "test:lemonv_drama",
-  "task": "根据互动视频选题报告 (video_topic_report_20260329.md) 中的首选选题「母亲隐瞒近40岁女儿已婚骗17万彩礼」，生成互动视频剧本。要求：1) 对话形式 2) AB选择互动设计（站母亲还是站女儿）3) 抖音/快手平台适配",
-  "priority": "P0",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mnbvsnda",
-  "createdAt": "2026-03-29T14:55:36.527Z",
-  "status": "done",
-  "completedAt": "2026-03-29T14:57:44.855Z",
-  "summary": "已生成互动视频剧本「母亲隐瞒近40岁女儿已婚骗17万彩礼」— 对话形式，AB选择互动设计（站母亲vs站女儿），已适配抖音/快手平台，包含完整分镜脚本、角色设定和平台适配建议。输出文件：workspace/lemonv_drama/drama_T-mnbvsnda.json + script_T-mnbvsnda.md"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncavo9p",
-  "from": "master",
-  "to": "test:interactive-video-selector",
-  "task": "从weibo-scraper的产出中选择视频选题，源文件在 data/companies/test/workspace/weibo-scraper/",
-  "priority": "P1",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mncavo9p",
-  "createdAt": "2026-03-29T21:57:51.902Z",
-  "status": "done",
-  "completedAt": "2026-03-29T23:59:04.894Z",
-  "summary": "从微博热搜数据中筛选出3个推荐选题（女生举报性骚扰、老人骑行30年、母亲骗彩礼），Top1选题评分90.3分，已生成选题报告到workspace/interactive-video-selector/video_topic_report_20260330.md"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncl1sti",
-  "from": "master",
-  "to": "test:interactive-video-selector",
-  "task": "从weibo-scraper的产出中选择视频选题，源文件在 data/companies/test/workspace/weibo-scraper/",
-  "priority": "P1",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mncl1sti",
-  "createdAt": "2026-03-30T02:42:33.898Z",
-  "status": "done",
-  "completedAt": "2026-03-30T02:45:03.510Z",
-  "summary": "从微博热搜50条中筛选出TOP3推荐选题：1)女生举报老师性骚扰失败状告公安机关(89.5分) 2)母亲隐瞒近40岁女儿已婚骗17万彩礼(88.7分) 3)凌晨3点孩子等家长睡熟偷平板玩(85.8分)。报告已写入workspace/interactive-video-selector/video_topic_report_20260329.md"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncl5hxs",
-  "from": "master",
-  "to": "test:interactive-video-selector",
-  "task": "将微博热搜话题转化为适合短视频翻拍的互动视频选题报告。微博数据源文件：data/companies/test/workspace/weibo-scraper/weibo_all_20260329_103000.json。请分析微博热搜Top50，筛选出适合改编为古风互动视频的热门话题，输出到 workspace/output/",
-  "priority": "P1",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mncl5hxs",
-  "createdAt": "2026-03-30T02:45:26.418Z",
-  "status": "done",
-  "completedAt": "2026-03-30T02:47:46.442Z",
-  "summary": "已分析微博热搜Top50，筛选出11条适合古风改编的候选话题，对Top5进行6维度深度评估，生成选题报告(JSON+Markdown)。Top3推荐：1)母亲隐瞒女儿已婚骗彩礼(90.3分) 2)老人骑行30年治愈自己(84.3分) 3)村民办白事老外误当成饭店(87.8分)"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncl612p",
-  "from": "master",
-  "to": "test:lemonv_drama",
-  "task": "根据 interactive-video-selector 的产出生成互动视频脚本。源文件: data/companies/test/workspace/interactive-video-selector/video_topic_report_20260329.md。使用 TOP1 选题「女生举报老师性骚扰失败状告公安机关」(89.5分)，生成互动视频脚本并输出到 data/companies/test/workspace/lemonv_drama/",
-  "priority": "P0",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mncl612p",
-  "createdAt": "2026-03-30T02:45:51.220Z",
-  "status": "done",
-  "completedAt": "2026-03-30T02:49:16.793Z",
-  "summary": "完成TOP1选题「女生举报老师性骚扰失败状告公安机关」古风剧本创作，生成宋代公案题材《绣花鞋》互动剧本，含4个角色（沈碧萱、李德昭、周县令、沈祖母）、4个场景、4章节超高频互动剧情、4种结局（HE/NE/BE+隐藏真结局）、诗词意境和投票PK互动设计。输出至 data/companies/test/workspace/lemonv_drama/script_T-mncl612p.md 和 drama_T-mncl612p.json，共4200字。"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mnclaj64",
-  "from": "master",
-  "to": "test:lemonv_drama",
-  "task": "根据选题报告生成古风互动视频剧本并上传到服务器。选题文件：data/companies/test/workspace/interactive-video-selector/output/ancient_video_topics_20260329.json",
-  "priority": "P1",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mnclaj64",
-  "createdAt": "2026-03-30T02:49:21.295Z",
-  "status": "done",
-  "completedAt": "2026-03-30T02:53:17.119Z",
-  "summary": "古风互动剧本生成完成！选题：老夫人贪聘礼隐瞒隐情，傻女婿入赘险成冤大头（微博热搜改编，评分90.3）。生成5个角色（老夫人、少夫人、新郎官、媒婆、县太爷）+ 3个场景 + 13个互动分支 + 4种结局（HE/NE/BE+隐藏真结局）+ 诗词意境 + 数值系统。已写入数据库 Drama ID=22。图片生成任务已提交 Jimeng API。"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mnclb0ik",
-  "from": "master",
-  "to": "test:lemonv_drama",
-  "task": "根据选题生成古风互动视频脚本。选题来源：interactive-video-selector 的产出 ancient_video_topics_20260329.json，Top1推荐选题「母亲隐瞒近40岁女儿已婚骗17万彩礼」(90.3分)。请生成完整的互动视频脚本，输出到 workspace/lemonv_drama/drama_T-mncl5hxs.json",
-  "priority": "P0",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mnclb0ik",
-  "createdAt": "2026-03-30T02:49:43.774Z",
-  "status": "done",
-  "completedAt": "2026-03-30T02:52:12.248Z",
-  "summary": "完成古风互动剧本「老夫人贪聘礼隐瞒隐情」生成。产出：drama_T-mncl5hxs.json（角色5人+场景3个+剧情分支7幕+5种结局+数值系统）"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mnclclls",
-  "from": "master",
-  "to": "test:lemonv_drama",
-  "task": "根据选题报告生成古风互动视频剧本并上传到服务器。选题文件：data/companies/test/workspace/interactive-video-selector/output/ancient_video_topics_20260329.json",
-  "priority": "P1",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mnclclls",
-  "createdAt": "2026-03-30T02:50:57.764Z",
-  "status": "done",
-  "completedAt": "2026-03-30T02:56:14.370Z",
-  "summary": "已生成古风互动剧本《贪聘礼隐情案》，包含5个角色（老夫人、柳如烟、傻女婿、王媒婆、县太爷）、3个场景、4种结局（HE/NE/BE/隐藏）。图片生成因环境缺少rembg未完成，数据库写入因缺少sequelize未完成。剧本JSON保存在workspace/lemonv_drama/T-mnclclls_drama.json，dramaKey=drama_mnclge8i。"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mnclju66",
-  "from": "master",
-  "to": "test:interactive-video-selector",
-  "task": "将微博热搜话题转化为适合短视频翻拍的互动视频选题报告。微博数据源文件：data/companies/test/workspace/weibo-scraper/weibo_all_20260329_103000.json。请分析微博热搜Top50，筛选出适合改编为古风互动视频的热门话题，输出到 workspace/output/",
-  "priority": "P1",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mnclju66",
-  "createdAt": "2026-03-30T02:56:35.458Z",
-  "status": "done",
-  "completedAt": "2026-03-30T02:57:56.494Z",
-  "summary": "已分析微博热搜Top50，筛选出10个适合古风化改编的话题，生成深度选题报告。强烈推荐《青天在上》（女生举报老师性骚扰→民告官），得分90.5；次推荐《瞒婚》（骗婚彩礼纠纷）得分87.9。报告已输出到workspace/output/"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncln07j",
-  "from": "master",
-  "to": "test:lemonv_drama",
-  "task": "根据选题报告生成古风互动视频剧本并上传到服务器。选题文件：data/companies/test/workspace/interactive-video-selector/output/ancient_video_topics_20260329.json",
-  "priority": "P1",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mncln07j",
-  "createdAt": "2026-03-30T02:59:03.253Z",
-  "status": "done",
-  "completedAt": "2026-03-30T03:02:30.727Z",
-  "summary": "已根据选题报告生成古风互动剧本《老夫人贪聘礼隐瞒隐情，傻女婿入赘险成冤大头》。已完成：1) 剧本JSON生成（含5个角色、4个场景、8段剧情、6个结局、诗词意境、数值系统）2) 图片生成任务提交（5个角色图+背景图+封面图）3) 剧本文件保存在 workspace/lemonv_drama/T-mncln07j_drama.json。注意：数据库写入因缺少sequelize模块未能执行，需手动运行insert-drama.js。"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mnclnjfq",
-  "from": "master",
-  "to": "test:lemonv_drama",
-  "task": "根据选题报告生成古风互动视频剧本<br><br>选题报告位置: data/companies/test/workspace/interactive-video-selector/output/ancient_video_topics_20260329.md<br><br>推荐 TOP1 话题（得分90.3）: 母亲隐瞒近40岁女儿已婚骗17万彩礼<br><br>古风改编设定:<br>- 背景: 古代大户人家娶媳<br>- 角色: 老夫人、少夫人、小姐、媒婆、县太爷<br>- 核心冲突: 老夫人贪图聘礼，隐瞒女儿已定亲<br>- 互动设计: 用户选择是否揭穿真相<br><br>请生成完整的古风互动视频剧本，输出到 data/companies/test/workspace/lemonv_drama/",
-  "priority": "P1",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mnclnjfq",
-  "createdAt": "2026-03-30T02:59:28.170Z",
-  "status": "done",
-  "completedAt": "2026-03-30T03:00:51.097Z",
-  "summary": "古风互动剧本\"聘金迷雾\"生成完成。包含5个角色（老夫人、沈婉清、王生、林县令、媒婆），5个场景，超高频互动设计（每场景3+选择点），5维数值系统（勇气/智慧/正义/好感/知名度），3+1多结局（HE/NE/BE+隐藏真结局），已输出到data/companies/test/workspace/lemonv_drama/T-mnclnjfq_drama.json"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncloxzz",
-  "from": "master",
-  "to": "test:lemonv_drama",
-  "task": "根据选题报告生成古风互动视频剧本并上传到服务器。选题文件：data/companies/test/workspace/interactive-video-selector/output/ancient_video_topics_20260329.json",
-  "priority": "P1",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mncloxzz",
-  "createdAt": "2026-03-30T03:00:33.700Z",
-  "status": "done",
-  "completedAt": "2026-03-30T03:05:49.108Z",
-  "summary": "已完成古风互动剧本生成。选题：老夫人贪聘礼隐瞒隐情（得分90.3）。生成了清代家庭题材剧本，包含5个角色、3场景、超高频互动分支、4种结局。脚本环境不完整（generate-images.js/insert-drama.js/API均不存在），仅生成JSON文件保存在workspace。"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mnclwez2",
-  "from": "master",
-  "to": "test:weibo-scraper",
-  "task": "爬取最新微博内容",
-  "priority": "P1",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mnclwez2",
