宜博 / lemonboss

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Commit 110869fbae1df6fec52375eedf7d525899c97726

Authored by yibo 2026-03-30 18:42:02 +0800

1 parent 82105ac7

chore: add rembg python dependency to install.sh

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data/system/agents/.tasks/T-mnbu7kcy.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mnbu7kcy",
	3	+ "from": "innovator",
	4	+ "to": "hunter",
	5	+ "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/",
	6	+ "priority": "P0",
	7	+ "goalId": "G-lemonv-drama-enhance",
	8	+ "nodeId": "G-lemonv-drama-enhance.1",
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mnbu7kcy",
	12	+ "createdAt": "2026-03-29T14:11:13.236Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-29T14:12:55.148Z",
	15	+ "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"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mnbuaomt.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mnbuaomt",
	3	+ "from": "innovator",
	4	+ "to": "digester",
	5	+ "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/",
	6	+ "priority": "P0",
	7	+ "goalId": "G-lemonv-drama-v6",
	8	+ "nodeId": "G-lemonv-drama-v6.2",
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mnbuaomt",
	12	+ "createdAt": "2026-03-29T14:13:38.742Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-29T14:15:29.056Z",
	15	+ "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)"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mnbudwvk.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mnbudwvk",
	3	+ "from": "master",
	4	+ "to": "innovator",
	5	+ "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",
	6	+ "priority": "P1",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mnbudwvk",
	12	+ "createdAt": "2026-03-29T14:16:09.393Z",
	13	+ "status": "dispatched"
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mnbue6qb.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mnbue6qb",
	3	+ "from": "innovator",
	4	+ "to": "evolver",
	5	+ "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/",
	6	+ "priority": "P0",
	7	+ "goalId": "G-lemonv-drma",
	8	+ "nodeId": "G-lemonv-drma.3",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mnbue6qb",
	12	+ "createdAt": "2026-03-29T14:16:22.165Z",
	13	+ "status": "dispatched"
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mnbuer1r.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mnbuer1r",
	3	+ "from": "innovator",
	4	+ "to": "evolver",
	5	+ "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/",
	6	+ "priority": "P0",
	7	+ "goalId": "G-lemonv-drama-v6",
	8	+ "nodeId": "G-lemonv-drama-v6.1",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mnbuer1r",
	12	+ "createdAt": "2026-03-29T14:16:48.496Z",
	13	+ "status": "dispatched"
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mnbvk2xx.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mnbvk2xx",
	3	+ "from": "master",
	4	+ "to": "test:weibo-scraper",
	5	+ "task": "爬取最新微博内容",
	6	+ "priority": "P1",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mnbvk2xx",
	12	+ "createdAt": "2026-03-29T14:48:56.807Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-29T14:50:25.562Z",
	15	+ "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)"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mnbvndl4.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mnbvndl4",
	3	+ "from": "master",
	4	+ "to": "test:interactive-video-selector",
	5	+ "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",
	6	+ "priority": "P0",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mnbvndl4",
	12	+ "createdAt": "2026-03-29T14:51:30.569Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-29T14:52:14.954Z",
	15	+ "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"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mnbvretc.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mnbvretc",
	3	+ "from": "master",
	4	+ "to": "test:lemonv_drama",
	5	+ "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。",
	6	+ "priority": "P1",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mnbvretc",
	12	+ "createdAt": "2026-03-29T14:54:38.785Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-29T14:56:11.597Z",
	15	+ "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"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mnbvsnda.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mnbvsnda",
	3	+ "from": "master",
	4	+ "to": "test:lemonv_drama",
	5	+ "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",
	6	+ "priority": "P0",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mnbvsnda",
	12	+ "createdAt": "2026-03-29T14:55:36.527Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-29T14:57:44.855Z",
	15	+ "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"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncavo9p.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncavo9p",
	3	+ "from": "master",
	4	+ "to": "test:interactive-video-selector",
	5	+ "task": "从weibo-scraper的产出中选择视频选题，源文件在 data/companies/test/workspace/weibo-scraper/",
	6	+ "priority": "P1",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mncavo9p",
	12	+ "createdAt": "2026-03-29T21:57:51.902Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-29T23:59:04.894Z",
	15	+ "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"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncl1sti.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncl1sti",
	3	+ "from": "master",
	4	+ "to": "test:interactive-video-selector",
	5	+ "task": "从weibo-scraper的产出中选择视频选题，源文件在 data/companies/test/workspace/weibo-scraper/",
	6	+ "priority": "P1",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mncl1sti",
	12	+ "createdAt": "2026-03-30T02:42:33.898Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T02:45:03.510Z",
	15	+ "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"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncl5hxs.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncl5hxs",
	3	+ "from": "master",
	4	+ "to": "test:interactive-video-selector",
	5	+ "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/",
	6	+ "priority": "P1",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mncl5hxs",
	12	+ "createdAt": "2026-03-30T02:45:26.418Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T02:47:46.442Z",
	15	+ "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)"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncl612p.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncl612p",
	3	+ "from": "master",
	4	+ "to": "test:lemonv_drama",
	5	+ "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/",
	6	+ "priority": "P0",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mncl612p",
	12	+ "createdAt": "2026-03-30T02:45:51.220Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T02:49:16.793Z",
	15	+ "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。"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mnclaj64.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mnclaj64",
	3	+ "from": "master",
	4	+ "to": "test:lemonv_drama",
	5	+ "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",
	6	+ "priority": "P1",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mnclaj64",
	12	+ "createdAt": "2026-03-30T02:49:21.295Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T02:53:17.119Z",
	15	+ "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。"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mnclb0ik.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mnclb0ik",
	3	+ "from": "master",
	4	+ "to": "test:lemonv_drama",
	5	+ "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",
	6	+ "priority": "P0",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mnclb0ik",
	12	+ "createdAt": "2026-03-30T02:49:43.774Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T02:52:12.248Z",
	15	+ "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）"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mnclclls.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mnclclls",
	3	+ "from": "master",
	4	+ "to": "test:lemonv_drama",
	5	+ "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",
	6	+ "priority": "P1",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mnclclls",
	12	+ "createdAt": "2026-03-30T02:50:57.764Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T02:56:14.370Z",
	15	+ "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。"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mnclju66.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mnclju66",
	3	+ "from": "master",
	4	+ "to": "test:interactive-video-selector",
	5	+ "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/",
	6	+ "priority": "P1",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mnclju66",
	12	+ "createdAt": "2026-03-30T02:56:35.458Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T02:57:56.494Z",
	15	+ "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/"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncln07j.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncln07j",
	3	+ "from": "master",
	4	+ "to": "test:lemonv_drama",
	5	+ "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",
	6	+ "priority": "P1",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mncln07j",
	12	+ "createdAt": "2026-03-30T02:59:03.253Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T03:02:30.727Z",
	15	+ "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。"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mnclnjfq.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mnclnjfq",
	3	+ "from": "master",
	4	+ "to": "test:lemonv_drama",
	5	+ "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/",
	6	+ "priority": "P1",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mnclnjfq",
	12	+ "createdAt": "2026-03-30T02:59:28.170Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T03:00:51.097Z",
	15	+ "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"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncloxzz.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncloxzz",
	3	+ "from": "master",
	4	+ "to": "test:lemonv_drama",
	5	+ "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",
	6	+ "priority": "P1",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mncloxzz",
	12	+ "createdAt": "2026-03-30T03:00:33.700Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T03:05:49.108Z",
	15	+ "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。"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mnclwez2.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mnclwez2",
	3	+ "from": "master",
	4	+ "to": "test:weibo-scraper",
	5	+ "task": "爬取最新微博内容",
	6	+ "priority": "P1",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mnclwez2",
	12	+ "createdAt": "2026-03-30T03:06:22.293Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T03:07:17.576Z",
	15	+ "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。"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mnclyspc.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mnclyspc",
	3	+ "from": "master",
	4	+ "to": "test:interactive-video-selector",
	5	+ "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。",
	6	+ "priority": "P0",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mnclyspc",
	12	+ "createdAt": "2026-03-30T03:08:13.398Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T03:08:48.234Z",
	15	+ "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。"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mnclz2ma.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mnclz2ma",
	3	+ "from": "master",
	4	+ "to": "test:interactive-video-selector",
	5	+ "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/",
	6	+ "priority": "P1",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mnclz2ma",
	12	+ "createdAt": "2026-03-30T03:08:26.247Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T03:13:04.656Z",
	15	+ "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"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncm2ghh.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncm2ghh",
	3	+ "from": "master",
	4	+ "to": "test:lemonv_drama",
	5	+ "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。",
	6	+ "priority": "P0",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mncm2ghh",
	12	+ "createdAt": "2026-03-30T03:11:04.186Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T03:14:10.580Z",
	15	+ "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"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncm2xv7.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncm2xv7",
	3	+ "from": "master",
	4	+ "to": "test:lemonv_drama",
	5	+ "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",
	6	+ "priority": "P1",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mncm2xv7",
	12	+ "createdAt": "2026-03-30T03:11:26.712Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T03:17:46.644Z",
	15	+ "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"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncmd7sj.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncmd7sj",
	3	+ "from": "master",
	4	+ "to": "test:lemonv_drama",
	5	+ "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",
	6	+ "priority": "P0",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mncmd7sj",
	12	+ "createdAt": "2026-03-30T03:19:26.132Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T03:21:59.514Z",
	15	+ "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+"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncme4re.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncme4re",
	3	+ "from": "master",
	4	+ "to": "test:weibo-scraper",
	5	+ "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。",
	6	+ "priority": "P1",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mncme4re",
	12	+ "createdAt": "2026-03-30T03:20:08.861Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T03:24:09.514Z",
	15	+ "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+)"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncmlkdq.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncmlkdq",
	3	+ "from": "master",
	4	+ "to": "test:interactive-video-selector",
	5	+ "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。",
	6	+ "priority": "P0",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mncmlkdq",
	12	+ "createdAt": "2026-03-30T03:25:55.700Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T03:27:25.100Z",
	15	+ "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。"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncu9yvq.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncu9yvq",
	3	+ "from": "innovator",
	4	+ "to": "hunter",
	5	+ "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",
	6	+ "priority": "P0",
	7	+ "goalId": "G-tsg-001",
	8	+ "nodeId": "G-tsg-001.1",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncu9yvq",
	12	+ "createdAt": "2026-03-30T07:00:51.550Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T07:13:10.061Z",
	15	+ "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)"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncubdq8.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncubdq8",
	3	+ "from": "innovator",
	4	+ "to": "hunter",
	5	+ "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",
	6	+ "priority": "P0",
	7	+ "goalId": "G-comp-spy",
	8	+ "nodeId": "G-comp-spy.1",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncubdq8",
	12	+ "createdAt": "2026-03-30T07:01:57.447Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T07:12:15.616Z",
