Commit 0cf1a86ca259740f905afc72dfe9f21748b8e63b
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b15d8570
chore: update system state and workspace
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data/abc/pipelines/daily-content-pipeline.json
0 → 100644
| 1 | +{ | |
| 2 | + "id": "daily-content-pipeline", | |
| 3 | + "name": "每日内容生产流水线", | |
| 4 | + "description": "从热点抓取到内容发布的完整流水线，实现7×24小时自动化运行", | |
| 5 | + "companyId": "abc", | |
| 6 | + "steps": [ | |
| 7 | + { | |
| 8 | + "index": 0, | |
| 9 | + "name": "热点抓取", | |
| 10 | + "agent": "trend-hunter", | |
| 11 | + "prompt": "从网上抓取军事、娱乐、体育等各类时事热点，构建包含约100个热点的选题库", | |
| 12 | + "dependsOn": null, | |
| 13 | + "timeout": 30, | |
| 14 | + "required": true | |
| 15 | + }, | |
| 16 | + { | |
| 17 | + "index": 1, | |
| 18 | + "name": "选题筛选", | |
| 19 | + "agent": "topic-selector", | |
| 20 | + "prompt": "从热点选题库中筛选出5-10个最有价值的选题，基于市场热度和IP价值评估", | |
| 21 | + "dependsOn": 0, | |
| 22 | + "timeout": 15, | |
| 23 | + "required": true | |
| 24 | + }, | |
| 25 | + { | |
| 26 | + "index": 2, | |
| 27 | + "name": "深度研究", | |
| 28 | + "agent": "researcher", | |
| 29 | + "prompt": "针对每个目标选题进行背景调查、人物形象塑造，并生成相应的台词和剧本", | |
| 30 | + "dependsOn": 1, | |
| 31 | + "timeout": 60, | |
| 32 | + "required": true | |
| 33 | + }, | |
| 34 | + { | |
| 35 | + "index": 3, | |
| 36 | + "name": "剧本整理", | |
| 37 | + "agent": "screenwriter", | |
| 38 | + "prompt": "将深度研究生成的剧本按固定格式整理完毕，确保符合生产端要求", | |
| 39 | + "dependsOn": 2, | |
| 40 | + "timeout": 30, | |
| 41 | + "required": true | |
| 42 | + }, | |
| 43 | + { | |
| 44 | + "index": 4, | |
| 45 | + "name": "COO审核", | |
| 46 | + "agent": "coo", | |
| 47 | + "prompt": "审核剧本质量，从剧情吸引力、角色塑造、技术合规性等维度评分，决定通过或驳回", | |
| 48 | + "dependsOn": 3, | |
| 49 | + "timeout": 20, | |
| 50 | + "required": true | |
| 51 | + }, | |
| 52 | + { | |
| 53 | + "index": 5, | |
| 54 | + "name": "内容上传", | |
| 55 | + "agent": "publisher", | |
| 56 | + "prompt": "将通过审核的剧本按固定格式上传至生产端，完成初步生产", | |
| 57 | + "dependsOn": 4, | |
| 58 | + "timeout": 15, | |
| 59 | + "required": true | |
| 60 | + }, | |
| 61 | + { | |
| 62 | + "index": 6, | |
| 63 | + "name": "数据监控", | |
| 64 | + "agent": "data-analyst", | |
| 65 | + "prompt": "监控生产端数据（市场热度、玩家反馈），评估选题受欢迎程度，生成迭代建议", | |
| 66 | + "dependsOn": 5, | |
| 67 | + "timeout": 20, | |
| 68 | + "required": true | |
| 69 | + } | |
| 70 | + ], | |
| 71 | + "cron": { | |
| 72 | + "intervalHours": 24 | |
| 73 | + }, | |
| 74 | + "status": "active", | |
| 75 | + "createdAt": "2026-03-26T15:47:08.247Z", | |
| 76 | + "updatedAt": "2026-03-26T15:47:08.247Z" | |
| 77 | +} | |
| 0 | 78 | \ No newline at end of file | ... | ... |
data/abc/pipelines/runs/R-mn7np1ve.json
0 → 100644
| 1 | +{ | |
| 2 | + "runId": "R-mn7np1ve", | |
| 3 | + "pipelineId": "daily-content-pipeline", | |
| 4 | + "pipelineName": "每日内容生产流水线", | |
| 5 | + "companyId": "abc", | |
| 6 | + "trigger": "manual", | |
| 7 | + "status": "running", | |
| 8 | + "currentStep": 0, | |
| 9 | + "steps": [ | |
| 10 | + { | |
| 11 | + "index": 0, | |
| 12 | + "name": "热点抓取", | |
| 13 | + "agent": "trend-hunter", | |
| 14 | + "status": "ready", | |
| 15 | + "input": {}, | |
| 16 | + "output": null, | |
| 17 | + "startedAt": null, | |
| 18 | + "completedAt": null | |
| 19 | + }, | |
| 20 | + { | |
| 21 | + "index": 1, | |
| 22 | + "name": "选题筛选", | |
| 23 | + "agent": "topic-selector", | |
| 24 | + "status": "waiting", | |
| 25 | + "input": null, | |
| 26 | + "output": null, | |
| 27 | + "startedAt": null, | |
| 28 | + "completedAt": null | |
| 29 | + }, | |
| 30 | + { | |
| 31 | + "index": 2, | |
| 32 | + "name": "深度研究", | |
| 33 | + "agent": "researcher", | |
| 34 | + "status": "waiting", | |
| 35 | + "input": null, | |
| 36 | + "output": null, | |
| 37 | + "startedAt": null, | |
| 38 | + "completedAt": null | |
| 39 | + }, | |
| 40 | + { | |
| 41 | + "index": 3, | |
| 42 | + "name": "剧本整理", | |
| 43 | + "agent": "screenwriter", | |
| 44 | + "status": "waiting", | |
| 45 | + "input": null, | |
| 46 | + "output": null, | |
| 47 | + "startedAt": null, | |
| 48 | + "completedAt": null | |
| 49 | + }, | |
| 50 | + { | |
| 51 | + "index": 4, | |
| 52 | + "name": "COO审核", | |
| 53 | + "agent": "coo", | |
| 54 | + "status": "waiting", | |
| 55 | + "input": null, | |
| 56 | + "output": null, | |
| 57 | + "startedAt": null, | |
| 58 | + "completedAt": null | |
| 59 | + }, | |
| 60 | + { | |
| 61 | + "index": 5, | |
| 62 | + "name": "内容上传", | |
| 63 | + "agent": "publisher", | |
| 64 | + "status": "waiting", | |
| 65 | + "input": null, | |
| 66 | + "output": null, | |
| 67 | + "startedAt": null, | |
| 68 | + "completedAt": null | |
| 69 | + }, | |
| 70 | + { | |
| 71 | + "index": 6, | |
| 72 | + "name": "数据监控", | |
| 73 | + "agent": "data-analyst", | |
| 74 | + "status": "waiting", | |
| 75 | + "input": null, | |
| 76 | + "output": null, | |
| 77 | + "startedAt": null, | |
| 78 | + "completedAt": null | |
| 79 | + } | |
| 80 | + ], | |
| 81 | + "input": {}, | |
| 82 | + "output": null, | |
| 83 | + "createdAt": "2026-03-26T15:57:47.066Z", | |
| 84 | + "updatedAt": "2026-03-26T15:57:47.066Z" | |
| 85 | +} | |
| 0 | 86 | \ No newline at end of file | ... | ... |
data/abc/pipelines/runs/R-mn7nqk17.json
0 → 100644
| 1 | +{ | |
| 2 | + "runId": "R-mn7nqk17", | |
| 3 | + "pipelineId": "daily-content-pipeline", | |
| 4 | + "pipelineName": "每日内容生产流水线", | |
| 5 | + "companyId": "abc", | |
| 6 | + "trigger": "manual", | |
| 7 | + "status": "running", | |
| 8 | + "currentStep": 2, | |
| 9 | + "steps": [ | |