-  "createdAt": "2026-03-30T03:06:22.293Z",
-  "status": "done",
-  "completedAt": "2026-03-30T03:07:17.576Z",
-  "summary": "成功爬取30条微博热搜，数据已保存至 data/companies/test/workspace/weibo-scraper/weibo_hotsearch_20260330_110710.json。热度最高: 身份证号的X到底咋读 (106万)，广州暴雨 (79万)，神21乘组在轨vlog又上新了 (63万)。分类覆盖: 娱乐、社会、科技、综合等。"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mnclyspc",
-  "from": "master",
-  "to": "test:interactive-video-selector",
-  "task": "根据以下微博热搜数据生成3个互动视频选题：身份证号的X到底咋读 (106万)，广州暴雨 (79万)，神21乘组在轨vlog又上新了 (63万)，vivo X300 Ultra专业V单 (58万)。请从这些话题中选择适合做古风互动剧情的内容，生成选题方案。",
-  "priority": "P0",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mnclyspc",
-  "createdAt": "2026-03-30T03:08:13.398Z",
-  "status": "done",
-  "completedAt": "2026-03-30T03:08:48.234Z",
-  "summary": "已生成3个古风互动视频选题。原始4条热搜中仅\"广州暴雨\"勉强适合古风改编，其余3条均为现代话题不适配。因此采用2个改编方案+1个创新重构方案：1)广州暴雨→古风侠义暴雨借宿 2)身份证话题→身世之谜古风悬疑 3)科举放榜→古风科举反转剧情。推荐选题1和3。"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mnclz2ma",
-  "from": "master",
-  "to": "test:interactive-video-selector",
-  "task": "将微博热搜话题转化为适合短视频翻拍的互动视频选题报告。微博数据源文件：data/companies/test/workspace/weibo-scraper/weibo_hotsearch_20260330_110710.json。请分析微博热搜Top50，筛选出适合改编为古风互动视频的热门话题，输出到 workspace/output/",
-  "priority": "P1",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mnclz2ma",
-  "createdAt": "2026-03-30T03:08:26.247Z",
-  "status": "done",
-  "completedAt": "2026-03-30T03:13:04.656Z",
-  "summary": "已分析微博热搜Top30，筛选出8个适合古风化改编的话题，生成深度报告。强烈推荐《丫鬟获罪》（保姆索赔130万→主仆命案）得分87.5；次推荐《县官断案》（基层干部离了稿子不会说话）得分82.0。报告已输出到workspace/output/video_topic_report_20260330.md"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncm2ghh",
-  "from": "master",
-  "to": "test:lemonv_drama",
-  "task": "根据以下微博热搜数据生成的3个互动视频选题方案，生成古风互动剧本。推荐选题1和3：选题1-广州暴雨→古风侠义暴雨借宿，选题3-科举放榜→古风科举反转剧情。请从中选择一个最具戏剧冲突的选题生成完整的互动剧本（包含角色、场景、对话、选项分支、多个结局），产出JSON文件。",
-  "priority": "P0",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mncm2ghh",
-  "createdAt": "2026-03-30T03:11:04.186Z",
-  "status": "done",
-  "completedAt": "2026-03-30T03:14:10.580Z",
-  "summary": "科举风云录-古风互动剧本已完成！选择选题3科举放榜反转剧情，生成了完整的4角色+5场景+多分支多结局剧本JSON（8500字），包含HE/NE/BE+隐藏真结局四种结局，数值系统【勇气/正气/智慧/名声】。文件已写入workspace/lemonv_drama/drama_T-mncm2ghh.json"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncm2xv7",
-  "from": "master",
-  "to": "test:lemonv_drama",
-  "task": "根据选题报告生成古风互动视频剧本并上传到服务器。选题文件：data/companies/test/workspace/interactive-video-selector/output/ancient_video_topics_20260329.json",
-  "priority": "P1",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mncm2xv7",
-  "createdAt": "2026-03-30T03:11:26.712Z",
-  "status": "done",
-  "completedAt": "2026-03-30T03:17:46.644Z",
-  "summary": "已根据选题报告完成古风互动剧本《贪聘礼》生成。基于母亲隐瞒女儿已婚骗取彩礼话题改编为大宋年间娶媳聘礼风波，5个角色，多结局设计，已写入数据库ID=23"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncmd7sj",
-  "from": "master",
-  "to": "test:lemonv_drama",
-  "task": "根据 interactive-video-selector 刚刚完成的最新选题报告（data/companies/test/workspace/output/video_topic_report_20260330.md），生成古风互动视频剧本。<br><br>## 首选选题（87.5分，强烈推荐）<br>- 原始话题：保姆上门当日病人死亡被索赔130万<br>- 古风化改编：丫鬟获罪（新仆入府主母暴亡）<br>- 背景：明清大户人家，丫鬟新进府当日，主母突然暴毙，管家向丫鬟索赔130两银子私了<br>- 角色设计：丫鬟、管家、老爷、师爷<br>- 场景：内宅、府衙<br>- 互动设计：AB选择 - 你会选择赔偿130两银子，还是击鼓鸣冤？<br>- 平台建议：抖音/快手<br><br>请生成完整的古风互动视频剧本（包含角色、场景、对话、选项分支、多个结局），产出JSON文件到 workspace/lemonv_drama/drama_T-mncz_NEWID.json",
-  "priority": "P0",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mncmd7sj",
-  "createdAt": "2026-03-30T03:19:26.132Z",
-  "status": "done",
-  "completedAt": "2026-03-30T03:21:59.514Z",
-  "summary": "完成古风互动剧本《丫鬟获罪：新仆入府主母暴亡》生成，基于微博热搜选题报告TOP1（87.5分），包含4个角色、4个场景、完整分支剧情和4种结局（HE/NE/BE），产出JSON文件至workspace/lemonv_drama/drama_T-mncmd7sj.json，字数3200+"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncme4re",
-  "from": "master",
-  "to": "test:weibo-scraper",
-  "task": "爬取最新微博热搜数据，用于互动视频选题。只需爬取并保存到 workspace 目录，无需做额外分析。",
-  "priority": "P1",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mncme4re",
-  "createdAt": "2026-03-30T03:20:08.861Z",
-  "status": "done",
-  "completedAt": "2026-03-30T03:24:09.514Z",
-  "summary": "成功爬取微博热搜 Top 50 条数据，保存至 data/companies/test/workspace/weibo-scraper/weibo_hotsearch_20260330_114200.json，热度最高: 五哈6阵容官宣 (110万+)"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncmlkdq",
-  "from": "master",
-  "to": "test:interactive-video-selector",
-  "task": "根据刚爬取的微博热搜数据 (weibo_hotsearch_20260330_114200.json)，从中选择 1-3 个最适合古风互动视频的选题，并给出评分和选择理由。只需要生成选题推荐并保存到 workspace 目录，无需做额外分析。",
-  "priority": "P0",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mncmlkdq",
-  "createdAt": "2026-03-30T03:25:55.700Z",
-  "status": "done",
-  "completedAt": "2026-03-30T03:27:25.100Z",
-  "summary": "从微博热搜数据中筛选出3个适合古风互动视频的选题：1)江山大同(88.3分)-权谋题材适合分支选择互动；2)从未忘记你们浴血护山河(83.0分)-英雄题材可做致敬视频；3)白日提灯(83.2分)-古装奇幻爱情可做番外互动。报告已保存至workspace目录。"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncu9yvq",
-  "from": "innovator",
-  "to": "hunter",
-  "task": "对趋势发现领域进行知识猎取。<br><br>【目标模板】trend-scout-global<br>【技能名称】Global Trend Scout (全球趋势发现)<br>【领域】短视频趋势监控<br>【关键词】TikTok trends, Instagram Reels, YouTube Shorts, viral content, trending hashtags, short video analytics<br>【额外指令】参考 scrapling_crawler 技能，训练监控 TikTok、Instagram Reels、YouTube Shorts 趋势能力<br><br>【执行流程】<br>1. 问题澄清 - 理解全球短视频趋势发现的核心需求<br>2. 研究 Knowhow - 调研三大平台的趋势发现方法论<br>3. 寻找标杆 - 找到行业最佳实践<br>4. 筛选分类 - 按平台/内容类型分类<br>5. 爬取数据 - 获取真实趋势数据<br>6. 洞察提取 - 提取可复用的方法论<br><br>【产出位置】workspace/hunt/<br><br>请严格按照 6 Phase 执行，产出 knowhow.md + toplist.md + crawl/ 目录",
-  "priority": "P0",
-  "goalId": "G-tsg-001",
-  "nodeId": "G-tsg-001.1",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncu9yvq",
-  "createdAt": "2026-03-30T07:00:51.550Z",
-  "status": "done",
-  "completedAt": "2026-03-30T07:13:10.061Z",
-  "summary": "完成 Global Trend Scout 知识猎取，产出 knowhow.md + toplist.md + crawl/ (TikTok/IG/YouTube 采集路径验证)"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncubdq8",
-  "from": "innovator",
-  "to": "hunter",
-  "task": "对【竞品侦察competitor-spy】进行知识猎取。skillName: 竞品情报, targetAgentId: competitor-spy, domain: 国内外主流互动剧App监控与AppStore商店评价抓取, keywords: [互动剧,app_store,评价抓取,竞品监控,ios,android,商店数据]。执行：问题澄清→研究Knowhow→寻找标杆→筛选分类→爬取数据→洞察提取。产出到 workspace/competitor-spy/hunt/。额外训练要求：监控国内外主流互动剧App的更新动态、商店评价抓取能力",
-  "priority": "P0",
-  "goalId": "G-comp-spy",
-  "nodeId": "G-comp-spy.1",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncubdq8",
-  "createdAt": "2026-03-30T07:01:57.447Z",
-  "status": "done",
-  "completedAt": "2026-03-30T07:12:15.616Z",
-  "summary": "完成竞品侦察知识猎取：产出knowhow.md方法论、toplist.md标杆排行、crawl/目录含中国/美国区iOS互动剧App数据。包含iTunes API抓取方案、竞品监控指标、技术实现代码示例。"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncubs5w",
-  "from": "innovator",
-  "to": "hunter",
-  "task": "对 data-harvester 进行知识猎取。<br><br>## 目标<br>训练使用 OpenClaw 登录各平台后台，抓取播放量、完播率、CTR 及用户评论情感的能力。<br><br>## targetAgentId<br>data-harvester<br><br>## 技能领域<br>数据抓取（Data Harvesting）- 登录各平台后台抓取播放量、完播率、CTR、用户评论情感分析<br><br>## 关键词<br>[数据抓取, 播放量, 完播率, CTR, 用户评论, 情感分析, OpenClaw, 平台后台登录, 短视频数据, 互动数据]<br><br>## 执行要求<br>按照 main_skill 的 6 Phase 流程执行：<br>1. 问题澄清 - 确认数据抓取需求和平台范围<br>2. 研究 Knowhow - 调研各平台（抖音、快手、B站、小红书、视频号等）的数据获取方式<br>3. 寻找标杆 - 找到行业最佳数据抓取方案<br>4. 筛选分类 - 按平台/数据类型分类<br>5. 爬取数据 - 使用 OpenClaw 或其他工具尝试获取样本数据<br>6. 洞察提取 - 总结数据字段、API 接口、登录方式<br><br>## 产出位置<br>必须写入: ../../data/system/templates/data-harvester/workspace/hunt/<br><br>## 验收标准<br>产出 knowhow.md (知识手册) + toplist.md (排行榜/最佳实践) + crawl/ (爬取配置和样本数据)",
-  "priority": "P0",
-  "goalId": "G-data-harvester",
-  "nodeId": "G-data-harvester.1",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncubs5w",
-  "createdAt": "2026-03-30T07:02:16.154Z",
-  "status": "done",
-  "completedAt": "2026-03-30T07:10:17.451Z",
-  "summary": "已完成 data-harvester 知识猎取任务。产出: knowhow.md (平台数据获取方案、API接口、情感分析) + toplist.md (排行榜、最佳实践) + crawl/ (API配置和样本数据)。总计 3 个文件，覆盖抖音/B站/快手/小红书/视频号 5 大平台的数据采集方案。"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncue787",
-  "from": "innovator",
-  "to": "hunter",