	15	+ "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。"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncubs5w.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncubs5w",
	3	+ "from": "innovator",
	4	+ "to": "hunter",
	5	+ "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)",
	6	+ "priority": "P0",
	7	+ "goalId": "G-data-harvester",
	8	+ "nodeId": "G-data-harvester.1",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncubs5w",
	12	+ "createdAt": "2026-03-30T07:02:16.154Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T07:10:17.451Z",
	15	+ "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。"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncue787.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncue787",
	3	+ "from": "innovator",
	4	+ "to": "hunter",
	5	+ "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/",
	6	+ "priority": "P0",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncue787",
	12	+ "createdAt": "2026-03-30T07:04:08.991Z",
	13	+ "status": "dispatched"
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncue7hc.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncue7hc",
	3	+ "from": "innovator",
	4	+ "to": "hunter",
	5	+ "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。",
	6	+ "priority": "P0",
	7	+ "goalId": "G-clickbait-train",
	8	+ "nodeId": "G-clickbait-train.1",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncue7hc",
	12	+ "createdAt": "2026-03-30T07:04:09.313Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T07:12:22.068Z",
	15	+ "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）"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncuebf1.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncuebf1",
	3	+ "from": "innovator",
	4	+ "to": "hunter",
	5	+ "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",
	6	+ "priority": "P0",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncuebf1",
	12	+ "createdAt": "2026-03-30T07:04:14.415Z",
	13	+ "status": "dispatched"
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncuedja.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncuedja",
	3	+ "from": "innovator",
	4	+ "to": "hunter",
	5	+ "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)",
	6	+ "priority": "P0",
	7	+ "goalId": "G-auto-publisher",
	8	+ "nodeId": "G-auto-publisher.1",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncuedja",
	12	+ "createdAt": "2026-03-30T07:04:17.161Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T07:07:55.469Z",
	15	+ "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)"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncufg80.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncufg80",
	3	+ "from": "innovator",
	4	+ "to": "hunter",
	5	+ "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",
	6	+ "priority": "P0",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncufg80",
	12	+ "createdAt": "2026-03-30T07:05:07.304Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T07:16:57.898Z",
	15	+ "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。"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncufnbb.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncufnbb",
	3	+ "from": "innovator",
	4	+ "to": "hunter",
	5	+ "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",
	6	+ "priority": "P0",
	7	+ "goalId": "G-pd-training",
	8	+ "nodeId": "G-pd-training.1",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncufnbb",
	12	+ "createdAt": "2026-03-30T07:05:16.493Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T07:12:21.846Z",
	15	+ "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。"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncufp7k.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncufp7k",
	3	+ "from": "innovator",
	4	+ "to": "hunter",
	5	+ "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。",
	6	+ "priority": "P0",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncufp7k",
	12	+ "createdAt": "2026-03-30T07:05:18.946Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T07:12:16.587Z",
	15	+ "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/"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncufq4r.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncufq4r",
	3	+ "from": "innovator",
	4	+ "to": "hunter",
	5	+ "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",
	6	+ "priority": "P0",
	7	+ "goalId": "G-story-arc",
	8	+ "nodeId": "G-story-arc.1",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncufq4r",
	12	+ "createdAt": "2026-03-30T07:05:20.140Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T07:15:24.525Z",
	15	+ "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/"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncuhz3e.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncuhz3e",
	3	+ "from": "master",
	4	+ "to": "drama:trend-scout-cn",
	5	+ "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/",
	6	+ "priority": "P0",
	7	+ "goalId": "G-001",
	8	+ "nodeId": "G-001.1.1",
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mncuhz3e",
	12	+ "createdAt": "2026-03-30T07:07:05.075Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T07:11:51.551Z",
	15	+ "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。"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncui5d9.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncui5d9",
	3	+ "from": "master",
	4	+ "to": "drama:story-architect",
	5	+ "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/",
	6	+ "priority": "P0",
	7	+ "goalId": "G-001",
	8	+ "nodeId": "G-001.2.1",
	9	+ "parentTaskId": null,
	10	+ "sourceConvId": null,
	11	+ "executionConvId": "task-T-mncui5d9",
	12	+ "createdAt": "2026-03-30T07:07:13.200Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T07:13:05.741Z",
	15	+ "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。"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncujgfx.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncujgfx",
	3	+ "from": "innovator",
	4	+ "to": "digester",
	5	+ "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)",
	6	+ "priority": "P0",
	7	+ "goalId": "G-auto-publisher",
	8	+ "nodeId": "G-auto-publisher.2",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncujgfx",
	12	+ "createdAt": "2026-03-30T07:08:14.207Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T07:13:18.056Z",
	15	+ "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。"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mnculhkp.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mnculhkp",
	3	+ "from": "innovator",
	4	+ "to": "digester",
	5	+ "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",
	6	+ "priority": "P0",
	7	+ "goalId": "G-auto-publisher",
	8	+ "nodeId": "G-auto-publisher.2",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mnculhkp",
	12	+ "createdAt": "2026-03-30T07:09:48.993Z",
	13	+ "status": "dispatched"
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncumyfu.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncumyfu",
	3	+ "from": "innovator",
	4	+ "to": "digester",
	5	+ "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/",
	6	+ "priority": "P0",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncumyfu",
	12	+ "createdAt": "2026-03-30T07:10:57.505Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T07:19:15.150Z",
	15	+ "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。"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncunc8c.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncunc8c",
	3	+ "from": "innovator",
	4	+ "to": "digester",
	5	+ "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/。",
	6	+ "priority": "P0",
	7	+ "goalId": "G-comp-spy",
	8	+ "nodeId": "G-comp-spy.2",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncunc8c",
	12	+ "createdAt": "2026-03-30T07:11:15.380Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T07:20:16.938Z",
	15	+ "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。"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncunkxu.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncunkxu",
	3	+ "from": "innovator",
	4	+ "to": "hunter",
	5	+ "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",
	6	+ "priority": "P0",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncunkxu",
	12	+ "createdAt": "2026-03-30T07:11:26.667Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T07:18:54.054Z",
	15	+ "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"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncuom5g.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncuom5g",
	3	+ "from": "innovator",
	4	+ "to": "digester",
	5	+ "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）",
	6	+ "priority": "P0",
	7	+ "goalId": "G-pd-training",
	8	+ "nodeId": "G-pd-training.2",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncuom5g",
	12	+ "createdAt": "2026-03-30T07:12:14.894Z",
	13	+ "status": "dispatched"
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncupe7m.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncupe7m",
	3	+ "from": "innovator",
	4	+ "to": "digester",
	5	+ "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。",
	6	+ "priority": "P0",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncupe7m",
	12	+ "createdAt": "2026-03-30T07:12:51.259Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T07:18:22.680Z",
	15	+ "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"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncupgip.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncupgip",
	3	+ "from": "innovator",
	4	+ "to": "digester",
	5	+ "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",
	6	+ "priority": "P0",
	7	+ "goalId": "G-story-arc",
	8	+ "nodeId": "G-story-arc.2",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncupgip",
	12	+ "createdAt": "2026-03-30T07:12:54.244Z",
	13	+ "status": "dispatched"
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncups4u.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncups4u",
	3	+ "from": "innovator",
	4	+ "to": "digester",
	5	+ "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/",
	6	+ "priority": "P0",
	7	+ "goalId": "G-001",
	8	+ "nodeId": "G-001.2",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncups4u",
	12	+ "createdAt": "2026-03-30T07:13:09.303Z",
	13	+ "status": "dispatched"
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncupsxm.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncupsxm",
	3	+ "from": "innovator",
	4	+ "to": "digester",
	5	+ "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/。",
	6	+ "priority": "P0",
	7	+ "goalId": "G-clickbait",
	8	+ "nodeId": "G-clickbait.2",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncupsxm",
	12	+ "createdAt": "2026-03-30T07:13:10.337Z",
	13	+ "status": "dispatched"
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncuqxuz.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncuqxuz",
	3	+ "from": "innovator",
	4	+ "to": "digester",
	5	+ "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/",
	6	+ "priority": "P0",
	7	+ "goalId": "T-mncufp7k",
	8	+ "nodeId": "T-mncufp7k.2",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncuqxuz",
	12	+ "createdAt": "2026-03-30T07:14:03.379Z",
	13	+ "status": "dispatched"
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncusls1.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncusls1",
	3	+ "from": "innovator",
	4	+ "to": "digester",
	5	+ "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",
	6	+ "priority": "P0",
	7	+ "goalId": "G-comp-spy",
	8	+ "nodeId": "G-comp-spy.2",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncusls1",
	12	+ "createdAt": "2026-03-30T07:15:21.036Z",
	13	+ "status": "dispatched"
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncusnkb.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncusnkb",
	3	+ "from": "innovator",
	4	+ "to": "digester",
	5	+ "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)",
	6	+ "priority": "P0",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncusnkb",
	12	+ "createdAt": "2026-03-30T07:15:23.342Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T07:19:18.865Z",
	15	+ "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"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncutmeo.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncutmeo",
	3	+ "from": "innovator",
	4	+ "to": "evolver",
	5	+ "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/。",
	6	+ "priority": "P0",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncutmeo",
	12	+ "createdAt": "2026-03-30T07:16:08.508Z",
	13	+ "status": "dispatched"
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncuut1e.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncuut1e",
	3	+ "from": "innovator",
	4	+ "to": "digester",
	5	+ "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",
	6	+ "priority": "P0",
	7	+ "goalId": "G-TSG",
	8	+ "nodeId": "G-TSG.2",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncuut1e",
	12	+ "createdAt": "2026-03-30T07:17:03.756Z",
	13	+ "status": "dispatched"
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncuuuv1.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncuuuv1",
	3	+ "from": "innovator",
	4	+ "to": "digester",
	5	+ "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/",
	6	+ "priority": "P0",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncuuuv1",
	12	+ "createdAt": "2026-03-30T07:17:06.112Z",
	13	+ "status": "dispatched"
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncuuvgv.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncuuvgv",
	3	+ "from": "innovator",
	4	+ "to": "evolver",
	5	+ "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",
	6	+ "priority": "P0",
	7	+ "goalId": "G-003",
	8	+ "nodeId": "G-003.3",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncuuvgv",
	12	+ "createdAt": "2026-03-30T07:17:06.899Z",
	13	+ "status": "dispatched"
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncuuvo6.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncuuvo6",
	3	+ "from": "innovator",
	4	+ "to": "digester",
	5	+ "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",
	6	+ "priority": "P0",
	7	+ "goalId": "G-story-architect",
	8	+ "nodeId": "G-story-architect.2",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncuuvo6",
	12	+ "createdAt": "2026-03-30T07:17:07.161Z",
	13	+ "status": "dispatched"
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncuw40n.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncuw40n",
	3	+ "from": "innovator",
	4	+ "to": "digester",
	5	+ "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",
	6	+ "priority": "P0",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncuw40n",
	12	+ "createdAt": "2026-03-30T07:18:04.642Z",
	13	+ "status": "dispatched"
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncuwbwa.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncuwbwa",
	3	+ "from": "innovator",
	4	+ "to": "digester",
	5	+ "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",
	6	+ "priority": "P0",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncuwbwa",
	12	+ "createdAt": "2026-03-30T07:18:14.844Z",
	13	+ "status": "dispatched"
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncuwedo.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncuwedo",
	3	+ "from": "innovator",
	4	+ "to": "evolver",
	5	+ "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",
	6	+ "priority": "P0",
	7	+ "goalId": null,
	8	+ "nodeId": null,
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncuwedo",
	12	+ "createdAt": "2026-03-30T07:18:18.064Z",
	13	+ "status": "dispatched"
	14	+}
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...	...