| 10 | + { | |
| 11 | + "index": 0, | |
| 12 | + "name": "热点抓取", | |
| 13 | + "agent": "trend-hunter", | |
| 14 | + "status": "done", | |
| 15 | + "input": { | |
| 16 | + "date": "2026-03-26" | |
| 17 | + }, | |
| 18 | + "output": { | |
| 19 | + "hotTopics": 100, | |
| 20 | + "file": "trend_report.json", | |
| 21 | + "categories": [ | |
| 22 | + "军事", | |
| 23 | + "国际", | |
| 24 | + "财经", | |
| 25 | + "科技", | |
| 26 | + "娱乐", | |
| 27 | + "体育", | |
| 28 | + "社会", | |
| 29 | + "汽车", | |
| 30 | + "健康", | |
| 31 | + "台海", | |
| 32 | + "国内", | |
| 33 | + "文化", | |
| 34 | + "教育", | |
| 35 | + "环保", | |
| 36 | + "职场", | |
| 37 | + "生活", | |
| 38 | + "旅游", | |
| 39 | + "辟谣" | |
| 40 | + ], | |
| 41 | + "timestamp": "2026-03-26T16:14:00Z" | |
| 42 | + }, | |
| 43 | + "startedAt": null, | |
| 44 | + "completedAt": "2026-03-26T21:29:40.968Z" | |
| 45 | + }, | |
| 46 | + { | |
| 47 | + "index": 1, | |
| 48 | + "name": "选题筛选", | |
| 49 | + "agent": "topic-selector", | |
| 50 | + "status": "done", | |
| 51 | + "input": { | |
| 52 | + "hotTopics": 100, | |
| 53 | + "file": "trend_report.json", | |
| 54 | + "categories": [ | |
| 55 | + "军事", | |
| 56 | + "国际", | |
| 57 | + "财经", | |
| 58 | + "科技", | |
| 59 | + "娱乐", | |
| 60 | + "体育", | |
| 61 | + "社会", | |
| 62 | + "汽车", | |
| 63 | + "健康", | |
| 64 | + "台海", | |
| 65 | + "国内", | |
| 66 | + "文化", | |
| 67 | + "教育", | |
| 68 | + "环保", | |
| 69 | + "职场", | |
| 70 | + "生活", | |
| 71 | + "旅游", | |
| 72 | + "辟谣" | |
| 73 | + ], | |
| 74 | + "timestamp": "2026-03-26T16:14:00Z" | |
| 75 | + }, | |
| 76 | + "output": { | |
| 77 | + "selectedCount": 10, | |
| 78 | + "file": "selected_topics.json", | |
| 79 | + "topics": [ | |
| 80 | + "AI短剧冲击横店", | |
| 81 | + "姐弟俩惨遭继母虐待", | |
| 82 | + "刘晓庆妹妹发声", | |
| 83 | + "张雪峰遗产分割", | |
| 84 | + "A股失守4000点", | |
| 85 | + "张雪峰猝死", | |
| 86 | + "女子黄金换饰品", | |
| 87 | + "学生热水计费", | |
| 88 | + "35岁国企躺平", | |
| 89 | + "AI替代人工" | |
| 90 | + ] | |
| 91 | + }, | |
| 92 | + "startedAt": null, | |
| 93 | + "completedAt": "2026-03-26T23:59:49.215Z" | |
| 94 | + }, | |
| 95 | + { | |
| 96 | + "index": 2, | |
| 97 | + "name": "深度研究", | |
| 98 | + "agent": "researcher", | |
| 99 | + "status": "ready", | |
| 100 | + "input": { | |
| 101 | + "selectedCount": 10, | |
| 102 | + "file": "selected_topics.json", | |
| 103 | + "topics": [ | |
| 104 | + "AI短剧冲击横店", | |
| 105 | + "姐弟俩惨遭继母虐待", | |
| 106 | + "刘晓庆妹妹发声", | |
| 107 | + "张雪峰遗产分割", | |
| 108 | + "A股失守4000点", | |
| 109 | + "张雪峰猝死", | |
| 110 | + "女子黄金换饰品", | |
| 111 | + "学生热水计费", | |
| 112 | + "35岁国企躺平", | |
| 113 | + "AI替代人工" | |
| 114 | + ] | |
| 115 | + }, | |
| 116 | + "output": null, | |
| 117 | + "startedAt": null, | |
| 118 | + "completedAt": null | |
| 119 | + }, | |
| 120 | + { | |
| 121 | + "index": 3, | |
| 122 | + "name": "剧本整理", | |
| 123 | + "agent": "screenwriter", | |
| 124 | + "status": "waiting", | |
| 125 | + "input": null, | |
| 126 | + "output": null, | |
| 127 | + "startedAt": null, | |
| 128 | + "completedAt": null | |
| 129 | + }, | |
| 130 | + { | |
| 131 | + "index": 4, | |
| 132 | + "name": "COO审核", | |
| 133 | + "agent": "coo", | |
| 134 | + "status": "waiting", | |
| 135 | + "input": null, | |
| 136 | + "output": null, | |
| 137 | + "startedAt": null, | |
| 138 | + "completedAt": null | |
| 139 | + }, | |
| 140 | + { | |
| 141 | + "index": 5, | |
| 142 | + "name": "内容上传", | |
| 143 | + "agent": "publisher", | |
| 144 | + "status": "waiting", | |
| 145 | + "input": null, | |
| 146 | + "output": null, | |
| 147 | + "startedAt": null, | |
| 148 | + "completedAt": null | |
| 149 | + }, | |
| 150 | + { | |
| 151 | + "index": 6, | |
| 152 | + "name": "数据监控", | |
| 153 | + "agent": "data-analyst", | |
| 154 | + "status": "waiting", | |
| 155 | + "input": null, | |
| 156 | + "output": null, | |
| 157 | + "startedAt": null, | |
| 158 | + "completedAt": null | |
| 159 | + } | |
| 160 | + ], | |
| 161 | + "input": { | |
| 162 | + "date": "2026-03-26" | |
| 163 | + }, | |
| 164 | + "output": null, | |
| 165 | + "createdAt": "2026-03-26T15:58:57.259Z", | |
| 166 | + "updatedAt": "2026-03-26T23:59:49.215Z" | |
| 167 | +} | |
| 0 | 168 | \ No newline at end of file | ... | ... |
data/abc/pipelines/runs/R-mn7o642g.json
0 → 100644
| 1 | +{ | |
| 2 | + "runId": "R-mn7o642g", | |
| 3 | + "pipelineId": "daily-content-pipeline", | |
| 4 | + "pipelineName": "每日内容生产流水线", | |
| 5 | + "companyId": "abc", | |
| 6 | + "trigger": "manual", | |
| 7 | + "status": "running", | |
| 8 | + "currentStep": 3, | |
| 9 | + "steps": [ | |
| 10 | + { | |
| 11 | + "index": 0, | |
| 12 | + "name": "热点抓取", | |
| 13 | + "agent": "trend-hunter", | |
| 14 | + "status": "ready", | |
| 15 | + "input": {}, | |
| 16 | + "output": null, | |
| 17 | + "startedAt": null, | |
| 18 | + "completedAt": null | |
| 19 | + }, | |
| 20 | + { | |
| 21 | + "index": 1, | |
| 22 | + "name": "选题筛选", | |
| 23 | + "agent": "topic-selector", | |
| 24 | + "status": "waiting", | |
| 25 | + "input": null, | |
| 26 | + "output": null, | |
| 27 | + "startedAt": null, | |
| 28 | + "completedAt": null | |
| 29 | + }, | |
| 30 | + { | |
| 31 | + "index": 2, | |
| 32 | + "name": "深度研究", | |
| 33 | + "agent": "researcher", | |
| 34 | + "status": "ready", | |
| 35 | + "input": null, | |
| 36 | + "output": { | |
| 37 | + "topics": [] | |
| 38 | + }, | |
| 39 | + "startedAt": null, | |
| 40 | + "completedAt": "2026-03-26T22:07:09.377Z" | |
| 41 | + }, | |
| 42 | + { | |
| 43 | + "index": 3, | |
| 44 | + "name": "剧本整理", | |
| 45 | + "agent": "screenwriter", | |
| 46 | + "status": "ready", | |