-  "task": "对「高光剪辑手」领域进行知识猎取。skillName: hook-editor, targetAgentId: hook-editor, domain: 短视频高光剪辑 + 爆点识别, keywords: [漫剧,高光时刻,反转瞬间,引流视频,短视频剪辑技巧,完播率,3秒法则]。执行6 Phase：问题澄清→研究Knowhow→寻找标杆→筛选分类→爬取数据→洞察提取。目标：识别漫剧成品中的名场面/反转瞬间，剪辑成5-15秒高转化引流视频。额外要求：爬取抖音/快手高点赞引流视频案例，分析其开头3秒法则、高潮点设置、结尾引导技巧。产出到 workspace/hook-editor/hunt/",
-  "priority": "P0",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncue787",
-  "createdAt": "2026-03-30T07:04:08.991Z",
-  "status": "dispatched"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncue7hc",
-  "from": "innovator",
-  "to": "hunter",
-  "task": "对 高CTR标题文案（clickbait）进行知识猎取。<br><br>【技能领域】<br>- 爆款引流视频标题文案撰写<br>- 国内平台：小红书体<br>- 海外平台：POV体<br><br>【目标 Agent】clickbait-copywriter<br>【输入路径】../../templates/clickbait-copywriter/workspace/hunt/<br>【输出路径】../../templates/clickbait-copywriter/workspace/hunt/<br><br>【执行要求】<br>1. 问题澄清：了解高CTR标题的核心要素（悬念/情绪/数字/冲突/金句）<br>2. 研究Knowhow：搜索小红书/抖音/TikTok/YouTube 爆款标题公式<br>3. 寻找标杆：收集各平台高赞标题案例（至少50条）<br>4. 筛选分类：按平台（国内/海外）+ 风格（种草/测评/故事/干货）分类<br>5. 爬取数据：从公开数据中提取高CTR标题特征<br>6. 洞察提取：总结标题公式与优化技巧<br><br>【产出要求】<br>- knowhow.md：标题撰写方法论（悬念制造、情绪调动、数字锚点、金句结构）<br>- toplist.md：平台分类的爆款标题案例库<br>- crawl/：原始数据（JSON格式）<br><br>请严格按上述6步执行，产出到指定目录。",
-  "priority": "P0",
-  "goalId": "G-clickbait-train",
-  "nodeId": "G-clickbait-train.1",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncue7hc",
-  "createdAt": "2026-03-30T07:04:09.313Z",
-  "status": "done",
-  "completedAt": "2026-03-30T07:12:22.068Z",
-  "summary": "高CTR标题文案知识猎取完成：产出 knowhow.md（5大核心要素+平台差异+模板库+避坑指南）、toplist.md（85条案例，平台+风格分类）、crawl/titles_data.json（30条结构化数据）"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncuebf1",
-  "from": "innovator",
-  "to": "hunter",
-  "task": "对【内容策划效果评估】进行知识猎取。skillName: 内容策划效果评估, targetAgentId: strategy-reflector, domain: 内容策划/选题分析, keywords: [内容策划,选题分析,画风分析,吸粉策略,策划预期vs实操对比,爆款选题特征]。执行：问题澄清→研究Knowhow→寻找标杆→筛选分类→爬取数据→洞察提取。产出到 workspace/strategy-reflector/hunt/。额外要求：重点调研什么选题+画风最吸粉，以及策划预期与实操表现的对比分析方法",
-  "priority": "P0",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncuebf1",
-  "createdAt": "2026-03-30T07:04:14.415Z",
-  "status": "dispatched"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncuedja",
-  "from": "innovator",
-  "to": "hunter",
-  "task": "对 auto-publisher 进行知识猎取（browser automation + video upload）。<br><br>【目标】<br>- targetAgentId: auto-publisher<br>- skillName: 自动化视频发布<br>- domain: browser_automation_video_upload<br>- keywords: [OpenClaw, Playwright, 微博视频上传, 抖音上传API, 小红书发布, 视频号上传, 多平台发布工具]<br><br>【额外训练要求】<br>训练使用 OpenClaw 执行浏览器自动化，将视频片段上传到各社交平台并记录URL的能力<br><br>【输入路径】<br>已有基础: ../../templates/auto-publisher/<br><br>【执行要求】<br>按照你的 main_skill 执行 6 Phase 流程：<br>1. 问题澄清<br>2. 研究Knowhow<br>3. 寻找标杆<br>4. 数据筛选分类<br>5. 爬取数据<br>6. 洞察提取<br><br>【产出位置】<br>必须写入: ../../templates/auto-publisher/workspace/hunt/<br><br>输出文件：<br>- knowhow.md (知识猎取报告)<br>- toplist.md (平台/API 排名)<br>- crawl/ (爬取配置和数据集)",
-  "priority": "P0",
-  "goalId": "G-auto-publisher",
-  "nodeId": "G-auto-publisher.1",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncuedja",
-  "createdAt": "2026-03-30T07:04:17.161Z",
-  "status": "done",
-  "completedAt": "2026-03-30T07:07:55.469Z",
-  "summary": "已完成 auto-publisher 知识猎取。产出: knowhow.md (浏览器自动化方法论), toplist.md (平台排名), crawl/ (配置和脚本模板)"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncufg80",
-  "from": "innovator",
-  "to": "hunter",
-  "task": "对【社区运营/评论管理】领域进行知识猎取。<br><br>## 目标 Agent<br>- id: community-manager<br>- 模板路径: /Users/yibo/Documents/LemonAI/lemonboss/data/system/templates/community-manager<br><br>## 训练技能<br>社区评论运营能力：<br>1. 评论区通过马甲号进行语义化回复<br>2. 神评点赞与筛选<br>3. 私信引导与转化<br><br>## 执行要求<br>按 hunter main_skill 执行 6 Phase 流程：<br>1. 问题澄清 - 明确技能边界和数据需求<br>2. 研究Knowhow - 调研社区运营最佳实践<br>3. 寻找标杆 - 找到优秀的评论互动案例<br>4. 筛选分类 - 分类整理运营策略<br>5. 爬取数据 - 收集真实评论数据、互动话术<br>6. 洞察提取 - 提炼核心方法论<br><br>## 产出要求<br>产出文件必须写入目标 Agent 的 workspace 目录：<br>- /Users/yibo/Documents/LemonAI/lemonboss/data/system/templates/community-manager/workspace/hunt/knowhow.md<br>- /Users/yibo/Documents/LemonAI/lemonboss/data/system/templates/community-manager/workspace/hunt/toplist.md<br>- /Users/yibo/Documents/LemonAI/lemonboss/data/system/templates/community-manager/workspace/hunt/crawl/ (爬取的数据)<br><br>## 验收标准<br>- knowhow.md: 包含至少 5 个核心知识点<br>- toplist.md: 包含至少 10 个分类案例<br>- crawl/: 至少包含 50 条真实评论/话术数据",
-  "priority": "P0",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncufg80",
-  "createdAt": "2026-03-30T07:05:07.304Z",
-  "status": "done",
-  "completedAt": "2026-03-30T07:16:57.898Z",
-  "summary": "社区运营评论管理知识猎取已完成。产出文件：knowhow.md（方法论文档，2000+字，7章节）、toplist.md（标杆案例，150+条）、crawl/comment_scripts.md（150+条评论话术）。覆盖马甲号体系、语义化回复、神评策略、私信转化、平台差异等核心知识点。"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncufnbb",
-  "from": "innovator",
-  "to": "hunter",
-  "task": "对 production-director (生产指令官) 进行知识猎取。<br><br>**目标模板**: ../../templates/production-director<br>**技能领域**: AI生产指令精准下发<br>**核心能力**: 将需求指令精准下发 Spec 给 AI 图片生成引擎<br><br>**执行范围**:<br>1. 研究 AI 图片生成的 prompt/spec 规范<br>2. 寻找业界标杆：将需求转化为生成指令的最佳实践<br>3. 收集常见的 prompt 工程方法论（文生图、图生图、ControlNet 等）<br>4. 筛选分类：不同场景的 spec 模板<br>5. 爬取相关 API 文档和技术博客<br>6. 提取关键洞察：什么样的 spec 能获得高质量生成结果<br><br>**产出位置**: ../../templates/production-director/workspace/hunt/<br>必须产出：knowhow.md (知识总结), toplist.md (分类榜单), crawl/ (原始数据)<br><br>**验收标准**: 包含至少 10 个不同场景的 prompt/spec 案例",
-  "priority": "P0",
-  "goalId": "G-pd-training",
-  "nodeId": "G-pd-training.1",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncufnbb",
-  "createdAt": "2026-03-30T07:05:16.493Z",
-  "status": "done",
-  "completedAt": "2026-03-30T07:12:21.846Z",
-  "summary": "对 production-director (AI生产指令精准下发) 完成知识猎取。产出: knowhow.md (Prompt规范/引擎语法/场景模板/质量优化/10案例), toplist.md (资源平台/场景TOP10/风格榜/模型推荐), crawl/ (2个原始数据)。覆盖: Stable Diffusion/Midjourney/DALL-E 规范, 15+场景模板。"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncufp7k",
-  "from": "innovator",
-  "to": "hunter",
-  "task": "对中国网络热词进行知识猎取。skillName: trend_scout_cn, targetAgentId: trend-scout-cn, domain: 中国网络热词监控, keywords: [小红书热词, 抖音热词, 微博热搜, B站热门, 小说网站热词, 互动剧, 漫剧, 乙女, 后宫, 短剧]。执行：问题澄清→研究Knowhow→寻找标杆→筛选分类→爬取数据→洞察提取。产出到 /Users/yibo/Documents/LemonAI/lemonboss/data/system/agents/hunter/workspace/trend-scout-cn/hunt/。参考 scrapling_crawler 技能训练无头浏览器爬取能力。",
-  "priority": "P0",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncufp7k",
-  "createdAt": "2026-03-30T07:05:18.946Z",
-  "status": "done",
-  "completedAt": "2026-03-30T07:12:16.587Z",
-  "summary": "【中国网络热词监控】知识猎取任务已完成。产出：knowhow.md（10章节，10KB）、toplist.md（8章节，30+信源）、crawl/爬取方案（微博/B站）。所有产出已同步到模板目录 data/system/templates/trend-scout-cn/workspace/hunt/"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncufq4r",
-  "from": "innovator",
-  "to": "hunter",
-  "task": "对 story-architect 进行知识猎取。<br><br>【目标 Agent】story-architect（模板目录: ../../templates/story-architect）<br><br>【技能名称】剧本大纲创作<br>【技能领域】网络小说/互动剧剧本架构设计<br><br>【核心能力要求】<br>1. 基于热点趋势生成剧本大纲<br>2. 设计互动分支点（选择肢）<br>3. 情感映射（好感度/体力等数值体系）<br><br>【参考技能】lemonv_drama（位于 ../../templates/lemonv_drama），已训练完毕，可参考其：<br>- workspace/hunt/knowhow.md<br>- workspace/hunt/toplist.md<br>- workspace/hunt/crawl/ 中的热门剧本数据<br><br>【执行要求】<br>1. 问题澄清：明确 story-architect vs lemonv_drama 的差异（story-architect 更偏大纲创作，不直接写剧本）<br>2. 研究 Knowhow：调研网文/互动剧剧本结构、节拍表、三幕式<br>3. 寻找标杆：分析番茄小说/起点等平台热门剧本结构<br>4. 筛选分类：重生逆袭/甜宠恋爱/悬疑推理/古风穿越等热门题材<br>5. 爬取数据：获取热门剧本大纲案例<br>6. 洞察提取：提取剧本结构模式、互动分支设计<br><br>【输出目录】../../templates/story-architect/workspace/hunt/<br><br>- 需要创建 workspace/hunt/ 目录<br>- 产出: knowhow.md（领域知识定义）, toplist.md（热门标杆）, crawl/（数据文件）<br><br>【验收标准】<br>- knowhow.md 包含完整的剧本架构知识定义<br>- toplist.md 包含至少 10 个热门剧本案例<br>- crawl/ 至少包含 5 个优秀剧本大纲案例",
-  "priority": "P0",
-  "goalId": "G-story-arc",
-  "nodeId": "G-story-arc.1",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncufq4r",
-  "createdAt": "2026-03-30T07:05:20.140Z",
-  "status": "done",
-  "completedAt": "2026-03-30T07:15:24.525Z",
-  "summary": "知识猎取完成。产出：knowhow.md（12章节剧本大纲方法论）+ toplist.md（7章节标杆案例）+ crawl/（5个精品大纲案例：重生逆袭/甜宠恋爱/仙侠升级/悬疑推理/古风互动剧）。所有文件已写入 ../../templates/story-architect/workspace/hunt/"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncuhz3e",
-  "from": "master",
-  "to": "drama:trend-scout-cn",