data/system/agents/.tasks/T-mncuxdta.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncuxdta",
	3	+ "from": "innovator",
	4	+ "to": "evolver",
	5	+ "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",
	6	+ "priority": "P0",
	7	+ "goalId": "G-pd-training",
	8	+ "nodeId": "G-pd-training.3",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncuxdta",
	12	+ "createdAt": "2026-03-30T07:19:03.995Z",
	13	+ "status": "dispatched"
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncuynqg.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncuynqg",
	3	+ "from": "innovator",
	4	+ "to": "evolver",
	5	+ "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/",
	6	+ "priority": "P0",
	7	+ "goalId": "G-mncupe7m",
	8	+ "nodeId": "G-mncupe7m.3",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncuynqg",
	12	+ "createdAt": "2026-03-30T07:20:03.507Z",
	13	+ "status": "dispatched"
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mncz867y.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mncz867y",
	3	+ "from": "innovator",
	4	+ "to": "hunter",
	5	+ "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/",
	6	+ "priority": "P0",
	7	+ "goalId": "G-idearruncreate-fde",
	8	+ "nodeId": "G-idearruncreate-fde.1",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mncz867y",
	12	+ "createdAt": "2026-03-30T09:19:25.836Z",
	13	+ "status": "dispatched"
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mnczgrwi.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mnczgrwi",
	3	+ "from": "innovator",
	4	+ "to": "digester",
	5	+ "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/",
	6	+ "priority": "P0",
	7	+ "goalId": "G-idearruncreate-fde",
	8	+ "nodeId": "G-idearruncreate-fde.2",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mnczgrwi",
	12	+ "createdAt": "2026-03-30T09:26:07.187Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T09:28:47.373Z",
	15	+ "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"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mnczlwlc.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mnczlwlc",
	3	+ "from": "innovator",
	4	+ "to": "evolver",
	5	+ "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/",
	6	+ "priority": "P0",
	7	+ "goalId": "G-idearruncreate-fde",
	8	+ "nodeId": "G-idearruncreate-fde.3",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mnczlwlc",
	12	+ "createdAt": "2026-03-30T09:30:06.545Z",
	13	+ "status": "done",
	14	+ "completedAt": "2026-03-30T09:33:28.686Z",
	15	+ "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"
	16	+}
0	17	\ No newline at end of file
...	...