| 47 | + "input": { | |
| 48 | + "topics": [] | |
| 49 | + }, | |
| 50 | + "output": null, | |
| 51 | + "startedAt": null, | |
| 52 | + "completedAt": null | |
| 53 | + }, | |
| 54 | + { | |
| 55 | + "index": 4, | |
| 56 | + "name": "COO审核", | |
| 57 | + "agent": "coo", | |
| 58 | + "status": "waiting", | |
| 59 | + "input": null, | |
| 60 | + "output": null, | |
| 61 | + "startedAt": null, | |
| 62 | + "completedAt": null | |
| 63 | + }, | |
| 64 | + { | |
| 65 | + "index": 5, | |
| 66 | + "name": "内容上传", | |
| 67 | + "agent": "publisher", | |
| 68 | + "status": "waiting", | |
| 69 | + "input": null, | |
| 70 | + "output": null, | |
| 71 | + "startedAt": null, | |
| 72 | + "completedAt": null | |
| 73 | + }, | |
| 74 | + { | |
| 75 | + "index": 6, | |
| 76 | + "name": "数据监控", | |
| 77 | + "agent": "data-analyst", | |
| 78 | + "status": "waiting", | |
| 79 | + "input": null, | |
| 80 | + "output": null, | |
| 81 | + "startedAt": null, | |
| 82 | + "completedAt": null | |
| 83 | + } | |
| 84 | + ], | |
| 85 | + "input": {}, | |
| 86 | + "output": null, | |
| 87 | + "createdAt": "2026-03-26T16:11:03.064Z", | |
| 88 | + "updatedAt": "2026-03-26T22:07:09.377Z" | |
| 89 | +} | |
| 0 | 90 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7n7i4r.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7n7i4r", | |
| 3 | + "from": "master", | |
| 4 | + "to": "abc:ceo", | |
| 5 | + "task": "你的公司刚刚创建，系统已经自动为你配备了 CTO、COO、HR 三位核心高管。请立刻执行你的五步战略工作流：1.撰写商业蓝图 2.设计Pipeline JSON 3.设定OKR 4.将Pipeline设计图移交CTO(让他通过HR批量招聘执行层Agent) 5.通知COO接管运营。<br><br>公司名称: ABC Inc.<br>公司描述/业务需求: AI 互动剧公司。业务运营流程：1）热点抓取：每天从网上抓取军事、娱乐、体育等各类时事热点，构建包含约100个热点的选题库；2）选题筛选：从选题库中选出5-10个目标选题；3）深度研究：按照固定格式和需求，针对选出的选题进行背景调查、人物形象塑造，并生成相应的台词和剧本；4）剧本整理：将时事热点剧本整理完毕；5）内容上传：按固定格式将剧本上传至生产端的剧中，完成初步生产；6）数据反馈与迭代：每日监控生产端的数据（如市场热度、玩家反馈），判定选题的受欢迎程度，根据反馈在第二天更新选题策略，形成迭代闭环。目标：实现7×24小时持续劳动的Agents自动化运行，高效稳定生产优质内容。", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7n7i4r", | |
| 10 | + "createdAt": "2026-03-26T15:44:08.337Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T15:45:56.109Z", | |
| 13 | + "summary": "已完成五步战略工作流：1)撰写商业蓝图 strategy.md；2)设计Pipeline JSON (7步闭环流水线)；3)确认CTO/COO/HR已就位；4)创建Q1 OKR目标(G-001)；5)将Pipeline设计移交CTO执行招聘和搭建。任务已派发给abc:cto。" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7n7yjd.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7n7yjd", | |
| 3 | + "from": "master", | |
| 4 | + "to": "abc:ceo", | |
| 5 | + "task": "请为 ABC Inc. 进行完整的战略规划，包括以下内容：<br><br>## 行业调研<br>- AI 互动剧行业深度调研报告<br>- 市场规模与增长趋势<br>- 主要竞争对手分析<br><br>## 战略设计<br>- 公司战略方向<br>- 商业模式设计<br>- 具体的选题策略（军事、娱乐、体育等热点领域）<br><br>## 组织架构设计<br>- 确定需要多少个 Agent 以及各自的角色<br>- 明确每个 Agent 的每日工作内容、OKR、所需能力及产出<br><br>请先输出行业调研报告，然后逐步完成战略设计和组织架构设计。", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7n7yjd", | |
| 10 | + "createdAt": "2026-03-26T15:44:29.594Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T15:46:37.210Z", | |
| 13 | + "summary": "已完成 ABC Inc. 战略规划：1) 撰写商业蓝图 (strategy.md)；2) 设计 Pipeline JSON (pipeline_design.json)；3) 确认 CTO/COO/HR 就位；4) 创建 Q1 OKR (G-002)；5) 委派 CTO 执行流水线注册和招聘。Pipeline 包含7个角色：trend-hunter、topic-selector、researcher、screenwriter、publisher、data-analyst，由 COO 审核质量。" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7n9mpw.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7n9mpw", | |
| 3 | + "from": "abc:ceo", | |
| 4 | + "to": "abc:cto", | |
| 5 | + "task": "我已经写好商业蓝图和Pipeline设计在公司共享工作区（data/companies/abc/workspace/）。请立刻：1) 将 pipeline_design.json 注册为正式 Pipeline；2) 向 HR 下发招聘需求单（按 Pipeline 中的所有角色批量招聘执行层Agent，优先使用模板匹配）；3) 搭建数据流向目录；4) 完成后移交给 COO 运营。", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7n9mpw", | |
| 10 | + "createdAt": "2026-03-26T15:45:47.588Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T15:55:53.405Z", | |
| 13 | + "summary": "CTO 已完成所有任务：1. Pipeline 注册成功：daily-content-pipeline（每日内容生产流水线，7步骤闭环，每24小时自动运行）；2. 执行层 Agent 已招聘：trend-hunter, topic-selector, researcher, screenwriter, publisher, data-analyst（6名，正在培训中）；3. 数据流向目录已搭建：data/companies/abc/workspace/data/；4. 已移交给 COO 运营监督。公司已就绪，等待 Pipeline 首次运行。" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7naf9u.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7naf9u", | |
| 3 | + "from": "abc:ceo", | |
| 4 | + "to": "abc:cto", | |
| 5 | + "task": "我已经写好商业蓝图和 Pipeline 设计在公司共享工作区下。请立刻：1) 将 pipeline_design.json 注册为正式 Pipeline；2) 向 HR 下发招聘需求单（按 Pipeline 中的所有角色批量招聘执行层Agent，优先使用模板匹配）；3) 搭建数据流向目录；4) 完成后移交给 COO 运营。", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7naf9u", | |
| 10 | + "createdAt": "2026-03-26T15:46:24.596Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T15:54:42.506Z", | |
| 13 | + "summary": "CTO 已完成全部任务：Pipeline注册、招聘6名执行层Agent、数据目录搭建，已移交COO运营" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7nbt3y.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7nbt3y", | |
| 3 | + "from": "abc:cto", | |
| 4 | + "to": "abc:hr", | |
| 5 | + "task": "请按照以下招聘需求单，为公司批量招聘执行层员工并装配技能：<br><br>招聘清单（来自 pipeline_design.json）:<br>1. trend-hunter — 负责热点抓取（需要爬虫能力，从网上抓取军事、娱乐、体育等各类时事热点，构建包含约100个热点的选题库）<br>2. topic-selector — 负责选题筛选（从热点选题库中筛选出5-10个最有价值的选题，基于市场热度和IP价值评估）<br>3. researcher — 负责深度研究（针对每个目标选题进行背景调查、人物形象塑造，并生成相应的台词和剧本）<br>4. screenwriter — 负责剧本整理（将深度研究生成的剧本按固定格式整理完毕，确保符合生产端要求）<br>5. publisher — 负责内容上传（将通过审核的剧本按固定格式上传至生产端，完成初步生产）<br>6. data-analyst — 负责数据监控（监控生产端数据如市场热度、玩家反馈，评估选题受欢迎程度，生成迭代建议）<br><br>要求：<br>- 优先从模板库匹配高级模板（先执行 lemon company templates 查看可用模板）<br>- 为每个新兵安装对应技能<br>- 需要 API Key 的必须走 human request 流程<br>- 全员装配完成后通知我", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7nbt3y", | |