-  "task": "请执行第一天任务：监控小红书、抖音、微博、B站、小说网站的互动剧/漫剧/乙女/后宫/短剧热词高赞内容，抓取评论区的槽点和期待点。输出热点报告到 workspace/",
-  "priority": "P0",
-  "goalId": "G-001",
-  "nodeId": "G-001.1.1",
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mncuhz3e",
-  "createdAt": "2026-03-30T07:07:05.075Z",
-  "status": "done",
-  "completedAt": "2026-03-30T07:11:51.551Z",
-  "summary": "已完成第一天热点监控任务，整合小红书/抖音/微博/B站/小说网站的互动剧/短剧/乙女/后宫/短剧趋势，输出热点报告到 workspace/trend_report_20260330.md。包含热门题材分析、各平台特征、评论区槽点与期待点汇总、建议创作方向等。"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncui5d9",
-  "from": "master",
-  "to": "drama:story-architect",
-  "task": "请执行剧本策划任务：根据趋势捕获-CN输出的热点报告，分析并生成3个互动剧剧本大纲Demo，包含：核心选题、画风、结构、互动钩子。评估每个方案的引流潜力值(Traffic Score)。输出到 workspace/",
-  "priority": "P0",
-  "goalId": "G-001",
-  "nodeId": "G-001.2.1",
-  "parentTaskId": null,
-  "sourceConvId": null,
-  "executionConvId": "task-T-mncui5d9",
-  "createdAt": "2026-03-30T07:07:13.200Z",
-  "status": "done",
-  "completedAt": "2026-03-30T07:13:05.741Z",
-  "summary": "已完成3个互动剧剧本大纲Demo：1)协议婚约(豪门甜虐,TraffficScore:92) 2)命运的重启(悬疑烧脑,88) 3)皇后的逆袭(宫斗复仇,95)。输出到workspace/story_outlines.json，推荐优先开发宫斗复仇线，引流潜力最高。"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncujgfx",
-  "from": "innovator",
-  "to": "digester",
-  "task": "消化 auto-publisher 的调研数据（browser automation + video upload）。<br><br>【目标】<br>- targetAgentId: auto-publisher<br>- skillName: 自动化视频发布<br>- domain: browser_automation_video_upload<br><br>【额外训练要求】<br>训练使用 OpenClaw 执行浏览器自动化，将视频片段上传到各社交平台并记录URL的能力<br><br>【输入路径】<br>HUNT 产出: ../../templates/auto-publisher/workspace/hunt/<br><br>【执行要求】<br>按照你的 main_skill 执行 4 Step 流程：<br>1. 审查Benchmark → 理解评测维度<br>2. 逐项标注 → 标注 good/bad testdata<br>3. 特征提取 → 提取成功特征<br>4. 完善Benchmark → 生成 benchmark_v1.md<br><br>【产出位置】<br>必须写入: ../../templates/auto-publisher/workspace/digest/<br><br>输出文件：<br>- benchmark_v1.md (评测基准)<br>- good_testdata/ (合格样本)<br>- bad_testdata/ (不合格样本)",
-  "priority": "P0",
-  "goalId": "G-auto-publisher",
-  "nodeId": "G-auto-publisher.2",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncujgfx",
-  "createdAt": "2026-03-30T07:08:14.207Z",
-  "status": "done",
-  "completedAt": "2026-03-30T07:13:18.056Z",
-  "summary": "消化 auto-publisher 调研数据完成。产出: benchmark_v1.md (6维评分标准), knowhow.md (知识合成), good_testdata/3项 (playwright_config/selectors/upload_script)。全部 3 项 crawl 数据 avg score=90，均进入 good testdata。"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mnculhkp",
-  "from": "innovator",
-  "to": "digester",
-  "task": "消化 auto-publisher 的调研数据。<br><br>【目标】<br>- targetAgentId: auto-publisher<br>- skillName: 自动化视频发布<br>- domain: browser_automation_video_upload<br><br>【输入路径】<br>workspace/auto-publisher/hunt/<br>- knowhow.md (浏览器自动化方法论)<br>- toplist.md (平台排名)<br>- crawl/ (playwright_config.js, selectors.md, upload_script.js)<br><br>【执行要求】<br>按照你的 main_skill 执行 4 Step 流程：<br>1. 审查Benchmark<br>2. 逐项标注<br>3. 特征提取<br>4. 完善Benchmark<br><br>【产出位置】<br>必须写入: ../../templates/auto-publisher/workspace/digest/<br><br>输出文件：<br>- benchmark_v1.md (评分标准)<br>- good_testdata/ (至少3个成功案例)<br>- bad_testdata/ (至少2个失败案例)<br>- industry_avg_score: 基准分数",
-  "priority": "P0",
-  "goalId": "G-auto-publisher",
-  "nodeId": "G-auto-publisher.2",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mnculhkp",
-  "createdAt": "2026-03-30T07:09:48.993Z",
-  "status": "dispatched"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncumyfu",
-  "from": "innovator",
-  "to": "digester",
-  "task": "消化 data-harvester 的调研数据。skillName: 数据抓取, targetAgentId: data-harvester。输入: ../../data/system/templates/data-harvester/workspace/hunt/。执行：审查Benchmark→逐项标注→特征提取→完善Benchmark。产出到 ../../data/system/templates/data-harvester/workspace/digest/",
-  "priority": "P0",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncumyfu",
-  "createdAt": "2026-03-30T07:10:57.505Z",
-  "status": "done",
-  "completedAt": "2026-03-30T07:19:15.150Z",
-  "summary": "消化data-harvester调研数据任务已完成。产出:benchmark_v1.md(6维评分标准),knowhow.md(知识合成),good_testdata(2项:抖音88分/B站85分),bad_testdata(2项:快手65分/小红书55分)。平均分73.25。"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncunc8c",
-  "from": "innovator",
-  "to": "digester",
-  "task": "消化【competitor-spy竞品侦察】的调研数据。skillName: 竞品情报, targetAgentId: competitor-spy。输入: hunter/workspace/competitor-spy/hunt/ (knowhow.md, toplist.md)。执行：审查Benchmark→逐项标注→特征提取→完善Benchmark。产出到 workspace/competitor-spy/digest/。",
-  "priority": "P0",
-  "goalId": "G-comp-spy",
-  "nodeId": "G-comp-spy.2",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncunc8c",
-  "createdAt": "2026-03-30T07:11:15.380Z",
-  "status": "done",
-  "completedAt": "2026-03-30T07:20:16.938Z",
-  "summary": "消化competitor-spy竞品侦察调研数据完成。产出benchmark_v2、knowhow、4项good_testdata(score 89-91)，avg 89.75分。"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncunkxu",
-  "from": "innovator",
-  "to": "hunter",
-  "task": "对【内容策划效果评估】进行知识猎取。第2次尝试。skillName: 内容策划效果评估, targetAgentId: strategy-reflector, domain: 内容策划/选题分析, keywords: [内容策划,选题分析,画风分析,吸粉策略,策划预期vs实操对比,爆款选题特征]。产出文件：knowhow.md + toplist.md + crawl/ 数据到 workspace/strategy-reflector/hunt/。额外要求：调研爆款选题特征、画风分析方法",
-  "priority": "P0",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncunkxu",
-  "createdAt": "2026-03-30T07:11:26.667Z",
-  "status": "done",
-  "completedAt": "2026-03-30T07:18:54.054Z",
-  "summary": "已完成内容策划效果评估知识猎取，产出knowhow.md(8章节方法论)和toplist.md(6章节排行榜)，包含选题热度/竞争差异度/执行可行性/粉丝期望差/画风分析五大评估维度，以及爆款选题特征/吸粉策略/策划预期vs实操对比/量化成功率等核心内容"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncuom5g",
-  "from": "innovator",
-  "to": "digester",
-  "task": "消化 production-director 的调研数据。<br><br>**目标模板**: ../../templates/production-director<br>**技能领域**: AI生产指令精准下发<br>**核心能力**: 将需求指令精准下发 Spec 给 AI 图片生成引擎<br><br>**输入目录**: ../../templates/production-director/workspace/hunt/<br>**Hunt 产出**:<br>- knowhow.md - AI 生产指令核心知识（SD/MJ/DALL-E 语法、参数、质量标记）<br>- toplist.md - 场景化 Prompt 案例 TOP10<br><br>**执行范围**:<br>1. 审查并理解 hunt 产出的知识内容<br>2. 构建 Benchmark 评估标准（至少 10 个评分维度）<br>3. 创建 good_testdata/ (5个高质量 spec 案例)<br>4. 创建 bad_testdata/ (5个低质量 spec 案例)<br>5. 定义 industry_avg_score 基准线<br><br>**产出位置**: ../../templates/production-director/workspace/digest/<br>必须产出：benchmark_v1.md, good_testdata/, bad_testdata/<br><br>**验收标准**:<br>- Benchmark 包含至少 10 个评分维度<br>- Good testdata 包含 5 个不同场景的优质 spec<br>- Bad testdata 包含 5 个低质量 spec（常见错误）",
-  "priority": "P0",
-  "goalId": "G-pd-training",
-  "nodeId": "G-pd-training.2",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncuom5g",
-  "createdAt": "2026-03-30T07:12:14.894Z",
-  "status": "dispatched"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncupe7m",
-  "from": "innovator",
-  "to": "digester",
-  "task": "消化 trend-scout-cn 的调研数据。skillName: trend_scout_cn, targetAgentId: trend-scout-cn。输入: /Users/yibo/Documents/LemonAI/lemonboss/data/system/templates/trend-scout-cn/workspace/hunt/。执行：审查Benchmark→逐项标注→特征提取→完善Benchmark。产出到 /Users/yibo/Documents/LemonAI/lemonboss/data/system/agents/digester/workspace/trend-scout-cn/digest/。目标: Benchmark Score ≥ 95。",
-  "priority": "P0",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncupe7m",
-  "createdAt": "2026-03-30T07:12:51.259Z",
-  "status": "done",
-  "completedAt": "2026-03-30T07:18:22.680Z",
-  "summary": "trend-scout-cn 调研数据消化完成。产出 benchmark_v1.md (5维评分)、knowhow_v1.md、good_testdata 3项 (B站92/知乎78/百度75)、bad_testdata 2项 (微博48/抖音45)，最高分92，目标95差距3分"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncupgip",
-  "from": "innovator",
-  "to": "digester",
-  "task": "对 story-architect 进行知识消化。<br><br>【目标 Agent】story-architect（模板目录: ../../templates/story-architect）<br><br>【技能名称】剧本大纲创作<br>【输入目录】../../templates/story-architect/workspace/hunt/<br>- knowhow.md（已生成）<br>- toplist.md（已生成）<br>- crawl/（案例数据）<br><br>【参考】lemonv_drama 的 benchmark 构建方式（位于 ../../templates/lemonv_drama/workspace/digest/benchmark_v2.md）<br><br>【执行要求】<br>1. 审查 Benchmark 设计：根据 knowhow.md 定义评分维度<br>2. 逐项标注：为每个维度设计评分标准和题目<br>3. 特征提取：从 crawl/ 提取优秀案例特征<br>4. 完善 Benchmark：生成 benchmark_v1.md<br><br>【额外训练要求/指令】<br>参考现有的 lemonv_drama 技能，训练从热点趋势生成剧本大纲、互动分支点、情感映射的能力。<br><br>【输出目录】../../templates/story-architect/workspace/digest/<br>- 需要创建 workspace/digest/ 目录<br>- 产出: benchmark_v1.md（评分标准）, good_testdata/(优秀案例), bad_testdata/(失败案例)<br><br>【验收标准】<br>- benchmark_v1.md 包含至少 5 个评分维度，每个维度有明确的评分标准<br>- good_testdata/ 至少包含 3 个优秀大纲案例<br>- bad_testdata/ 至少包含 2 个失败案例",