data/system/agents/.tasks/T-mnczm565.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mnczm565",
	3	+ "from": "innovator",
	4	+ "to": "evolver",
	5	+ "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/",
	6	+ "priority": "P0",
	7	+ "goalId": "G-idearun-create",
	8	+ "nodeId": "G-idearun-create.3",
	9	+ "parentTaskId": "T-mnbudwvk",
	10	+ "sourceConvId": "task-T-mnbudwvk",
	11	+ "executionConvId": "task-T-mnczm565",
	12	+ "createdAt": "2026-03-30T09:30:17.652Z",
	13	+ "status": "dispatched"
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/agent-creator/memory/cerebellum/task-from-master.md 0 → 100644

View file @110869f

	1	+# 方案: [TASK from master]
	2	+
	3	+> 创建时间: 2026-03-27
	4	+
	5	+## 适用条件
	6	+- 类似任务: [TASK from master]
	7	+
	8	+## 执行步骤
	9	+1. ✅ 微博热搜 API 爬取
	10	+2. ✅ 分类筛选（娱乐/社会/科技/搞笑/体育/财经）
	11	+3. ✅ 关键指标提取（点赞/评论/转发/阅读）
	12	+4. ✅ 数据清洗与格式化 JSON 输出
	13	+5. ✅ 定时爬取支持（Cron）
	14	+
	15	+## 注意事项
	16	+- 自动从对话 task-T-mn972nl4 提取
	17	+- 耗时 186秒, 42 次工具调用
...	...

data/system/agents/agent-creator/memory/hippocampus-index.json 0 → 100644

View file @110869f

	1	+[
	2	+ {
	3	+ "scenario": "[TASK from master]",
	4	+ "keywords": [
	5	+ "任务编号",
	6	+ "任务",
	7	+ "务编",
	8	+ "编号",
	9	+ "任务编",
	10	+ "务编号",
	11	+ "优先级",
	12	+ "优先"
	13	+ ],
	14	+ "planFile": "task-from-master.md",
	15	+ "confidence": 0.9,
	16	+ "useCount": 20,
	17	+ "lastUsed": "2026-03-29",
	18	+ "createdAt": "2026-03-27"
	19	+ }
	20	+]
0	21	\ No newline at end of file
...	...

data/system/agents/agent-creator/memory/hippocampus.md 0 → 100644

View file @110869f

	1	+# 海马体 — 经验记忆索引
	2	+
	3	+> 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。
	4	+> ⚠️ 此文件由系统自动生成，勿手动编辑。真实数据在 hippocampus-index.json。
	5	+
	6	+\| # \| 情景模式 \| 匹配关键词 \| 方案文件 \| 置信度 \| 使用次数 \| 上次使用 \|
	7	+\|---\|---------\|-----------\|---------\|--------\|---------\|----------\|
	8	+\| 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 \| - \|
...	...

data/system/agents/agent-creator/memory/reflections/reflect-1774633866649.json 0 → 100644