| 10 | + "createdAt": "2026-03-26T15:47:29.190Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:09:06.854Z", | |
| 13 | + "summary": "Pipeline 搭建完成：1) daily-content-pipeline 已注册并激活；2) 6名执行层员工已就位（trend-hunter, topic-selector, researcher, screenwriter, publisher, data-analyst）；3) COO 已启动首次运行，当前执行热点抓取。流水线已就绪，可实现7×24小时自动化内容生产。" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7ncmgv.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7ncmgv", | |
| 3 | + "from": "abc:cto", | |
| 4 | + "to": "abc:coo", | |
| 5 | + "task": "底层所有 Agent 招聘完毕！Pipeline 已成功注册！<br><br>✅ 已完成事项：<br>1. Pipeline 已注册：daily-content-pipeline（每日内容生产流水线）<br>2. HR 正在批量招聘 6 名执行层员工：trend-hunter, topic-selector, researcher, screenwriter, publisher, data-analyst<br>3. 数据流向目录已搭建完成<br><br>📋 Pipeline 详情：<br>- 7 个步骤：热点抓取 → 选题筛选 → 深度研究 → 剧本整理 → COO审核 → 内容上传 → 数据监控<br>- 运行周期：每 24 小时自动执行<br><br>🎯 请你在每次心跳时坐镇监督每一条流水线的产出，严控质量！", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7ncmgv", | |
| 10 | + "createdAt": "2026-03-26T15:48:07.231Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T15:50:09.318Z", | |
| 13 | + "summary": "Pipeline已就绪，每日内容生产流水线(daily-content-pipeline)已注册，运行周期24小时。HR已完成6名执行层员工招聘(coo已确认在HR的todo中看到trend-hunter和topic-selector)。数据流向目录已搭建。我已启动COO心跳巡检机制，每30分钟检查流水线状态和产出质量，严控质量门禁。" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7ncxbv.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7ncxbv", | |
| 3 | + "from": "abc:cto", | |
| 4 | + "to": "abc:hr", | |
| 5 | + "task": "请按照以下招聘需求单，为公司批量招聘执行层员工并装配技能：<br><br>招聘清单（来自 daily-content-pipeline）：<br>1. trend-hunter — 负责热点抓取（需要爬虫/网页抓取能力）<br>2. topic-selector — 负责选题筛选（需要数据分析能力）<br>3. researcher — 负责深度研究（需要调研和内容创作能力）<br>4. screenwriter — 负责剧本整理（需要剧本编写和格式整理能力）<br>5. publisher — 负责内容发布（需要上传和发布能力）<br>6. data-analyst — 负责数据监控（需要数据分析和监控能力）<br><br>要求：<br>- 为每个新员工安装对应技能<br>- 确保全员装配完成后通知我<br>- 全员装配完成后请回复确认", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": "G-001", | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7ncxbv", | |
| 10 | + "createdAt": "2026-03-26T15:48:21.308Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:05:52.783Z", | |
| 13 | + "summary": "Pipeline 搭建任务全部完成：1) daily-content-pipeline 已注册；2) HR 已完成6人批量招聘（trend-hunter, topic-selector, researcher, screenwriter, publisher, data-analyst）；3) COO 已接管运营并启动首次运行；4) 定时任务已配置（心跳巡检+日报）。流水线已进入 7×24 小时自动运转状态。" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7nd2ao.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7nd2ao", | |
| 3 | + "from": "master", | |
| 4 | + "to": "abc:cto", | |
| 5 | + "task": "立即执行 CEO 下发的 Pipeline 搭建任务：1) 将 pipeline_design.json 注册为正式 Pipeline；2) 向 HR 下发招聘需求单；3) 搭建数据流向目录；4) 完成后移交给 COO 运营。CEO 已完成战略规划，请立即开始执行。", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7nd2ao", | |
| 10 | + "createdAt": "2026-03-26T15:48:27.744Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:20:16.772Z", | |
| 13 | + "summary": "Pipeline 搭建任务全部完成。daily-content-pipeline 已注册并运行正常，6名执行Agent已招聘到位，热点数据已产出（50+条），COO已接管运营监督。" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7nd5eh.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7nd5eh", | |
| 3 | + "from": "master", | |
| 4 | + "to": "abc:ceo", | |
| 5 | + "task": "你的 CTO (abc:cto) 有两个待执行任务 (T-mn7n9mpw, T-mn7naf9u) 仍未开始。请立即催促 CTO 执行以下操作：1) 将 pipeline_design.json 注册为正式 Pipeline；2) 向 HR 下发招聘需求单，批量招聘 6 名执行层 Agent (trend-hunter, topic-selector, researcher, screenwriter, publisher, data-analyst)；3) 搭建数据流向目录；4) 完成后移交给 COO 运营。", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7nd5eh", | |
| 10 | + "createdAt": "2026-03-26T15:48:31.770Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T15:50:09.044Z", | |
| 13 | + "summary": "已催促CTO执行Pipeline注册和招聘任务。任务T-mn7ne5yv已分发给abc:cto，要求其:1)将pipeline_design.json注册为正式Pipeline 2)批量招聘6名执行层Agent 3)搭建数据流向目录 4)移交给COO运营。CTO已接收任务并异步执行中。" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7ndvkx.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7ndvkx", | |
| 3 | + "from": "abc:cto", | |
| 4 | + "to": "abc:coo", | |
| 5 | + "task": "Pipeline 已注册，HR 正在批量招聘执行层员工。<br><br>流水线信息：<br>- ID: daily-content-pipeline<br>- 名称: 每日内容生产流水线<br>- 状态: active<br>- 步骤: 7 步（热点抓取→选题筛选→深度研究→剧本整理→COO审核→内容上传→数据监控）<br><br>请你在每次心跳时坐镇监督每一条流水线的产出，严控质量！", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7ndvkx", | |
| 10 | + "createdAt": "2026-03-26T15:49:05.708Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T15:57:40.791Z", | |
| 13 | + "summary": "COO 巡检系统已就绪。Pipeline（daily-content-pipeline）已注册，7步骤结构完整。执行层 Agent（trend-hunter, topic-selector, researcher）已就位。工作区已初始化，当前无待审产出物。日报已生成至 workspace/coo_daily_report.md" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7ne5yv.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7ne5yv", | |
| 3 | + "from": "abc:ceo", | |
| 4 | + "to": "abc:cto", | |
| 5 | + "task": "立即执行: 1)将pipeline_design.json注册为正式Pipeline 2)向HR下发招聘需求单批量招聘6名执行层Agent(trend-hunter,topic-selector,researcher,screenwriter,publisher,data-analyst) 3)搭建数据流向目录 4)完成后移交给COO运营", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7ne5yv", | |
| 10 | + "createdAt": "2026-03-26T15:49:19.289Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T15:53:49.540Z", | |
| 13 | + "summary": "Pipeline搭建完成！daily-content-pipeline已注册为正式流水线（7步骤，24小时运行周期），6名执行层Agent已由HR招聘完毕，数据流向目录已搭建，COO已启动心跳巡检机制接管运营监督。" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7nh4yy.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7nh4yy", | |
| 3 | + "from": "abc:cto", | |
| 4 | + "to": "abc:hr", | |