-  "priority": "P0",
-  "goalId": "G-story-arc",
-  "nodeId": "G-story-arc.2",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncupgip",
-  "createdAt": "2026-03-30T07:12:54.244Z",
-  "status": "dispatched"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncups4u",
-  "from": "innovator",
-  "to": "digester",
-  "task": "消化 production-director (AI生产指令精准下发) 的调研数据。skillName: AI生产指令精准下发, targetAgentId: production-director, domain: AI图片生成Prompt工程。输入: ../../templates/production-director/workspace/hunt/。执行：审查Benchmark→逐项标注→特征提取→完善Benchmark。产出到 ../../templates/production-director/workspace/digest/",
-  "priority": "P0",
-  "goalId": "G-001",
-  "nodeId": "G-001.2",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncups4u",
-  "createdAt": "2026-03-30T07:13:09.303Z",
-  "status": "dispatched"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncupsxm",
-  "from": "innovator",
-  "to": "digester",
-  "task": "消化 clickbait-copywriter 的调研数据。技能领域：爆款高CTR视频标题文案。输入路径：../../templates/clickbait-copywriter/workspace/hunt/。输出路径：../../templates/clickbait-copywriter/workspace/digest/。执行要求：1. 审查 knowhow.md 提取核心知识；2. 逐条分析 toplist.md 的标题案例，标注风格标签（悬念/情绪/数字/冲突/金句）；3. 构建 clickbait benchmark（包含评分维度：CTR效果预测/平台适配/情感强度/可执行性）；4. 准备 good/bad testdata 各3-5条。产出：benchmark_v1.md + good_testdata/ + bad_testdata/。",
-  "priority": "P0",
-  "goalId": "G-clickbait",
-  "nodeId": "G-clickbait.2",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncupsxm",
-  "createdAt": "2026-03-30T07:13:10.337Z",
-  "status": "dispatched"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncuqxuz",
-  "from": "innovator",
-  "to": "digester",
-  "task": "消化 trend-scout-cn 的调研数据。skillName: trend_scout_cn, targetAgentId: trend-scout-cn。输入: /Users/yibo/Documents/LemonAI/lemonboss/data/system/templates/trend-scout-cn/workspace/hunt/。执行：审查Benchmark→逐项标注→特征提取→完善Benchmark。产出到 /Users/yibo/Documents/LemonAI/lemonboss/data/system/templates/trend-scout-cn/workspace/digest/",
-  "priority": "P0",
-  "goalId": "T-mncufp7k",
-  "nodeId": "T-mncufp7k.2",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncuqxuz",
-  "createdAt": "2026-03-30T07:14:03.379Z",
-  "status": "dispatched"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncusls1",
-  "from": "innovator",
-  "to": "digester",
-  "task": "消化竞品侦察competitor-spy的调研数据。skillName: 竞品情报, targetAgentId: competitor-spy。输入: data/system/agents/hunter/workspace/competitor-spy/hunt/。执行：审查Benchmark→逐项标注→特征提取→完善Benchmark。产出到 data/system/templates/competitor-spy/workspace/digest/。额外要求：benchmark需覆盖国内外互动剧App监控、iOS/Android商店评价抓取能力",
-  "priority": "P0",
-  "goalId": "G-comp-spy",
-  "nodeId": "G-comp-spy.2",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncusls1",
-  "createdAt": "2026-03-30T07:15:21.036Z",
-  "status": "dispatched"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncusnkb",
-  "from": "innovator",
-  "to": "digester",
-  "task": "对【社区运营/评论管理】领域进行知识消化。<br><br>## 目标 Agent<br>- id: community-manager<br>- 模板路径: /Users/yibo/Documents/LemonAI/lemonboss/data/system/templates/community-manager<br><br>## 输入<br>- /Users/yibo/Documents/LemonAI/lemonboss/data/system/templates/community-manager/workspace/hunt/knowhow.md<br>- /Users/yibo/Documents/LemonAI/lemonboss/data/system/templates/community-manager/workspace/hunt/toplist.md<br><br>## 训练技能<br>社区评论运营能力：<br>1. 评论区通过马甲号进行语义化回复<br>2. 神评点赞与筛选<br>3. 私信引导与转化<br><br>## 执行要求<br>按 digester main_skill 执行 4 Step 流程：<br>1. 审查Benchmark - 制定评分标准<br>2. 逐项标注 - 对 knowhow 中的知识点打分<br>3. 特征提取 - 提取可复用的模式<br>4. 完善Benchmark - 产出具化评测标准<br><br>## 产出要求<br>产出文件必须写入目标 Agent 的 workspace/digest/ 目录：<br>- /Users/yibo/Documents/LemonAI/lemonboss/data/system/templates/community-manager/workspace/digest/benchmark_v1.md<br>- /Users/yibo/Documents/LemonAI/lemonboss/data/system/templates/community-manager/workspace/digest/good_testdata/ (好评案例)<br>- /Users/yibo/Documents/LemonAI/lemonboss/data/system/templates/community-manager/workspace/digest/bad_testdata/ (差评案例)<br><br>## 验收标准<br>- benchmark_v1.md: 包含至少 5 个评分维度，每个维度有明确的评分标准 (1-5分)<br>- good_testdata/: 至少 3 个高质量互动案例<br>- bad_testdata/: 至少 3 个低质量/违规案例<br>- industry_avg_score: 基于行业水平设定基准分 (如 75 分)",
-  "priority": "P0",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncusnkb",
-  "createdAt": "2026-03-30T07:15:23.342Z",
-  "status": "done",
-  "completedAt": "2026-03-30T07:19:18.865Z",
-  "summary": "社区运营消化完成。产出：benchmark_v1.md (6维评分标准)、good_testdata 3个(平均88.3分)、bad_testdata 3个(平均38.3分)、diff_report_v1.md"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncutmeo",
-  "from": "innovator",
-  "to": "evolver",
-  "task": "训练 trend-scout-cn 的热词监控技能。targetAgentId: trend-scout-cn。输入: /Users/yibo/Documents/LemonAI/lemonboss/data/system/templates/trend-scout-cn/workspace/digest/。参考: /Users/yibo/Documents/LemonAI/lemonboss/data/system/templates/scrapling_crawler/.claude/skills/main_skill/SKILL.md。执行 Small Loop：执行→评分→分析→改写→重复，passThreshold: 95, maxIterations: 10。产出到 /Users/yibo/Documents/LemonAI/lemonboss/data/system/agents/evolver/workspace/trend-scout-cn/evolve/。",
-  "priority": "P0",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncutmeo",
-  "createdAt": "2026-03-30T07:16:08.508Z",
-  "status": "dispatched"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncuut1e",
-  "from": "innovator",
-  "to": "digester",
-  "task": "消化 trend-scout-global 的调研数据。【目标模板】trend-scout-global【技能名称】Global Trend Scout (全球趋势发现)【领域】短视频趋势监控【输入】workspace/hunt/ (knowhow.md, toplist.md, crawl/)【产出位置】workspace/digest/执行流程：1. 审查并理解 Hunt 产出的所有 knowhow.md + toplist.md2. 构建 Benchmark 评估体系 (至少 5 个维度)3. 逐项标注训练数据 (good_testdata/bad_testdata)4. 完善 benchmark_v1.md产出要求：- benchmark_v1.md (5+ 评估维度)- good_testdata/ (至少 3 个案例)- bad_testdata/ (至少 2 个案例)【验收标准】每个测试案例需包含：输入描述 + 执行结果 + 评分理由",
-  "priority": "P0",
-  "goalId": "G-TSG",
-  "nodeId": "G-TSG.2",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncuut1e",
-  "createdAt": "2026-03-30T07:17:03.756Z",
-  "status": "dispatched"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncuuuv1",
-  "from": "innovator",
-  "to": "digester",
-  "task": "对 story-architect 进行知识消化。输入目录: ../../templates/story-architect/workspace/hunt/（已有 knowhow.md, toplist.md, crawl/）。输出目录: ../../templates/story-architect/workspace/digest/。需要产出: benchmark_v1.md, good_testdata/, bad_testdata/",
-  "priority": "P0",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncuuuv1",
-  "createdAt": "2026-03-30T07:17:06.112Z",
-  "status": "dispatched"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncuuvgv",
-  "from": "innovator",
-  "to": "evolver",
-  "task": "训练 auto-publisher 的 自动化视频发布 技能。<br><br>【目标】<br>- targetAgentId: auto-publisher<br>- skillName: 自动化视频发布<br>- domain: browser_automation_video_upload<br><br>【输入路径】<br>DIGEST 产出: ../../templates/auto-publisher/workspace/digest/<br>- benchmark_v1.md (评测基准)<br>- good_testdata/ (合格样本)<br>- bad_testdata/ (不合格样本)<br><br>【执行要求】<br>按照你的 main_skill 执行 Small Loop：<br>1. 执行 → 使用 good_testdata 样本执行任务<br>2. 评分 → 基于 benchmark_v1.md 评分<br>3. 分析 → 分析弱维度<br>4. 改写 → 修改 SKILL.md<br>5. 重复 → 直到 score ≥ 90 或达到最大迭代次数<br><br>【产出位置】<br>必须写入: ../../templates/auto-publisher/workspace/evolve/<br><br>输出文件：<br>- evolve_log.md (迭代日志)<br>- v1/skill.md, v2/skill.md, ... (版本迭代)<br>- 最终 SKILL.md (达标版本)<br><br>【通过标准】<br>passThreshold: 90, maxIterations: 10",
-  "priority": "P0",
-  "goalId": "G-003",
-  "nodeId": "G-003.3",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncuuvgv",
-  "createdAt": "2026-03-30T07:17:06.899Z",
-  "status": "dispatched"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncuuvo6",
-  "from": "innovator",
-  "to": "digester",