View file @110869f

	1	+{
	2	+ "timestamp": "2026-03-27T17:51:06.647Z",
	3	+ "convId": "task-T-mn972nl4",
	4	+ "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. ",
	5	+ "status": "completed",
	6	+ "toolCount": 42,
	7	+ "durationSec": 186,
	8	+ "patterns": [
	9	+ "成功创建/修改了 1 个文件: data/system/templates/weibo-scraper/`，包含：",
	10	+ "完成了 42 步工具调用的复杂任务 (186秒)"
	11	+ ],
	12	+ "lessons": [],
	13	+ "plan": {
	14	+ "scenario": "[TASK from master]",
	15	+ "keywords": [
	16	+ "任务编号",
	17	+ "任务",
	18	+ "务编",
	19	+ "编号",
	20	+ "任务编",
	21	+ "务编号",
	22	+ "优先级",
	23	+ "优先"
	24	+ ],
	25	+ "steps": [
	26	+ "✅ 微博热搜 API 爬取",
	27	+ "✅ 分类筛选（娱乐/社会/科技/搞笑/体育/财经）",
	28	+ "✅ 关键指标提取（点赞/评论/转发/阅读）",
	29	+ "✅ 数据清洗与格式化 JSON 输出",
	30	+ "✅ 定时爬取支持（Cron）"
	31	+ ],
	32	+ "confidence": 0.9
	33	+ }
	34	+}
0	35	\ No newline at end of file
...	...

data/system/agents/agent-creator/memory/reflections/reflect-1774666661177.json 0 → 100644

View file @110869f

	1	+{
	2	+ "timestamp": "2026-03-28T02:57:41.177Z",
	3	+ "convId": "callback-T-WEIBO-TRAINING",
	4	+ "task": "[TASK_CALLBACK]\n-TRAINING\n执行者: innova",
	5	+ "status": "completed",
	6	+ "toolCount": 14,
	7	+ "durationSec": 31291,
	8	+ "patterns": [
	9	+ "完成了 14 步工具调用的复杂任务 (31291秒)"
	10	+ ],
	11	+ "lessons": [],
	12	+ "plan": null
	13	+}
0	14	\ No newline at end of file
...	...

data/system/agents/agent-creator/memory/reflections/reflect-1774667321731.json 0 → 100644

View file @110869f

	1	+{
	2	+ "timestamp": "2026-03-28T03:08:41.731Z",
	3	+ "convId": "conv-mn9r2azu",
	4	+ "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",
	5	+ "status": "completed",
	6	+ "toolCount": 17,
	7	+ "durationSec": 65,
	8	+ "patterns": [
	9	+ "完成了 17 步工具调用的复杂任务 (65秒)"
	10	+ ],
	11	+ "lessons": [],
	12	+ "plan": null
	13	+}
0	14	\ No newline at end of file
...	...

data/system/agents/agent-creator/memory/reflections/reflect-1774667723199.json 0 → 100644

View file @110869f

	1	+{
	2	+ "timestamp": "2026-03-28T03:15:23.199Z",
	3	+ "convId": "conv-mn9r99sf",
	4	+ "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",
	5	+ "status": "completed",
	6	+ "toolCount": 23,
	7	+ "durationSec": 142,
	8	+ "patterns": [
	9	+ "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`",
	10	+ "完成了 23 步工具调用的复杂任务 (142秒)"
	11	+ ],
	12	+ "lessons": [],
	13	+ "plan": null
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/agent-creator/memory/reflections/reflect-1774667832397.json 0 → 100644

View file @110869f

	1	+{
	2	+ "timestamp": "2026-03-28T03:17:12.397Z",
	3	+ "convId": "conv-mn9r2azu",
	4	+ "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",
	5	+ "status": "completed",
	6	+ "toolCount": 18,
	7	+ "durationSec": 576,
	8	+ "patterns": [
	9	+ "完成了 18 步工具调用的复杂任务 (576秒)"
	10	+ ],
	11	+ "lessons": [],
	12	+ "plan": null
	13	+}
0	14	\ No newline at end of file
...	...

data/system/agents/agent-creator/memory/reflections/reflect-1774668386723.json 0 → 100644

View file @110869f

	1	+{
	2	+ "timestamp": "2026-03-28T03:26:26.723Z",
	3	+ "convId": "callback-T-mn9rekhp",
	4	+ "task": "[TASK_CALLBACK]\n执行者: innovator\n状态",
	5	+ "status": "completed",
	6	+ "toolCount": 9,
	7	+ "durationSec": 81,
	8	+ "patterns": [
	9	+ "完成了 9 步工具调用的复杂任务 (81秒)"
	10	+ ],
	11	+ "lessons": [],
	12	+ "plan": null
	13	+}
0	14	\ No newline at end of file
...	...

data/system/agents/agent-creator/memory/reflections/reflect-1774669259999.json 0 → 100644

View file @110869f

	1	+{
	2	+ "taskId": "T-mn9rt6fn",
	3	+ "from": "innovator",
	4	+ "status": "done",
	5	+ "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。",
	6	+ "timestamp": "2026-03-28T03:41:00Z",
	7	+ "score": 90.75,
	8	+ "skillName": "taobao-native",
	9	+ "targetAgentId": "taobao_client"
	10	+}
0	11	\ No newline at end of file
...	...

data/system/agents/agent-creator/memory/reflections/reflect-1774669291489.json 0 → 100644

View file @110869f

	1	+{
	2	+ "timestamp": "2026-03-28T03:41:31.489Z",
	3	+ "convId": "callback-T-mn9rd6bi",
	4	+ "task": "[TASK_CALLBACK]\n执行者: innovator\n状态",
	5	+ "status": "completed",
	6	+ "toolCount": 12,
	7	+ "durationSec": 211,
	8	+ "patterns": [
	9	+ "完成了 12 步工具调用的复杂任务 (211秒)"
	10	+ ],
	11	+ "lessons": [],
	12	+ "plan": null
	13	+}
0	14	\ No newline at end of file
...	...

data/system/agents/agent-creator/memory/reflections/reflect-1774669798038.json 0 → 100644

View file @110869f

	1	+{
	2	+ "timestamp": "2026-03-28T03:49:58.038Z",
	3	+ "convId": "callback-T-mn9s0xwb",
	4	+ "task": "[TASK_CALLBACK]\n执行者: innovator\n状态",
	5	+ "status": "completed",
	6	+ "toolCount": 6,
	7	+ "durationSec": 46,
	8	+ "patterns": [
	9	+ "完成了 6 步工具调用的复杂任务 (46秒)"
	10	+ ],
	11	+ "lessons": [],
	12	+ "plan": null
	13	+}
0	14	\ No newline at end of file
...	...

data/system/agents/agent-creator/memory/reflections/reflect-1774669986428.json 0 → 100644

View file @110869f

	1	+{
	2	+ "timestamp": "2026-03-28T03:53:06.428Z",
	3	+ "convId": "callback-T-mn9s7uac",
	4	+ "task": "[TASK_CALLBACK]\n执行者: innovator\n状态",
	5	+ "status": "completed",
	6	+ "toolCount": 5,
	7	+ "durationSec": 45,
	8	+ "patterns": [
	9	+ "成功创建/修改了 1 个文件: data/system/templates/taobao_client/config.json",
	10	+ "完成了 5 步工具调用的复杂任务 (45秒)"
	11	+ ],
	12	+ "lessons": [],
	13	+ "plan": null
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/agent-creator/memory/reflections/reflect-1774670038153.json 0 → 100644