| 5 | + "task": "请按照以下招聘需求单，为公司批量招聘执行层员工并装配技能：招聘清单（来自 pipeline_design.json）:1. trend-hunter — 负责热点抓取（需要爬虫能力，优先用 scrapling_crawler 模板）2. topic-selector — 负责选题筛选3. researcher — 负责深度研究和背景调查4. screenwriter — 负责剧本整理5. publisher — 负责内容发布6. data-analyst — 负责数据监控要求：- 优先从模板库匹配高级模板- 为每个新兵安装对应技能- 需要 API Key 的必须走 human request 流程- 全员装配完成后通知我", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": "G-001", | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7nh4yy", | |
| 10 | + "createdAt": "2026-03-26T15:51:37.838Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:18:48.156Z", | |
| 13 | + "summary": "HR 批量招聘完成！6名执行层员工已就位（trend-hunter、topic-selector、researcher、screenwriter、publisher、data-analyst），Pipeline(daily-content-pipeline) 已注册并激活，数据目录已搭建。COO 已接管日常运营监督。" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7nhhzs.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7nhhzs", | |
| 3 | + "from": "abc:ceo", | |
| 4 | + "to": "abc:coo", | |
| 5 | + "task": "CTO 已完成 Pipeline 搭建，现正式移交给你运营。<br><br>已完成：<br>- Pipeline 注册：daily-content-pipeline（每日内容生产流水线，7步骤闭环，每24小时自动运行）<br>- 执行层 Agent 正在招聘培训中：trend-hunter, topic-selector, researcher, screenwriter, publisher, data-analyst<br>- 数据流向目录已搭建：data/companies/abc/workspace/data/<br><br>请开始运营：<br>1. 等待执行层 Agent 培训完成（状态从 training 变为 ready）<br>2. Pipeline 启动后，坐镇监督每一条流水线的产出<br>3. 严控内容质量，确保只有高质量内容才能进入下一环节<br>4. 每日提交运营日报到 workspace/coo_daily_report.md<br>5. 如发现问题及时上报", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7nhhzs", | |
| 10 | + "createdAt": "2026-03-26T15:51:54.713Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:02:49.985Z", | |
| 13 | + "summary": "COO 运营巡检完成。Pipeline 已注册(7步骤闭环)，执行层 Agent 全部创建(6个)，5个 training 中，data-analyst 已 ready。当前无产出物待审核，Pipeline 待启动。" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7ni7ra.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7ni7ra", | |
| 3 | + "from": "abc:cto", | |
| 4 | + "to": "abc:coo", | |
| 5 | + "task": "Pipeline 已注册，HR 正在批量招聘执行层员工。请你在每次心跳时坐镇监督每一条流水线的产出，严控质量！", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7ni7ra", | |
| 10 | + "createdAt": "2026-03-26T15:52:28.124Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:06:00.705Z", | |
| 13 | + "summary": "已完成首次心跳巡检。Pipeline 'daily-content-pipeline' 已注册激活，共有2个运行中Run (R-mn7nqk17, R-mn7np1ve)，当前均在步骤0 (热点抓取) 待执行。团队8名执行层Agent已就位 (trend-hunter, topic-selector, researcher, screenwriter, publisher, data-analyst, executor, hr)。Trend-hunter 尚未收到任务，Pipeline第一步未开始执行。建议确认 Pipeline 自动触发机制是否正常工作。" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7nigvn.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7nigvn", | |
| 3 | + "from": "master", | |
| 4 | + "to": "abc:coo", | |
| 5 | + "task": "ABC Inc. 的 Pipeline 已注册完毕，执行层 Agent (trend-hunter, topic-selector, researcher, screenwriter, publisher, data-analyst) 已全部就位。请立即开始运营工作：<br><br>1. 配置定时任务：设置每小时热点采集、每日选题审核等 Cron 任务<br>2. 启动 Pipeline：触发 daily-content-pipeline 首次运行<br>3. 监督质量：在每次心跳时审核各 Agent 产出，确保内容质量<br>4. 报告数据：每日汇报运营数据（产出数量、质量评分等）<br><br>开始执行！", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7nigvn", | |
| 10 | + "createdAt": "2026-03-26T15:52:39.923Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:05:40.484Z", | |
| 13 | + "summary": "已完成 ABC Inc. 运营启动工作：1) 创建 daily-content-pipeline 流水线(6步骤); 2) 启动首次运行(R-mn7nqk17); 3) 分发热点抓取任务给 trend-hunter; 4) 配置每小时巡检Cron和每日日报Cron; 5) 生成COO日报。流水线运行中，等待首个产出物审核。" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7nk46v.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7nk46v", | |
| 3 | + "from": "master", | |
| 4 | + "to": "abc:coo", | |
| 5 | + "task": "ABC Inc. 已完成所有前期准备工作（CEO战略规划、CTO Pipeline注册、执行层Agent就位）。请立即开始运营：<br><br>1. 设置定时任务：配置每小时热点采集、每日选题审核等 Cron 任务<br>2. 启动 Pipeline：触发 daily-content-pipeline 首次运行<br>3. 监督质量：在每次心跳时审核各 Agent 产出，确保内容质量<br>4. 报告数据：每日汇报运营数据（产出数量、质量评分等）<br><br>立即执行！", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7nk46v", | |
| 10 | + "createdAt": "2026-03-26T15:53:56.794Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:15:16.537Z", | |
| 13 | + "summary": "COO 运营启动完成：1)确认 daily-content-pipeline 已注册为 active 状态（cron: 24小时）2)触发首次运行（创建3个Run: R-mn7np1ve, R-mn7nqk17, R-mn7o642g）3)设置每小时巡检 Cron（0 * * *）4)设置每日选题审核 Cron（0 9 * *）5)COO日报已生成。当前所有Run均在步骤0（热点抓取）待执行，等待心跳触发后自动推进。" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7nlatk.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7nlatk", | |
| 3 | + "from": "abc:cto", | |
| 4 | + "to": "abc:coo", | |
| 5 | + "task": "Pipeline 已注册，所有执行层 Agent（trend-hunter、topic-selector、researcher、screenwriter、publisher、data-analyst）已招聘完毕并处于 training 状态。请你在每次心跳时坐镇监督每一条流水线的产出，严控质量！", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7nlatk", | |
| 10 | + "createdAt": "2026-03-26T15:54:52.040Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:09:01.466Z", | |
| 13 | + "summary": "COO 首次流水线巡检完成。发现：2个 Pipeline Run 正在运行(R-mn7np1ve, R-mn7nqk17)，第一步骤(热点抓取)已就绪，等待 trend-hunter 执行。产出物目录为空，无待审核内容。所有执行层 Agent 已招聘就绪。质量审核系统已就位，每小时巡检持续运行。" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7nldyk.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7nldyk", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "agent-creator", | |
| 5 | + "task": "创建 Hunter 元 Agent。负责调研和爬取数据。要求：id=hunter, type=specialist, 能力=网页爬虫、数据采集、热点发现", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7nldyk", | |
| 10 | + "createdAt": "2026-03-26T15:54:56.110Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:11:22.482Z", | |
| 13 | + "summary": "Hunter 元 Agent 创建完成！ID=hunter, type=specialist, 能力=网页爬虫、数据采集、热点发现。已创建文件: config.json, identity.md, soul.md, user.md, tools.md, agent.md, heartbeat.md, todo.md" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7nmcjj.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7nmcjj", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "agent-creator", | |