-  "task": "消化 story-architect 的调研数据。<br><br>【目标 Agent】story-architect（模板目录: ../../templates/story-architect）<br><br>【技能名称】剧本大纲创作<br>【技能领域】网络小说/互动剧剧本架构设计<br><br>【输入目录】../../templates/story-architect/workspace/hunt/<br>- knowhow.md（12章节剧本大纲方法论）<br>- toplist.md（7章节标杆案例）<br>- crawl/（5个精品大纲案例：重生逆袭/甜宠恋爱/仙侠升级/悬疑推理/古风互动剧）<br><br>【核心能力要求】<br>1. 基于热点趋势生成剧本大纲<br>2. 设计互动分支点（选择肢）<br>3. 情感映射（好感度/体力等数值体系）<br><br>【执行要求】<br>1. 审查 Benchmark：定义评分维度（结构完整性/分支设计/情感映射/创新性/可执行性）<br>2. 逐项标注：对每个测试案例按维度评分<br>3. 特征提取：提取剧本结构模式、互动分支设计、情感数值体系<br>4. 完善 Benchmark：生成 benchmark_v1.md + good/bad_testdata<br><br>【输出目录】../../templates/story-architect/workspace/digest/<br><br>【验收标准】<br>- benchmark_v1.md 包含完整的评分维度和标准<br>good_testdata/ 包含至少 3 个高分案例<br>- bad_testdata/ 包含至少 2 个低分案例",
-  "priority": "P0",
-  "goalId": "G-story-architect",
-  "nodeId": "G-story-architect.2",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncuuvo6",
-  "createdAt": "2026-03-30T07:17:07.161Z",
-  "status": "dispatched"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncuw40n",
-  "from": "innovator",
-  "to": "digester",
-  "task": "消化【内容策划效果评估】的调研数据。skillName: 内容策划效果评估, targetAgentId: strategy-reflector。输入: workspace/strategy-reflector/hunt/。执行：审查Benchmark→逐项标注→特征提取→完善Benchmark。产出到 workspace/strategy-reflector/digest/。额外要求：构建完整的评分体系，标注每个测试维度的评分标准",
-  "priority": "P0",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncuw40n",
-  "createdAt": "2026-03-30T07:18:04.642Z",
-  "status": "dispatched"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncuwbwa",
-  "from": "innovator",
-  "to": "digester",
-  "task": "消化【社区运营/评论管理】的调研数据。<br><br>## 目标 Agent<br>- id: community-manager<br>- 模板路径: /Users/yibo/Documents/LemonAI/lemonboss/data/system/templates/community-manager<br><br>## 训练技能<br>社区评论运营能力：<br>1. 评论区通过马甲号进行语义化回复<br>2. 神评点赞与筛选<br>3. 私信引导与转化<br><br>## 输入数据<br>- workspace/hunt/knowhow.md - 方法论文档<br>- workspace/hunt/toplist.md - 标杆案例<br>- workspace/hunt/crawl/comment_scripts.md - 评论话术数据<br><br>## 执行要求<br>按 digester main_skill 执行 4 Step 流程：<br>1. 审查 Benchmark - 理解评分维度<br>2. 逐项标注 - 对输入数据打标签<br>3. 特征提取 - 提炼技能特征<br>4. 完善 Benchmark - 输出 benchmark_v1.md<br><br>## 产出要求<br>产出文件必须写入目标 Agent 的 workspace 目录：<br>- /Users/yibo/Documents/LemonAI/lemonboss/data/system/templates/community-manager/workspace/digest/benchmark_v1.md<br>- /Users/yibo/Documents/LemonAI/lemonboss/data/system/templates/community-manager/workspace/digest/good_testdata/ (标注数据)<br>- /Users/yibo/Documents/LemonAI/lemonboss/data/system/templates/community-manager/workspace/digest/bad_testdata/ (反例数据)<br><br>## 验收标准<br>- benchmark_v1.md: 包含至少 5 个评分维度，定义 industry_avg_score<br>- good_testdata/: 至少 5 个优质案例<br>- bad_testdata/: 至少 3 个反面案例",
-  "priority": "P0",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncuwbwa",
-  "createdAt": "2026-03-30T07:18:14.844Z",
-  "status": "dispatched"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncuwedo",
-  "from": "innovator",
-  "to": "evolver",
-  "task": "训练 community-manager 的【社区评论运营】技能。<br><br>## 目标 Agent<br>- id: community-manager<br>- 模板路径: /Users/yibo/Documents/LemonAI/lemonboss/data/system/templates/community-manager<br><br>## 输入<br>- /Users/yibo/Documents/LemonAI/lemonboss/data/system/templates/community-manager/workspace/digest/benchmark_v1.md<br>- /Users/yibo/Documents/LemonAI/lemonboss/data/system/templates/community-manager/workspace/digest/good_testdata/<br>- /Users/yibo/Documents/LemonAI/lemonboss/data/system/templates/community-manager/workspace/hunt/knowhow.md<br><br>## 训练技能<br>社区评论运营能力：<br>1. 评论区通过马甲号进行语义化回复<br>2. 神评点赞与筛选<br>3. 私信引导与转化<br><br>## 执行要求<br>按 evolver main_skill 执行 Small Loop：<br>1. 执行 - 使用当前技能处理测试案例<br>2. 评分 - 按 benchmark_v1.md 评分<br>3. 分析 - 识别弱维度<br>4. 改写 - 优化 SKILL.md<br>5. 重复 - 直到 score ≥ 90<br><br>## 参数<br>- passThreshold: 90<br>- maxIterations: 10<br><br>## 产出要求<br>产出文件必须写入目标 Agent 的 workspace/evolve/ 目录：<br>- /Users/yibo/Documents/LemonAI/lemonboss/data/system/templates/community-manager/workspace/evolve/evolve_log.md<br>- /Users/yibo/Documents/LemonAI/lemonboss/data/system/templates/community-manager/workspace/evolve/SKILL.md<br><br>## 验收标准<br>- 最终 score ≥ 90<br>- evolve_log.md 记录每次迭代<br>- SKILL.md 包含完整的技能定义",
-  "priority": "P0",
-  "goalId": null,
-  "nodeId": null,
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncuwedo",
-  "createdAt": "2026-03-30T07:18:18.064Z",
-  "status": "dispatched"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncuxdta",
-  "from": "innovator",
-  "to": "evolver",
-  "task": "训练 production-director 的 AI 生产指令精准下发技能。<br><br>**目标模板**: ../../templates/production-director<br>**技能领域**: AI生产指令精准下发<br>**核心能力**: 将需求指令精准下发 Spec 给 AI 图片生成引擎<br><br>**输入目录**: ../../templates/production-director/workspace/digest/<br>**DIGEST 产出**:<br>- benchmark_v1.md - 10维评分标准<br>- good_testdata/ - 高质量 spec 案例<br>- bad_testdata/ - 低质量 spec 案例<br><br>**执行任务**: Small Loop 技能迭代训练<br>- 使用 benchmark_v1.md 评估标准<br>- 对 good_testdata 中的案例进行评分验证<br>- 识别薄弱维度并改进<br>- 执行 → 评分 → 分析 → 改写 SKILL.md → 重复<br><br>**目标**: passThreshold: 95 分<br>**最大迭代**: 10 轮<br><br>**关键训练点**: <br>- 精准理解用户需求并转化为高质量 Prompt/Spec<br>- 掌握 SD/MJ/DALL-E 等引擎的语法差异<br>- 不同场景的 spec 模板化能力<br><br>**产出位置**: ../../templates/production-director/workspace/evolve/<br>必须产出：evolve_log.md, 最终 SKILL.md<br><br>**验收标准**: final_score ≥ 95",
-  "priority": "P0",
-  "goalId": "G-pd-training",
-  "nodeId": "G-pd-training.3",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncuxdta",
-  "createdAt": "2026-03-30T07:19:03.995Z",
-  "status": "dispatched"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncuynqg",
-  "from": "innovator",
-  "to": "evolver",
-  "task": "训练 trend-scout-cn 的 trend_scout_cn 技能。targetAgentId: trend-scout-cn, skillName: trend_scout_cn。输入: /Users/yibo/Documents/LemonAI/lemonboss/data/system/agents/digester/workspace/trend-scout-cn/digest/。执行 Small Loop：执行→评分→分析→改写→重复，passThreshold: 95, maxIterations: 10。产出到 /Users/yibo/Documents/LemonAI/lemonboss/data/system/templates/trend-scout-cn/workspace/evolve/",
-  "priority": "P0",
-  "goalId": "G-mncupe7m",
-  "nodeId": "G-mncupe7m.3",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncuynqg",
-  "createdAt": "2026-03-30T07:20:03.507Z",
-  "status": "dispatched"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mncz867y",
-  "from": "innovator",
-  "to": "hunter",
-  "task": "对「创意跑酷生成师」(idearrun-create) 领域进行知识猎取。skillName: create-drama, targetAgentId: idearrun-create, domain: 互动剧本自动生成。执行：问题澄清→研究Knowhow→寻找标杆→筛选分类→爬取数据→洞察提取。技能定义：从素材（新闻、故事、段子）自动生成经典互动剧本（含角色图、场景图）并写入数据库。输入目录已有模板：data/system/templates/idearuncreate/skills/SKILL.md。产出到 workspace/idearrun-create/hunt/",
-  "priority": "P0",
-  "goalId": "G-idearruncreate-fde",
-  "nodeId": "G-idearruncreate-fde.1",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mncz867y",
-  "createdAt": "2026-03-30T09:19:25.836Z",
-  "status": "dispatched"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mnczgrwi",
-  "from": "innovator",
-  "to": "digester",
-  "task": "对「创意跑酷生成师」(idearrun-create) 的 create-drama 技能进行知识消化。skillName: create-drama, targetAgentId: idearrun-create。输入：data/system/templates/idearuncreate/skills/SKILL.md 作为模板 + workspace/hunt/ 的调研数据（如果存在）。执行：审查Benchmark→逐项标注→特征提取→完善Benchmark。产出到 workspace/idearrun-create/digest/",
-  "priority": "P0",
-  "goalId": "G-idearruncreate-fde",
-  "nodeId": "G-idearruncreate-fde.2",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mnczgrwi",
-  "createdAt": "2026-03-30T09:26:07.187Z",
-  "status": "done",
-  "completedAt": "2026-03-30T09:28:47.373Z",
-  "summary": "create-drama 知识消化完成。benchmark_v1.md (5维评分)、knowhow_v1.md、good_testdata 3项 (SKILL模板92/核心配置88/标杆资源78)、bad_testdata 2项 (角色超限55/空白脸部缺失48)，目标分数70，差距0分"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mnczlwlc",
-  "from": "innovator",
-  "to": "evolver",
-  "task": "训练 idearrun-create 的 create-drama 技能。targetAgentId: idearrun-create。输入: workspace/idearrun-create/digest/。执行 Small Loop: 执行→评分→分析→改写→重复, passThreshold: 95, maxIterations: 10。产出到 workspace/idearrun-create/evolve/",
-  "priority": "P0",
-  "goalId": "G-idearruncreate-fde",
-  "nodeId": "G-idearruncreate-fde.3",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mnczlwlc",
-  "createdAt": "2026-03-30T09:30:06.545Z",
-  "status": "done",
-  "completedAt": "2026-03-30T09:33:28.686Z",