View file @110869f

	1	+{
	2	+ "timestamp": "2026-03-28T03:53:58.153Z",
	3	+ "convId": "callback-T-mn9s5y2m",
	4	+ "task": "[TASK_CALLBACK]\n执行者: innovator\n状态",
	5	+ "status": "completed",
	6	+ "toolCount": 11,
	7	+ "durationSec": 100,
	8	+ "patterns": [
	9	+ "成功创建/修改了 1 个文件: data/system/templates/taobao_client/.claude/skills/main_skill/SKILL.md`",
	10	+ "完成了 11 步工具调用的复杂任务 (100秒)"
	11	+ ],
	12	+ "lessons": [],
	13	+ "plan": null
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/agent-creator/memory/reflections/reflect-1774713717615.json 0 → 100644

View file @110869f

	1	+{
	2	+ "timestamp": "2026-03-28T16:01:57.615Z",
	3	+ "convId": "create-test-shopping_assistant-mnainf5r",
	4	+ "task": "新员工 test:shopping_assistant 已由脚手架创建空白目录在 `../test/",
	5	+ "status": "completed",
	6	+ "toolCount": 18,
	7	+ "durationSec": 124,
	8	+ "patterns": [
	9	+ "完成了 18 步工具调用的复杂任务 (124秒)"
	10	+ ],
	11	+ "lessons": [],
	12	+ "plan": null
	13	+}
0	14	\ No newline at end of file
...	...

data/system/agents/agent-creator/memory/reflections/reflect-1774715855893.json 0 → 100644

View file @110869f

	1	+{
	2	+ "timestamp": "2026-03-28T16:37:35.883Z",
	3	+ "convId": "callback-T-mnajplu9",
	4	+ "task": "[TASK_CALLBACK]\n执行者: innovator\n状态",
	5	+ "status": "completed",
	6	+ "toolCount": 8,
	7	+ "durationSec": 41,
	8	+ "patterns": [
	9	+ "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`。",
	10	+ "完成了 8 步工具调用的复杂任务 (41秒)",
	11	+ "涉及技能训练/评分/进化流程"
	12	+ ],
	13	+ "lessons": [],
	14	+ "plan": null
	15	+}
0	16	\ No newline at end of file
...	...

data/system/agents/agent-creator/memory/reflections/reflect-1774716765890.json 0 → 100644

View file @110869f

	1	+{
	2	+ "timestamp": "2026-03-28T16:52:45.889Z",
	3	+ "convId": "callback-T-mnajlf22",
	4	+ "task": "[TASK_CALLBACK]\n执行者: innovator\n状态",
	5	+ "status": "completed",
	6	+ "toolCount": 18,
	7	+ "durationSec": 1374,
	8	+ "patterns": [
	9	+ "完成了 18 步工具调用的复杂任务 (1374秒)"
	10	+ ],
	11	+ "lessons": [],
	12	+ "plan": null
	13	+}
0	14	\ No newline at end of file
...	...

data/system/agents/agent-creator/memory/reflections/reflect-1774751941351.json 0 → 100644

View file @110869f

	1	+{
	2	+ "timestamp": "2026-03-29T02:39:01.347Z",
	3	+ "convId": "tpl-gen-interactive-video-selector-mnb5fo37",
	4	+ "task": "请你为模板 Agent \"interactive-video-selector\" 编写完整的定义文件",
	5	+ "status": "completed",
	6	+ "toolCount": 14,
	7	+ "durationSec": 80,
	8	+ "patterns": [
	9	+ "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",
	10	+ "完成了 14 步工具调用的复杂任务 (80秒)"
	11	+ ],
	12	+ "lessons": [],
	13	+ "plan": null
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/agent-creator/memory/reflections/reflect-1774854052580.json 0 → 100644

View file @110869f

	1	+{
	2	+ "timestamp": "2026-03-30T07:00:52.580Z",
	3	+ "convId": "tpl-gen-competitor-spy-mncu6r7c",
	4	+ "task": "请你为模板 Agent \"competitor-spy\" 编写完整的定义文件。\n模板位于 `../.",
	5	+ "status": "completed",
	6	+ "toolCount": 16,
	7	+ "durationSec": 151,
	8	+ "patterns": [
	9	+ "成功创建/修改了 1 个文件: data/system/templates/competitor-spy/`",
	10	+ "完成了 16 步工具调用的复杂任务 (151秒)"
	11	+ ],
	12	+ "lessons": [],
	13	+ "plan": null
	14	+}
0	15	\ No newline at end of file
...	...

data/system/agents/agent-creator/memory/reflections/reflect-1774854107318.json 0 → 100644

View file @110869f

	1	+{
	2	+ "timestamp": "2026-03-30T07:01:47.318Z",
	3	+ "convId": "tpl-gen-trend-scout-global-mncu6r4b",
	4	+ "task": "请你为模板 Agent \"trend-scout-global\" 编写完整的定义文件。\n模板位于 `",
	5	+ "status": "completed",
	6	+ "toolCount": 23,
	7	+ "durationSec": 206,
	8	+ "patterns": [
	9	+ "完成了 23 步工具调用的复杂任务 (206秒)"
	10	+ ],
	11	+ "lessons": [],
	12	+ "plan": null
	13	+}
0	14	\ No newline at end of file
...	...

data/system/agents/agent-creator/memory/reflections/reflect-1774854235700.json 0 → 100644

View file @110869f

	1	+{
	2	+ "timestamp": "2026-03-30T07:03:55.700Z",
	3	+ "convId": "tpl-gen-data-harvester-mncu7fzp",
	4	+ "task": "请你为模板 Agent \"data-harvester\" 编写完整的定义文件。\n模板位于 `../.",
	5	+ "status": "completed",
	6	+ "toolCount": 27,
	7	+ "durationSec": 302,
	8	+ "patterns": [
	9	+ "完成了 27 步工具调用的复杂任务 (302秒)"
	10	+ ],
	11	+ "lessons": [],
	12	+ "plan": null
	13	+}
0	14	\ No newline at end of file
...	...

data/system/agents/agent-creator/memory/reflections/reflect-1774862337840.json 0 → 100644

View file @110869f

	1	+{
	2	+ "timestamp": "2026-03-30T09:18:57.840Z",
	3	+ "convId": "create-test-idearrun-create-mncz5n4w",
	4	+ "task": "新员工 test:idearrun-create 已由脚手架创建空白目录在 `../test/age",
	5	+ "status": "completed",
	6	+ "toolCount": 17,
	7	+ "durationSec": 89,
	8	+ "patterns": [
	9	+ "完成了 17 步工具调用的复杂任务 (89秒)"
	10	+ ],
	11	+ "lessons": [],
	12	+ "plan": null
	13	+}
0	14	\ No newline at end of file
...	...

data/system/agents/agent-creator/memory/short-term/session-1774670038152.md 0 → 100644