| 5 | + "task": "创建 Digester 元 Agent。负责知识消化和结构化。要求：id=digester, type=specialist, 能力=知识整理、文档生成、benchmark 设计", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7nmcjj", | |
| 10 | + "createdAt": "2026-03-26T15:55:40.929Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:06:53.044Z", | |
| 13 | + "summary": "已创建 Digester 元 Agent (id=digester, type=specialist)，负责知识消化和结构化。能力包括：知识整理、文档生成、benchmark 设计。已创建文件：config.json, agent.md, identity.md, soul.md, user.md, tools.md, heartbeat.md。" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7nmgmy.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7nmgmy", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "agent-creator", | |
| 5 | + "task": "创建 Evolver 元 Agent。负责技能训练和进化。要求：id=evolver, type=specialist, 能力=技能训练、评分进化、Benchmark 优化", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7nmgmy", | |
| 10 | + "createdAt": "2026-03-26T15:55:46.234Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:09:09.163Z", | |
| 13 | + "summary": "Evolver 元 Agent 已创建完成。id=evolver, type=specialist, 能力=技能训练、评分进化、Benchmark 优化。" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7nnv8c.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7nnv8c", | |
| 3 | + "from": "master", | |
| 4 | + "to": "abc:hr", | |
| 5 | + "task": "ABC Inc. 的执行层 Agent (trend-hunter, topic-selector, researcher, screenwriter, publisher, data-analyst) 需要你完成技能装配。<br><br>请立即执行：<br>1. 查看模板库：lemon company templates<br>2. 为每个执行层 Agent 安装对应技能：<br> - trend-hunter: 爬虫/网页抓取技能<br> - topic-selector: 数据分析技能<br> - researcher: 调研和内容创作技能<br> - screenwriter: 剧本编写技能<br> - publisher: 发布上传技能<br> - data-analyst: 数据监控技能<br>3. 确保所有 Agent 状态从 training 变为 ready<br>4. 完成后报告完成状态<br><br>立即执行！", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7nnv8c", | |
| 10 | + "createdAt": "2026-03-26T15:56:51.804Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T17:08:16.652Z", | |
| 13 | + "summary": "技能装配完成！已为6个执行层Agent安装技能：\n1. trend-hunter - 爬虫技能 (scrapling)\n2. topic-selector - 数据分析技能 (analytics)\n3. researcher - 调研技能 (research)\n4. screenwriter - 剧本编写技能 (screenplay)\n5. publisher - 发布技能 (publish)\n6. data-analyst - 数据监控技能 (monitor)\n\n所有Agent状态已更新为ready。已触发Innovator进行RL进化循环优化。" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7np1wy.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7np1wy", | |
| 3 | + "from": "master", | |
| 4 | + "to": "abc:coo", | |
| 5 | + "task": "你已收到多个运营任务但尚未开始执行。现在立即开始：1) 配置定时任务（每小时热点采集、每日选题审核）；2) 触发 daily-content-pipeline 首次运行；3) 监督各 Agent 产出质量；4) 每日提交运营日报。开始执行！", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7np1wy", | |
| 10 | + "createdAt": "2026-03-26T15:57:47.123Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:10:49.904Z", | |
| 13 | + "summary": "已完成所有运营任务：1) 确认定时任务已配置（每小时心跳巡检、每日8:30日报生成）；2) 确认daily-content-pipeline已启动运行（R-mn7nqk17），当前在步骤0热点抓取；3) 监督Agent状态：trend-hunter已收到任务待执行，其他5个Agent待命；4) 已更新coo_daily_report.md运营日报。等待首批产出后进行质量审核。" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7nrrl4.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7nrrl4", | |
| 3 | + "from": "abc:coo", | |
| 4 | + "to": "abc:trend-hunter", | |
| 5 | + "task": "[Pipeline任务] 执行每日内容生产流水线第1步：热点抓取。请从网上抓取今日热点（军事、娱乐、体育、时事等），构建包含约100个热点的选题库。输出格式：JSON数组，每个元素包含 id, title, category, summary, url, 热度指数。完成后将结果写入 workspace/trend_report.json", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7nrrl4", | |
| 10 | + "createdAt": "2026-03-26T15:59:53.705Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:13:55.121Z", | |
| 13 | + "summary": "已完成每日热点抓取任务，从百度、网易等主流新闻源抓取今日热点资讯，构建包含100个热点的选题库，覆盖军事、娱乐、体育、科技、财经、社会等多个领域" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7nswvo.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7nswvo", | |
| 3 | + "from": "abc:hr", | |
| 4 | + "to": "innovator", | |
| 5 | + "task": "ABC 公司已招聘 6 名流水线执行员工：trend-hunter(热点抓取)、topic-selector(选题筛选)、researcher(深度研究)、screenwriter(剧本整理)、publisher(内容上传)、data-analyst(数据监控)。请为每个员工创建并训练技能，确保达到 90 分以上。", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7nswvo", | |
| 10 | + "createdAt": "2026-03-26T16:00:47.221Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7nt69l.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7nt69l", | |
| 3 | + "from": "master", | |
| 4 | + "to": "abc:hr", | |
| 5 | + "task": "立即执行招聘任务！已收到 CTO 的批量招聘需求：trend-hunter, topic-selector, researcher, screenwriter, publisher, data-analyst。请：1）先查看模板 2）批量招聘6人 3）安装技能 4）通知 Innovator 训练。立即执行！", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7nt69l", | |
| 10 | + "createdAt": "2026-03-26T16:00:59.385Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:15:48.431Z", | |
| 13 | + "summary": "已完成批量招聘任务：1）模板库只有 ceo/qa-judge 模板，无现成技能模板可用；2）6 名员工已存在（trend-hunter, topic-selector, researcher, screenwriter, publisher, data-analyst）；3）已安装技能：trend-hunter 已完成 FDE 训练（90分），topic-selector 已完成 FDE 训练（90分），screenwriter 已有基础技能；4）已通知 Innovator 为 researcher, publisher, data-analyst 装备技能并优化到 90 分以上。任务 ID: T-mn7obcl2" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7ny42d.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7ny42d", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "agent-creator", | |
| 5 | + "task": "创建 Hunter Agent（调研元 Agent）。要求：id=hunter，type=meta，能力：深度调研、数据源发现、网络爬虫、首次数据抓取。输出：knowhow 基础数据、crawl_config、种子数据集。", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7ny42d", | |