-  "summary": "训练 idearrun-create 的 create-drama 技能完成：最终分数 99 (A+, ≥95 达标 ✅), 迭代次数 1, SKILL.md 已存在 9484 bytes"
-}
 \ No newline at end of file
-{
-  "taskId": "T-mnczm565",
-  "from": "innovator",
-  "to": "evolver",
-  "task": "训练 idearun-create 的 create-drama 技能。targetAgentId: idearun-create。输入：workspace/idearun-create/digest/ 的 benchmark + 测试数据。执行 Small Loop：执行→评分→分析→改写→重复，passThreshold: 90, maxIterations: 10。产出到 workspace/idearun-create/evolve/",
-  "priority": "P0",
-  "goalId": "G-idearun-create",
-  "nodeId": "G-idearun-create.3",
-  "parentTaskId": "T-mnbudwvk",
-  "sourceConvId": "task-T-mnbudwvk",
-  "executionConvId": "task-T-mnczm565",
-  "createdAt": "2026-03-30T09:30:17.652Z",
-  "status": "dispatched"
-}
 \ No newline at end of file
-# 方案: [TASK from master]
-
-> 创建时间: 2026-03-27
-
-## 适用条件
-- 类似任务: [TASK from master]
-
-## 执行步骤
-1. ✅ 微博热搜 API 爬取
-2. ✅ 分类筛选（娱乐/社会/科技/搞笑/体育/财经）
-3. ✅ 关键指标提取（点赞/评论/转发/阅读）
-4. ✅ 数据清洗与格式化 JSON 输出
-5. ✅ 定时爬取支持（Cron）
-
-## 注意事项
-- 自动从对话 task-T-mn972nl4 提取
-- 耗时 186秒, 42 次工具调用
-[
-  {
-    "scenario": "[TASK from master]",
-    "keywords": [
-      "任务编号",
-      "任务",
-      "务编",
-      "编号",
-      "任务编",
-      "务编号",
-      "优先级",
-      "优先"
-    ],
-    "planFile": "task-from-master.md",
-    "confidence": 0.9,
-    "useCount": 20,
-    "lastUsed": "2026-03-29",
-    "createdAt": "2026-03-27"
-  }
-]
 \ No newline at end of file
-# 海马体 — 经验记忆索引
-
-> 记录过往处理过的情景模式。遇到相似任务时，优先调用小脑中的固化方案。
-> ⚠️ 此文件由系统自动生成，勿手动编辑。真实数据在 hippocampus-index.json。
-
-| # | 情景模式 | 匹配关键词 | 方案文件 | 置信度 | 使用次数 | 上次使用 |
-|---|---------|-----------|---------|--------|---------|----------|
-| 1 | [TASK from master] | 任务编号, 任务, 务编, 编号, 任务编, 务编号, 优先级, 优先 | task-from-master.md | 0.9 | 0 | - |
-{
-  "timestamp": "2026-03-27T17:51:06.647Z",
-  "convId": "task-T-mn972nl4",
-  "task": "[TASK from master]\n任务编号: T-mn972nl4\n优先级: P1\n\n创建一个模板 Agent，名称为「微博热点内容爬取」，负责从微博平台爬取热点话题、热门微博、热搜榜等内容。需要具备以下能力：1. 调用微博热搜/话题API或网页爬取热点内容 2. 支持按分类筛选（娱乐、社会、科技、搞笑等）3. 提取微博内容、点赞数、评论数、转发数等关键指标 4. 数据清洗和格式化输出 5. ",
-  "status": "completed",
-  "toolCount": 42,
-  "durationSec": 186,
-  "patterns": [
-    "成功创建/修改了 1 个文件: data/system/templates/weibo-scraper/`，包含：",
-    "完成了 42 步工具调用的复杂任务 (186秒)"
-  ],
-  "lessons": [],
-  "plan": {
-    "scenario": "[TASK from master]",
-    "keywords": [
-      "任务编号",
-      "任务",
-      "务编",
-      "编号",
-      "任务编",
-      "务编号",
-      "优先级",
-      "优先"
-    ],
-    "steps": [
-      "✅ 微博热搜 API 爬取",
-      "✅ 分类筛选（娱乐/社会/科技/搞笑/体育/财经）",
-      "✅ 关键指标提取（点赞/评论/转发/阅读）",
-      "✅ 数据清洗与格式化 JSON 输出",
-      "✅ 定时爬取支持（Cron）"
-    ],
-    "confidence": 0.9
-  }
-}
 \ No newline at end of file
-{
-  "timestamp": "2026-03-28T02:57:41.177Z",
-  "convId": "callback-T-WEIBO-TRAINING",
-  "task": "[TASK_CALLBACK]\n-TRAINING\n执行者: innova",
-  "status": "completed",
-  "toolCount": 14,
-  "durationSec": 31291,
-  "patterns": [
-    "完成了 14 步工具调用的复杂任务 (31291秒)"
-  ],
-  "lessons": [],
-  "plan": null
-}
 \ No newline at end of file
-{
-  "timestamp": "2026-03-28T03:08:41.731Z",
-  "convId": "conv-mn9r2azu",
-  "task": "我需要给weibo-scraper 一些功能上的反馈： 他不但需要爬取当日的热点新闻，还需要读到这个",
-  "status": "completed",
-  "toolCount": 17,
-  "durationSec": 65,
-  "patterns": [
-    "完成了 17 步工具调用的复杂任务 (65秒)"
-  ],
-  "lessons": [],
-  "plan": null
-}
 \ No newline at end of file
-{
-  "timestamp": "2026-03-28T03:15:23.199Z",
-  "convId": "conv-mn9r99sf",
-  "task": "帮我训练一个新的 agent，taobao_client ，用来跟淘宝的客户端交互读取数据，/Use",
-  "status": "completed",
-  "toolCount": 23,
-  "durationSec": 142,
-  "patterns": [
-    "成功创建/修改了 7 个文件: data/system/templates/taobao_client/agent.md`, data/system/templates/taobao_client/config.json`, data/system/templates/taobao_client/identity.md`",
-    "完成了 23 步工具调用的复杂任务 (142秒)"
-  ],
-  "lessons": [],
-  "plan": null
-}
 \ No newline at end of file
-{
-  "timestamp": "2026-03-28T03:17:12.397Z",
-  "convId": "conv-mn9r2azu",
-  "task": "我需要给weibo-scraper 一些功能上的反馈： 他不但需要爬取当日的热点新闻，还需要读到这个",
-  "status": "completed",
-  "toolCount": 18,
-  "durationSec": 576,
-  "patterns": [
-    "完成了 18 步工具调用的复杂任务 (576秒)"
-  ],
-  "lessons": [],
-  "plan": null
-}
 \ No newline at end of file
-{
-  "timestamp": "2026-03-28T03:26:26.723Z",
-  "convId": "callback-T-mn9rekhp",
-  "task": "[TASK_CALLBACK]\n执行者: innovator\n状态",
-  "status": "completed",
-  "toolCount": 9,
-  "durationSec": 81,
-  "patterns": [
-    "完成了 9 步工具调用的复杂任务 (81秒)"
-  ],
-  "lessons": [],
-  "plan": null
-}
 \ No newline at end of file
-{
-  "taskId": "T-mn9rt6fn",
-  "from": "innovator",
-  "status": "done",
-  "summary": "taobao-native 技能训练完成。DIGEST: benchmark_v1.md + 3 good + 2 bad。EVOLVING: 2轮迭代，最终分数 90.75，达标。已部署到 taobao_client 模板。",
-  "timestamp": "2026-03-28T03:41:00Z",
-  "score": 90.75,
-  "skillName": "taobao-native",
-  "targetAgentId": "taobao_client"
-}
 \ No newline at end of file
-{
-  "timestamp": "2026-03-28T03:41:31.489Z",
-  "convId": "callback-T-mn9rd6bi",
-  "task": "[TASK_CALLBACK]\n执行者: innovator\n状态",
-  "status": "completed",
-  "toolCount": 12,
-  "durationSec": 211,
-  "patterns": [
-    "完成了 12 步工具调用的复杂任务 (211秒)"
-  ],
-  "lessons": [],
-  "plan": null
-}
 \ No newline at end of file
-{
-  "timestamp": "2026-03-28T03:49:58.038Z",
-  "convId": "callback-T-mn9s0xwb",
-  "task": "[TASK_CALLBACK]\n执行者: innovator\n状态",
-  "status": "completed",
-  "toolCount": 6,
-  "durationSec": 46,
-  "patterns": [
-    "完成了 6 步工具调用的复杂任务 (46秒)"
-  ],
-  "lessons": [],
-  "plan": null
-}
 \ No newline at end of file
-{
-  "timestamp": "2026-03-28T03:53:06.428Z",
-  "convId": "callback-T-mn9s7uac",
-  "task": "[TASK_CALLBACK]\n执行者: innovator\n状态",
-  "status": "completed",
-  "toolCount": 5,
-  "durationSec": 45,
-  "patterns": [
-    "成功创建/修改了 1 个文件: data/system/templates/taobao_client/config.json",
-    "完成了 5 步工具调用的复杂任务 (45秒)"
-  ],
-  "lessons": [],
-  "plan": null
-}
 \ No newline at end of file
-{
-  "timestamp": "2026-03-28T03:53:58.153Z",
-  "convId": "callback-T-mn9s5y2m",
-  "task": "[TASK_CALLBACK]\n执行者: innovator\n状态",
-  "status": "completed",
-  "toolCount": 11,
-  "durationSec": 100,
-  "patterns": [
-    "成功创建/修改了 1 个文件: data/system/templates/taobao_client/.claude/skills/main_skill/SKILL.md`",
-    "完成了 11 步工具调用的复杂任务 (100秒)"
-  ],
-  "lessons": [],
-  "plan": null
-}
 \ No newline at end of file
-{
-  "timestamp": "2026-03-28T16:01:57.615Z",
-  "convId": "create-test-shopping_assistant-mnainf5r",
-  "task": "新员工 test:shopping_assistant 已由脚手架创建空白目录在 `../test/",
-  "status": "completed",
-  "toolCount": 18,
-  "durationSec": 124,
-  "patterns": [
-    "完成了 18 步工具调用的复杂任务 (124秒)"
-  ],
-  "lessons": [],
-  "plan": null
-}
 \ No newline at end of file
-{
-  "timestamp": "2026-03-28T16:37:35.883Z",
-  "convId": "callback-T-mnajplu9",
-  "task": "[TASK_CALLBACK]\n执行者: innovator\n状态",
-  "status": "completed",
-  "toolCount": 8,
-  "durationSec": 41,
-  "patterns": [
-    "成功创建/修改了 4 个文件: data/system/templates/weibo-scraper/workspace/evolve/final_SKILL.md`。, data/system/templates/weibo-scraper/workspace/evolve/`。已向, workspace/evolve/final_SKILL.md`。",
-    "完成了 8 步工具调用的复杂任务 (41秒)",
-    "涉及技能训练/评分/进化流程"
-  ],
-  "lessons": [],
-  "plan": null
-}
 \ No newline at end of file
-{
-  "timestamp": "2026-03-28T16:52:45.889Z",
-  "convId": "callback-T-mnajlf22",
-  "task": "[TASK_CALLBACK]\n执行者: innovator\n状态",
-  "status": "completed",
-  "toolCount": 18,
-  "durationSec": 1374,
-  "patterns": [
-    "完成了 18 步工具调用的复杂任务 (1374秒)"
-  ],
-  "lessons": [],
-  "plan": null
-}
 \ No newline at end of file
-{
-  "timestamp": "2026-03-29T02:39:01.347Z",
-  "convId": "tpl-gen-interactive-video-selector-mnb5fo37",
-  "task": "请你为模板 Agent \"interactive-video-selector\" 编写完整的定义文件",
-  "status": "completed",
-  "toolCount": 14,
-  "durationSec": 80,
-  "patterns": [
-    "成功创建/修改了 5 个文件: data/system/templates/interactive-video-selector/agent.md, data/system/templates/interactive-video-selector/identity.md, data/system/templates/interactive-video-selector/soul.md",
-    "完成了 14 步工具调用的复杂任务 (80秒)"
-  ],
-  "lessons": [],
-  "plan": null
-}
 \ No newline at end of file
-{
-  "timestamp": "2026-03-30T07:00:52.580Z",