View file @110869f

	1	+# Session 摘要
	2	+
	3	+- 时间: 2026-03-28 03:53:58
	4	+- 对话ID: callback-T-mn9s5y2m
	5	+- 状态: completed
	6	+- 工具调用: 11 次
	7	+- 耗时: 2分钟
	8	+
	9	+## 任务
	10	+[TASK_CALLBACK]
	11	+执行者: innovator
	12	+状态
	13	+
	14	+## 结果
	15	+我来检查任务执行结果，并查看我的计划文件。
	16	+
	17	+
	18	+让我检查一下我的记忆和相关任务记录。
	19	+
	20	+
	21	+让我检查 Innovator 产出的训练结果文件。
	22	+
	23	+
	24	+Innovator 已成功完成 taobao_client 的技能训练。让我更新记忆并向 master 汇报。
	25	+
	26	+
	27	+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。
	28	+
	29	+
	30	+任务完成确认！
	31	+
	32	+Innovator 已成功完成 taobao_client 的 taobao-native 技能训练：
	33	+
	34	+\| 指标 \| 值 \|
	35	+\|------\|-----\|
	36	+\| 任务编号 \| T-mn9s5y2m \|
	37	+\| 执行者 \| innovator \|
	38	+\| 得分 \| 90.75 ✅ (≥90 达标) \|
	39	+\| 迭代次数 \| 2 次 \|
	40	+\| 最终 SKILL.md \| 已部署到 `data/system/templates/taobao_client/.claude/skills/main_skill/SKILL.md` \|
	41	+
	42	+已向 master 汇报任务完成。
	43	+
	44	+## 涉及文件
	45	+- data/system/templates/taobao_client/.claude/skills/main_skill/SKILL.md`
...	...

data/system/agents/agent-creator/memory/short-term/session-1774713717614.md 0 → 100644

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	1	+# Session 摘要
	2	+
	3	+- 时间: 2026-03-28 16:01:57
	4	+- 对话ID: create-test-shopping_assistant-mnainf5r
	5	+- 状态: completed
	6	+- 工具调用: 18 次
	7	+- 耗时: 2分钟
	8	+
	9	+## 任务
	10	+新员工 test:shopping_assistant 已由脚手架创建空白目录在 `../test/
	11	+
	12	+## 结果
	13	+...shopping_assistant 创建标准模板文件。
	14	+
	15	+
	16	+任务完成。已为 `test:shopping_assistant` 补充完整的定义文件：
	17	+
	18	+```
	19	+test/agents/shopping_assistant/
	20	+├── agent.md # Agent 主定义 (角色、能力、输出格式)
	21	+├── identity.md # 身份定义 (核心价值观、行为准则)
	22	+├── soul.md # 灵魂定义 (人格、决策树、内在独白)
	23	+├── user.md # 用户定义 (目标用户、需求、用法示例)
	24	+└── config.json # 配置文件
	25	+```
	26	+
	27	+Agent 概览：
	28	+- ID: shopping_assistant
	29	+- 名称: 🛒 购物助手
	30	+- 类型: specialist
	31	+- 核心能力: 商品搜索、多平台比价、优惠券查找、订单追踪
	32	+
	33	+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。
...	...

data/system/agents/agent-creator/memory/short-term/session-1774715855874.md 0 → 100644

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	1	+# Session 摘要
	2	+
	3	+- 时间: 2026-03-28 16:37:35
	4	+- 对话ID: callback-T-mnajplu9
	5	+- 状态: completed
	6	+- 工具调用: 8 次
	7	+- 耗时: 41秒
	8	+
	9	+## 任务
	10	+[TASK_CALLBACK]
	11	+执行者: innovator
	12	+状态
	13	+
	14	+## 结果
	15	+... (≥90 达标) \|
	16	+\| 版本 \| v2.0.0 \|
	17	+\| 主要改进 \| 多榜爬取（5个榜单）、数据合并存储、HTML/JSON 输出 \|
	18	+
	19	+技能已更新至 `data/system/templates/weibo-scraper/workspace/evolve/final_SKILL.md`。
	20	+
	21	+现在向 master 汇报任务完成：
	22	+
	23	+
	24	+任务已完成并汇报。
	25	+
	26	+任务完成确认！
	27	+
	28	+\| 指标 \| 值 \|
	29	+\|------\|-----\|
	30	+\| 任务编号 \| T-mnajplu9 \|
	31	+\| 执行者 \| innovator \|
	32	+\| 技能名称 \| weibo_multi_list_scraper \|
	33	+\| 最终版本 \| v2.0.0 \|
	34	+\| Benchmark 得分 \| 90.9 ✅ (≥90 达标) \|
	35	+\| 改进内容 \| 多榜爬取（5个榜单）+ 数据合并存储 + HTML/JSON 输出 \|
	36	+
	37	+`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。
	38	+
	39	+## 涉及文件
	40	+- data/system/templates/weibo-scraper/workspace/evolve/final_SKILL.md`。
	41	+- data/system/templates/weibo-scraper/workspace/evolve/`。已向
	42	+- workspace/evolve/final_SKILL.md`。
	43	+- workspace/evolve/`。已向
...	...

data/system/agents/agent-creator/memory/short-term/session-1774716000000.md 0 → 100644

View file @110869f

	1	+# Agent Creator 记忆
	2	+
	3	+## 近期记忆
	4	+- [2026-03-29] [TASK_CALLBACK] → weibo-scraper weibo_multi_list_scraper 技能训练完成 - 90.9分✅ (v2.0.0)
...	...

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	1	+# Session 摘要
	2	+
	3	+- 时间: 2026-03-28 16:52:45
	4	+- 对话ID: callback-T-mnajlf22
	5	+- 状态: completed
	6	+- 工具调用: 18 次
	7	+- 耗时: 23分钟
	8	+
	9	+## 任务
	10	+[TASK_CALLBACK]
	11	+执行者: innovator
	12	+状态
	13	+
	14	+## 结果
	15	+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。
	16	+
	17	+
	18	+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。
	19	+
	20	+让我更新任务记录并向 master 汇报。
	21	+
	22	+
	23	+Innovator 完成了 weibo-scraper v2 技能训练。现在向 master 汇报。
	24	+
	25	+
	26	+任务已完成并向 master 汇报。
	27	+
	28	+汇报内容:
	29	+- 任务编号: T-mnajlf22
	30	+- 执行者: innovator
	31	+- 状态: ✅ 已完成
	32	+- 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)
	33	+- 产出: SKILL.md 已部署到 weibo-scraper 模板
	34	+
	35	+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。
...	...

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	1	+# Session 摘要
	2	+
	3	+- 时间: 2026-03-29 02:39:01
	4	+- 对话ID: tpl-gen-interactive-video-selector-mnb5fo37
	5	+- 状态: completed
	6	+- 工具调用: 14 次
	7	+- 耗时: 1分钟
	8	+
	9	+## 任务
	10	+请你为模板 Agent "interactive-video-selector" 编写完整的定义文件
	11	+
	12	+## 结果
	13	+...ctor/agent.md \|
	14	+\| identity.md \| data/system/templates/interactive-video-selector/identity.md \|
	15	+\| soul.md \| data/system/templates/interactive-video-selector/soul.md \|
	16	+\| user.md \| data/system/templates/interactive-video-selector/user.md \|
	17	+\| tools.md \| data/system/templates/interactive-video-selector/tools.md \|
	18	+
	19	+目录结构验证：
	20	+- ✅ agent.md (2066 bytes)
	21	+- ✅ identity.md (674 bytes)
	22	+- ✅ soul.md (614 bytes)
	23	+- ✅ user.md (485 bytes)
	24	+- ✅ tools.md (542 bytes)
	25	+
	26	+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。
	27	+
	28	+## 涉及文件
	29	+- data/system/templates/interactive-video-selector/agent.md
	30	+- data/system/templates/interactive-video-selector/identity.md
	31	+- data/system/templates/interactive-video-selector/soul.md
	32	+- data/system/templates/interactive-video-selector/user.md
	33	+- data/system/templates/interactive-video-selector/tools.md
...	...