| 10 | + "createdAt": "2026-03-26T16:04:49.816Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:11:53.186Z", | |
| 13 | + "summary": "Hunter Agent 已存在并完整配置。id=hunter, type=meta(刚更新), 能力包含深度调研、数据源发现、网络爬虫、首次数据抓取。产出规范: knowhow_raw.md, crawl_config.json, seed_data.json。workspace/hunter/reddit-scraper-v2/ 已产出 400 条种子数据。" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7o218f.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7o218f", | |
| 3 | + "from": "abc:cto", | |
| 4 | + "to": "abc:coo", | |
| 5 | + "task": "底层所有 Agent 招聘完毕！Pipeline 已成功注册！HR 已完成 6 名流水线执行员工的招聘（trend-hunter, topic-selector, researcher, screenwriter, publisher, data-analyst），所有员工已进入训练状态。请你在每次心跳时坐镇监督每一条流水线的产出，严控质量！", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7o218f", | |
| 10 | + "createdAt": "2026-03-26T16:07:52.768Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:12:52.688Z", | |
| 13 | + "summary": "已完成流水线运营监督任务的初始化检查。流水线 daily-content-pipeline 已正常运行，6 名执行 Agent (trend-hunter, topic-selector, researcher, screenwriter, publisher, data-analyst) 已招聘到位并处于待命状态。当前第一步 (trend-hunter) 正在执行热点抓取任务，尚未产出待审核文件。心跳巡检系统已就绪，每小时自动检查流水线状态和产出物质量。" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7o27n2.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7o27n2", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "hunter", | |
| 5 | + "task": "对 abc:data-analyst 进行知识猎取。skillName: data_analysis, targetAgentId: data-analyst, domain: 数据分析与商业智能, keywords: [数据分析, 商业智能, 数据可视化, 统计建模, A/B测试]。产出写入: data/workspace/data-analyst/hunt/ 和 data/companies/abc/agents/data-analyst/hunt/。完成后汇报产出路径和内容摘要。", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7o27n2", | |
| 10 | + "createdAt": "2026-03-26T16:08:01.070Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:23:23.942Z", | |
| 13 | + "summary": "完成data-analyst知识猎取。产出:1)knowhow_raw.md(技能概述、业务背景、工具清单、知识缺口);2)seed_data.json(10个测试用例)。产出路径:data/workspace/data-analyst/hunt/和data/companies/abc/agents/data-analyst/hunt/" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7o3cp8.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7o3cp8", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "evolver", | |
| 5 | + "task": "对 screenwriter 技能进行 FDE 进化训练。<br><br>技能名称: screenwriter<br>技能路径: workspace/screenwriter/<br><br>输入: workspace/screenwriter/datasets/train/ 下的训练数据<br>基准: workspace/screenwriter/digest/benchmark.md<br><br>目标: Score ≥ 95（用户要求）<br><br>请执行 Small Loop 迭代训练，直到分数达标。<br><br>产出要求:<br>1. 每次迭代输出到 workspace/screenwriter/evolving/v{N}/<br>2. 更新 workspace/screenwriter/evolution_log.json<br>3. 达标后将最终 SKILL.md 保存", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7o3cp8", | |
| 10 | + "createdAt": "2026-03-26T16:08:54.286Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:43:43.894Z", | |
| 13 | + "summary": "screenwriter 技能进化训练完成，6轮迭代后达到目标分数95（目标≥95），SKILL.md已更新至v1.5.0" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7o3eu8.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7o3eu8", | |
| 3 | + "from": "abc:hr", | |
| 4 | + "to": "innovator", | |
| 5 | + "task": "ABC 公司已招聘 6 名执行层员工：trend-hunter（热点抓取）、topic-selector（选题筛选）、researcher（深度研究）、screenwriter（剧本整理）、publisher（内容发布）、data-analyst（数据监控）。请为这些员工启动 RL 进化循环，优化技能评分到 90 分以上。", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7o3eu8", | |
| 10 | + "createdAt": "2026-03-26T16:08:57.059Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T17:12:07.955Z", | |
| 13 | + "summary": "已启动 6 个执行层员工的 RL 进化循环：trend-hunter、topic-selector、researcher、screenwriter、publisher、data-analyst。Hunter 调研任务已分发（FDE 第一阶段），待完成后继续 Digester（消化）和 Evolver（进化）训练，目标优化技能评分到 90 分以上。" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7obcl2.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7obcl2", | |
| 3 | + "from": "abc:hr", | |
| 4 | + "to": "innovator", | |
| 5 | + "task": "ABC 公司已招聘 6 人：trend-hunter, topic-selector, researcher, screenwriter, publisher, data-analyst。其中 trend-hunter 和 topic-selector 已完成技能安装，screenwriter 已有基础技能。请为 researcher, publisher, data-analyst 装备技能并优化到 90 分以上！", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7obcl2", | |
| 10 | + "createdAt": "2026-03-26T16:15:07.398Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7od1jx.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7od1jx", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "evolver", | |
| 5 | + "task": "训练热点研究员技能 hot-topic-researcher.<br><br>技能名称: hot-topic-researcher<br>目标: 为 ABC Inc. researcher Agent 训练热点抓取与深度研究能力<br><br>输入文件:<br>- 训练数据: /Users/yibo/Documents/LemonAI/lemonboss/data/workspace/innovator/abc-researcher/training_dataset.json<br>- 基准: /Users/yibo/Documents/LemonAI/lemonboss/data/workspace/innovator/abc-researcher/benchmark.md<br>- 知识库: /Users/yibo/Documents/LemonAI/lemonboss/data/workspace/innovator/abc-researcher/knowhow.md<br><br>验收标准:<br>- Score ≥ 95 (满分100)<br>- 训练完成后输出 SKILL.md 到 /Users/yibo/Documents/LemonAI/lemonboss/data/workspace/evolver/hot-topic-researcher/<br><br>评分维度:<br>1. 抓取完整性 (40分): 标题/来源/分类/时间/热度<br>2. 数据质量 (30分): 无重复/无截断/编码/格式<br>3. 研究深度 (20分): 背景/人物/剧情<br>4. 效率 (10分)<br><br>请开始 Small Loop 迭代训练，直到达标。", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": "G-002", | |
| 8 | + "nodeId": "G-002.1.1", | |
| 9 | + "executionConvId": "task-T-mn7od1jx", | |
| 10 | + "createdAt": "2026-03-26T16:16:26.399Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T17:11:20.081Z", | |
| 13 | + "summary": "abc:researcher FDE训练完成。技能 hot_topic_researcher 已部署到 researcher 目录，可执行多源热点抓取与深度研究。" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7odmic.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7odmic", | |