-  "convId": "tpl-gen-competitor-spy-mncu6r7c",
-  "task": "请你为模板 Agent \"competitor-spy\" 编写完整的定义文件。\n模板位于 `../.",
-  "status": "completed",
-  "toolCount": 16,
-  "durationSec": 151,
-  "patterns": [
-    "成功创建/修改了 1 个文件: data/system/templates/competitor-spy/`",
-    "完成了 16 步工具调用的复杂任务 (151秒)"
-  ],
-  "lessons": [],
-  "plan": null
-}
 \ No newline at end of file
-{
-  "timestamp": "2026-03-30T07:01:47.318Z",
-  "convId": "tpl-gen-trend-scout-global-mncu6r4b",
-  "task": "请你为模板 Agent \"trend-scout-global\" 编写完整的定义文件。\n模板位于 `",
-  "status": "completed",
-  "toolCount": 23,
-  "durationSec": 206,
-  "patterns": [
-    "完成了 23 步工具调用的复杂任务 (206秒)"
-  ],
-  "lessons": [],
-  "plan": null
-}
 \ No newline at end of file
-{
-  "timestamp": "2026-03-30T07:03:55.700Z",
-  "convId": "tpl-gen-data-harvester-mncu7fzp",
-  "task": "请你为模板 Agent \"data-harvester\" 编写完整的定义文件。\n模板位于 `../.",
-  "status": "completed",
-  "toolCount": 27,
-  "durationSec": 302,
-  "patterns": [
-    "完成了 27 步工具调用的复杂任务 (302秒)"
-  ],
-  "lessons": [],
-  "plan": null
-}
 \ No newline at end of file
-{
-  "timestamp": "2026-03-30T09:18:57.840Z",
-  "convId": "create-test-idearrun-create-mncz5n4w",
-  "task": "新员工 test:idearrun-create 已由脚手架创建空白目录在 `../test/age",
-  "status": "completed",
-  "toolCount": 17,
-  "durationSec": 89,
-  "patterns": [
-    "完成了 17 步工具调用的复杂任务 (89秒)"
-  ],
-  "lessons": [],
-  "plan": null
-}
 \ No newline at end of file
-# Session 摘要
-
-- **时间**: 2026-03-28 03:53:58
-- **对话ID**: callback-T-mn9s5y2m
-- **状态**: completed
-- **工具调用**: 11 次
-- **耗时**: 2分钟
-
-## 任务
-[TASK_CALLBACK]
-执行者: innovator
-状态
-
-## 结果
-我来检查任务执行结果，并查看我的计划文件。
-
-
-让我检查一下我的记忆和相关任务记录。
-
-
-让我检查 Innovator 产出的训练结果文件。
-
-
-Innovator 已成功完成 taobao_client 的技能训练。让我更新记忆并向 master 汇报。
-
-
-Innovator 已成功完成 taobao_client 的 taobao-native 技能训练。现在向 master 汇报任务完成。
-
-
-**任务完成确认！**
-
-Innovator 已成功完成 taobao_client 的 taobao-native 技能训练：
-
-| 指标 | 值 |
-|------|-----|
-| 任务编号 | T-mn9s5y2m |
-| 执行者 | innovator |
-| 得分 | **90.75** ✅ (≥90 达标) |
-| 迭代次数 | 2 次 |
-| 最终 SKILL.md | 已部署到 `data/system/templates/taobao_client/.claude/skills/main_skill/SKILL.md` |
-
-已向 master 汇报任务完成。
-
-## 涉及文件
-- data/system/templates/taobao_client/.claude/skills/main_skill/SKILL.md`
-# Session 摘要
-
-- **时间**: 2026-03-28 16:01:57
-- **对话ID**: create-test-shopping_assistant-mnainf5r
-- **状态**: completed
-- **工具调用**: 18 次
-- **耗时**: 2分钟
-
-## 任务
-新员工 test:shopping_assistant 已由脚手架创建空白目录在 `../test/
-
-## 结果
-...shopping_assistant 创建标准模板文件。
-
-
-任务完成。已为 `test:shopping_assistant` 补充完整的定义文件：
-
-```
-test/agents/shopping_assistant/
-├── agent.md      # Agent 主定义 (角色、能力、输出格式)
-├── identity.md   # 身份定义 (核心价值观、行为准则)
-├── soul.md       # 灵魂定义 (人格、决策树、内在独白)
-├── user.md       # 用户定义 (目标用户、需求、用法示例)
-└── config.json   # 配置文件
-```
-
-**Agent 概览**：
-- **ID**: shopping_assistant
-- **名称**: 🛒 购物助手
-- **类型**: specialist
-- **核心能力**: 商品搜索、多平台比价、优惠券查找、订单追踪
-
-该 Agent 定位为购物领域的专业助手，擅长帮助用户完成从搜索到比价再到决策的全流程。目前技能待训练，如有需要可派发给 Innovator 进行 RL 训练。
-# Session 摘要
-
-- **时间**: 2026-03-28 16:37:35
-- **对话ID**: callback-T-mnajplu9
-- **状态**: completed
-- **工具调用**: 8 次
-- **耗时**: 41秒
-
-## 任务
-[TASK_CALLBACK]
-执行者: innovator
-状态
-
-## 结果
-... (≥90 达标) |
-| 版本 | v2.0.0 |
-| 主要改进 | 多榜爬取（5个榜单）、数据合并存储、HTML/JSON 输出 |
-
-技能已更新至 `data/system/templates/weibo-scraper/workspace/evolve/final_SKILL.md`。
-
-现在向 master 汇报任务完成：
-
-
-任务已完成并汇报。
-
-**任务完成确认！**
-
-| 指标 | 值 |
-|------|-----|
-| 任务编号 | T-mnajplu9 |
-| 执行者 | innovator |
-| 技能名称 | weibo_multi_list_scraper |
-| 最终版本 | v2.0.0 |
-| Benchmark 得分 | **90.9** ✅ (≥90 达标) |
-| 改进内容 | 多榜爬取（5个榜单）+ 数据合并存储 + HTML/JSON 输出 |
-
-`final_SKILL.md` 已生成并部署到 `data/system/templates/weibo-scraper/workspace/evolve/`。已向 master 汇报任务完成。
-
-## 涉及文件
-- data/system/templates/weibo-scraper/workspace/evolve/final_SKILL.md`。
-- data/system/templates/weibo-scraper/workspace/evolve/`。已向
-- workspace/evolve/final_SKILL.md`。
-- workspace/evolve/`。已向
-# Agent Creator 记忆
-
-## 近期记忆
-- [2026-03-29] [TASK_CALLBACK] → weibo-scraper weibo_multi_list_scraper 技能训练完成 - 90.9分✅ (v2.0.0)
-# Session 摘要
-
-- **时间**: 2026-03-28 16:52:45
-- **对话ID**: callback-T-mnajlf22
-- **状态**: completed
-- **工具调用**: 18 次
-- **耗时**: 23分钟
-
-## 任务
-[TASK_CALLBACK]
-执行者: innovator
-状态
-
-## 结果
-收到 Innovator 的 weibo-scraper v2 技能训练完成回调。让我先查看计划文件了解当前任务进度。
-
-
-收到 Innovator 的任务完成回调。weibo-scraper v2 技能训练（多榜爬取）已完成，得分 90.9 分，达到 90 分验收标准。
-
-让我更新任务记录并向 master 汇报。
-
-
-Innovator 完成了 weibo-scraper v2 技能训练。现在向 master 汇报。
-
-
-任务已完成并向 master 汇报。
-
-**汇报内容:**
-- 任务编号: T-mnajlf22
-- 执行者: innovator
-- 状态: ✅ 已完成
-- 结果: weibo-scraper v2 技能训练完成！多榜爬取技能 (weibo-multi-list-scraper) 达到 90.9 分 (v2.0.0)
-- 产出: SKILL.md 已部署到 weibo-scraper 模板
-
-根据 todo.md，当前没有其他待处理的任务。innovator 训练任务已全部完成。
-# Session 摘要
-
-- **时间**: 2026-03-29 02:39:01
-- **对话ID**: tpl-gen-interactive-video-selector-mnb5fo37
-- **状态**: completed
-- **工具调用**: 14 次
-- **耗时**: 1分钟
-
-## 任务
-请你为模板 Agent "interactive-video-selector" 编写完整的定义文件
-
-## 结果
-...ctor/agent.md |
-| identity.md | data/system/templates/interactive-video-selector/identity.md |
-| soul.md | data/system/templates/interactive-video-selector/soul.md |
-| user.md | data/system/templates/interactive-video-selector/user.md |
-| tools.md | data/system/templates/interactive-video-selector/tools.md |
-
-**目录结构验证**：
-- ✅ agent.md (2066 bytes)
-- ✅ identity.md (674 bytes)
-- ✅ soul.md (614 bytes)
-- ✅ user.md (485 bytes)
-- ✅ tools.md (542 bytes)
-
-文件内容已完成更新，包含了完整的 Agent 定义、身份信息、思维方式、用户上下文和工具使用规范。
-
-## 涉及文件
-- data/system/templates/interactive-video-selector/agent.md
-- data/system/templates/interactive-video-selector/identity.md
-- data/system/templates/interactive-video-selector/soul.md
-- data/system/templates/interactive-video-selector/user.md
-- data/system/templates/interactive-video-selector/tools.md
-# Session 摘要
-
-- **时间**: 2026-03-30 07:00:52
-- **对话ID**: tpl-gen-competitor-spy-mncu6r7c
-- **状态**: completed
-- **工具调用**: 16 次
-- **耗时**: 3分钟
-
-## 任务
-请你为模板 Agent "competitor-spy" 编写完整的定义文件。
-模板位于 `../.
-
-## 结果
-... (ID: call_function_djjcgp5743pn_1)
-
-
-
-文件显示大小为0，让我验证实际内容。
-
-🔧 [TOOL_CALL] Read (ID: call_function_49wghlpax95a_1)
-
-
-完成！已为 `competitor-spy` 模板创建完整的定义文件：
-
-**任务完成确认！**
-
-| 项目 | 详情 |
-|------|------|
-| **目录** | `data/system/templates/competitor-spy/` |
-| **文件** | agent.md, identity.md, soul.md, user.md, tools.md |
-| **Agent** | 竞品侦探 🕵️ |
-| **类型** | 通用执行型 |
-
-**定义摘要**：
-- **Agent ID**: competitor-spy
-- **核心能力**: 竞品监控、数据爬取、情报分析、报告生成
-- **输入**: 竞品名称/URL + 任务类型 + 平台 + 执行计划
-- **输出**: JSON 结构化数据 + Markdown 报告
-
-## 涉及文件
-- data/system/templates/competitor-spy/`
-# Session 摘要
-
-- **时间**: 2026-03-30 07:01:47
-- **对话ID**: tpl-gen-trend-scout-global-mncu6r4b
-- **状态**: completed
-- **工具调用**: 23 次
-- **耗时**: 3分钟
-
-## 任务
-请你为模板 Agent "trend-scout-global" 编写完整的定义文件。
-模板位于 `
-
-## 结果
-...nt.md      (2.4K) - Agent 主定义 (角色、能力、输出格式)
-├── identity.md   (351B) - 身份定义 (名称、Emoji、问候语)
-├── soul.md      (523B) - 灵魂定义 (思维方式、沟通风格、价值观)
-├── user.md      (425B) - 用户上下文 (上级、偏好、典型任务)
-├── tools.md     (607B) - 工具备注 (安全规则、外部依赖)
-├── config.json  - 配置 (平台列表、Skills)
-├── heartbeat.md
-├── plan.md
-└── todo.md
-```
-
-核心内容：
-- **能力**: 监控 TikTok/Instagram Reels/Twitter/YouTube Shorts，追踪 BGM、转场特效、热门网文 IP 风格
-- **平台**: 4 个主要平台的数据采集优先级
-- **技能**: scrapling_crawler + TikTok/Ins API
-- **输出**: 包含热度指标、趋势分析、IP 风格识别的多维报告
-# Session 摘要
-
-- **时间**: 2026-03-30 07:03:55
-- **对话ID**: tpl-gen-data-harvester-mncu7fzp
-- **状态**: completed
-- **工具调用**: 27 次
-- **耗时**: 5分钟
-
-## 任务
-请你为模板 Agent "data-harvester" 编写完整的定义文件。
-模板位于 `../.
-
-## 结果
-...。已为模板 Agent "data-harvester" 编写完整的定义文件：
-
-| 文件 | 大小 | 说明 |
-|------|------|------|
-| `agent.md` | 807B | Agent 主定义（基础信息、核心价值观、行为准则） |
-| `identity.md` | 2.3KB | 身份定义（能力列表、支持的数据源、工作流、输出格式） |
-| `soul.md` | 1.5KB | 灵魂/价值观（人格、决策树、质量标准） |
-| `tools.md` | 1.6KB | 工具定义（技能配置、工具策略、执行工作流） |
-| `user.md` | 1.6KB | 用户界面（目标用户、输入输出格式、使用示例） |
-
-目录结构验证：
-```
-data-harvester/
-├── agent.md      ✅
-├── identity.md   ✅
-├── soul.md       ✅
-├── tools.md      ✅
-├── user.md       ✅
-├── config.json
-├── memory/
-└── workspace/
-```