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	1	+# Session 摘要
	2	+
	3	+- 时间: 2026-03-30 07:00:52
	4	+- 对话ID: tpl-gen-competitor-spy-mncu6r7c
	5	+- 状态: completed
	6	+- 工具调用: 16 次
	7	+- 耗时: 3分钟
	8	+
	9	+## 任务
	10	+请你为模板 Agent "competitor-spy" 编写完整的定义文件。
	11	+模板位于 `../.
	12	+
	13	+## 结果
	14	+... (ID: call_function_djjcgp5743pn_1)
	15	+
	16	+
	17	+
	18	+文件显示大小为0，让我验证实际内容。
	19	+
	20	+🔧 [TOOL_CALL] Read (ID: call_function_49wghlpax95a_1)
	21	+
	22	+
	23	+完成！已为 `competitor-spy` 模板创建完整的定义文件：
	24	+
	25	+任务完成确认！
	26	+
	27	+\| 项目 \| 详情 \|
	28	+\|------\|------\|
	29	+\| 目录 \| `data/system/templates/competitor-spy/` \|
	30	+\| 文件 \| agent.md, identity.md, soul.md, user.md, tools.md \|
	31	+\| Agent \| 竞品侦探 🕵️ \|
	32	+\| 类型 \| 通用执行型 \|
	33	+
	34	+定义摘要：
	35	+- Agent ID: competitor-spy
	36	+- 核心能力: 竞品监控、数据爬取、情报分析、报告生成
	37	+- 输入: 竞品名称/URL + 任务类型 + 平台 + 执行计划
	38	+- 输出: JSON 结构化数据 + Markdown 报告
	39	+
	40	+## 涉及文件
	41	+- data/system/templates/competitor-spy/`
...	...

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	1	+# Session 摘要
	2	+
	3	+- 时间: 2026-03-30 07:01:47
	4	+- 对话ID: tpl-gen-trend-scout-global-mncu6r4b
	5	+- 状态: completed
	6	+- 工具调用: 23 次
	7	+- 耗时: 3分钟
	8	+
	9	+## 任务
	10	+请你为模板 Agent "trend-scout-global" 编写完整的定义文件。
	11	+模板位于 `
	12	+
	13	+## 结果
	14	+...nt.md (2.4K) - Agent 主定义 (角色、能力、输出格式)
	15	+├── identity.md (351B) - 身份定义 (名称、Emoji、问候语)
	16	+├── soul.md (523B) - 灵魂定义 (思维方式、沟通风格、价值观)
	17	+├── user.md (425B) - 用户上下文 (上级、偏好、典型任务)
	18	+├── tools.md (607B) - 工具备注 (安全规则、外部依赖)
	19	+├── config.json - 配置 (平台列表、Skills)
	20	+├── heartbeat.md
	21	+├── plan.md
	22	+└── todo.md
	23	+```
	24	+
	25	+核心内容：
	26	+- 能力: 监控 TikTok/Instagram Reels/Twitter/YouTube Shorts，追踪 BGM、转场特效、热门网文 IP 风格
	27	+- 平台: 4 个主要平台的数据采集优先级
	28	+- 技能: scrapling_crawler + TikTok/Ins API
	29	+- 输出: 包含热度指标、趋势分析、IP 风格识别的多维报告
...	...

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	1	+# Session 摘要
	2	+
	3	+- 时间: 2026-03-30 07:03:55
	4	+- 对话ID: tpl-gen-data-harvester-mncu7fzp
	5	+- 状态: completed
	6	+- 工具调用: 27 次
	7	+- 耗时: 5分钟
	8	+
	9	+## 任务
	10	+请你为模板 Agent "data-harvester" 编写完整的定义文件。
	11	+模板位于 `../.
	12	+
	13	+## 结果
	14	+...。已为模板 Agent "data-harvester" 编写完整的定义文件：
	15	+
	16	+\| 文件 \| 大小 \| 说明 \|
	17	+\|------\|------\|------\|
	18	+\| `agent.md` \| 807B \| Agent 主定义（基础信息、核心价值观、行为准则） \|
	19	+\| `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） \|
	20	+\| `soul.md` \| 1.5KB \| 灵魂/价值观（人格、决策树、质量标准） \|
	21	+\| `tools.md` \| 1.6KB \| 工具定义（技能配置、工具策略、执行工作流） \|
	22	+\| `user.md` \| 1.6KB \| 用户界面（目标用户、输入输出格式、使用示例） \|
	23	+
	24	+目录结构验证：
	25	+```
	26	+data-harvester/
	27	+├── agent.md ✅
	28	+├── identity.md ✅
	29	+├── soul.md ✅
	30	+├── tools.md ✅
	31	+├── user.md ✅
	32	+├── config.json
	33	+├── memory/
	34	+└── workspace/
	35	+```
...	...

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	1	+# Session 摘要
	2	+
	3	+- 时间: 2026-03-30 09:18:57
	4	+- 对话ID: create-test-idearrun-create-mncz5n4w
	5	+- 状态: completed
	6	+- 工具调用: 17 次
	7	+- 耗时: 1分钟
	8	+
	9	+## 任务
	10	+新员工 test:idearrun-create 已由脚手架创建空白目录在 `../test/age
	11	+
	12	+## 结果
	13	+...CALL] Bash (ID: call_function_460cdwj37nwj_1)
	14	+
	15	+
	16	+任务完成！已为模板 Agent `idearuncreate`（创意跑酷生成师）补充完整的定义文件：
	17	+
	18	+\| 文件 \| 大小 \| 说明 \|
	19	+\|------\|------\|------\|
	20	+\| `agent.md` \| 799B \| Agent 主定义（ID、名称、核心能力、输出格式） \|
	21	+\| `identity.md` \| 1.4KB \| 身份定义（职责、能力列表、输出JSON格式） \|
	22	+\| `soul.md` \| 763B \| 灵魂定义（思维方式、沟通风格、价值观） \|
	23	+\| `user.md` \| 644B \| 用户上下文（上级、典型任务、触发方式） \|
	24	+\| `tools.md` \| 870B \| 工具使用备注（可用工具、技能脚本、配置说明） \|
	25	+
	26	+核心能力确认：
	27	+- `idearuncreate` 已具备 `create-drama` 技能，可将素材（新闻/故事/段子/历史事件）转化为完整的 classic 互动剧本，包含角色立绘图、背景图、封面图及台词脚本，并写入数据库。
...	...

chore: add rembg python dependency to install.sh

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