| 3 | + "from": "abc:coo", | |
| 4 | + "to": "abc:topic-selector", | |
| 5 | + "task": "[Pipeline第2步] 选题筛选。从 workspace/trend_report.json 的100个热点中选出5-10个最具潜力的话题，作为短剧创作的目标选题。筛选标准：1)话题有争议性/反转空间；2)适合短剧改编；3)有持续热度。输出：JSON数组，选出的话题需包含id, title, category, reason(筛选理由)，写入 workspace/selected_topics.json", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": "G-001", | |
| 8 | + "nodeId": "G-001.2", | |
| 9 | + "executionConvId": "task-T-mn7odmic", | |
| 10 | + "createdAt": "2026-03-26T16:16:53.565Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:20:02.308Z", | |
| 13 | + "summary": "已完成选题筛选，从100个热点中选出10个最具潜力的候选选题，写入 selected_topics.json。筛选结果包含：AI短剧冲击横店(95)、继母虐待案(92)、A股失守4000点(92)、张雪峰猝死(88)、油价破9(88)、黄金换购陷阱(80)、35岁国企躺平(82)、AI替代人工(88)、张雪峰遗产(85)、热水计费(85)。" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7oe3x2.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7oe3x2", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "hunter", | |
| 5 | + "task": "【Hunt任务 - trend-hunter技能调研】 请调研 trend-hunter (热点抓取员) 的能力要求：1. 从网上抓取军事、娱乐、体育等各类时事热点 2. 构建包含约100个热点的选题库 3. 覆盖军事、娱乐、体育、科技、财经等多个领域。产出要求 - 输出目录: ../../workspace/innovator/trend-hunter-hunt/ - 文件1: knowhow_raw.md - 原始调研资料（数据源列表、抓取方法、热点分类） - 文件2: crawl_config.json - 爬虫配置（目标站点、采集频率、数据格式）。验收标准 - 至少列出 5+ 热门数据源 - 覆盖 3+ 行业领域 - 提供可执行的爬取配置", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7oe3x2", | |
| 10 | + "createdAt": "2026-03-26T16:17:16.122Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7of5wz.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7of5wz", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "hunter", | |
| 5 | + "task": "【Hunt任务 - topic-selector技能调研】 请调研 topic-selector (选题筛选员) 的能力要求：1. 从今日热点中筛选最具潜力的候选选题 2. 理性客观，依赖数据和分析做决策 3. 细致严谨，不遗漏重要信息。产出要求 - 输出目录: ../../workspace/innovator/topic-selector-hunt/ - 文件1: knowhow_raw.md - 原始调研资料（筛选方法论、评分维度、数据来源） - 文件2: crawl_config.json - 配置（数据输入源、筛选规则、输出格式）。验收标准 - 至少定义 3+ 筛选维度 - 覆盖多个行业类别 - 提供可执行的筛选配置", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7of5wz", | |
| 10 | + "createdAt": "2026-03-26T16:18:05.394Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7ogile.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7ogile", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "hunter", | |
| 5 | + "task": "【Hunt任务 - researcher技能调研】 请调研 researcher (深度研究员) 的能力要求：1. 深度研究和剧本创作 2. 严谨认真，注重事实 3. 思维活跃，想象力丰富。产出要求 - 输出目录: ../../workspace/innovator/researcher-hunt/ - 文件1: knowhow_raw.md - 原始调研资料（研究方法、数据来源、内容创作框架） - 文件2: crawl_config.json - 配置（输入源、研究流程、输出格式）。验收标准 - 至少定义 3+ 研究维度 - 包含剧本创作方法论 - 提供可执行的配置", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7ogile", | |
| 10 | + "createdAt": "2026-03-26T16:19:08.453Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:46:53.396Z", | |
| 13 | + "summary": "researcher技能调研已完成。产出：1) knowhow_raw.md - 包含研究方法论(5种研究类型)、剧本创作方法论(场景构建/三幕式结构/人物弧光等)、深度研究能力要求(4个维度)；2) crawl_config.json - 包含输入源、研究流程(5步骤)、输出格式、质量标准、能力维度定义；3) seed_data.json - 56条种子数据，涵盖研究方法、分析技能、剧本写作、数据源、能力要求等。验收标准：3+研究维度✓ 剧本创作方法论✓ 可执行配置✓" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7ogiob.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7ogiob", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "hunter", | |
| 5 | + "task": "【Hunt任务 - screenwriter技能调研】 请调研 screenwriter (剧本整理员) 的能力要求：1. 剧本格式规范化 2. 结构化整理 3. 格式校验 4. 细心认真，擅长格式整理，注重规范性。产出要求 - 输出目录: ../../workspace/innovator/screenwriter-hunt/ - 文件1: knowhow_raw.md - 原始调研资料（剧本格式规范、标准化模板） - 文件2: crawl_config.json - 配置（输入格式、校验规则、输出格式）。验收标准 - 至少定义 3+ 格式规范维度 - 包含标准化模板 - 提供可执行的配置", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7ogiob", | |
| 10 | + "createdAt": "2026-03-26T16:19:08.556Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:47:30.282Z", | |
| 13 | + "summary": "screenwriter技能调研完成。产出：knowhow_raw.md（5个格式规范维度）、crawl_config.json（10条校验规则）、seed_data.json（55条数据）" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7ohbqc.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7ohbqc", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "hunter", | |
| 5 | + "task": "【Hunt任务 - publisher技能调研】 请调研 publisher (内容发布员) 的能力要求：1. 将剧本上传到生产端 2. 内容上传、发布管理。产出要求 - 输出目录: ../../workspace/innovator/publisher-hunt/ - 文件1: knowhow_raw.md - 原始调研资料（发布平台、发布流程、格式要求） - 文件2: crawl_config.json - 配置（目标平台、认证方式、发布格式）。验收标准 - 至少定义 3+ 发布平台 - 包含发布流程 - 提供可执行的配置", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7ohbqc", | |
| 10 | + "createdAt": "2026-03-26T16:19:46.214Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:24:51.469Z", | |
| 13 | + "summary": "完成 publisher 技能调研，产出 5+ 发布平台配置（抖音/快手/视频号/B站/小红书），包含各平台认证方式、发布格式、API能力对比" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7ohbrg.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7ohbrg", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "hunter", | |
| 5 | + "task": "【Hunt任务 - data-analyst技能调研】 请调研 data-analyst (数据分析员) 的能力要求：1. 数据分析工作 2. 专业的数据分析师 3. 公司决策的数据支撑者 4. 选题优化的量化依据提供者。产出要求 - 输出目录: ../../workspace/innovator/data-analyst-hunt/ - 文件1: knowhow_raw.md - 原始调研资料（分析方法、数据源、指标体系） - 文件2: crawl_config.json - 配置（输入数据、分析维度、输出格式）。验收标准 - 至少定义 3+ 分析维度 - 包含量化指标体系 - 提供可执行的配置", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7ohbrg", | |
| 10 | + "createdAt": "2026-03-26T16:19:46.253Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:31:08.073Z", | |
| 13 | + "summary": "已完成data-analyst技能调研，产出4维分析能力模型、量化指标体系(增长/收入/运营/质量)、数据源配置(数据库/日志/BI/埋点)、输出格式规范(Markdown报告/CSV导出/图表/API)，满足验收标准" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7oi92j.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7oi92j", | |
| 3 | + "from": "abc:cto", | |
| 4 | + "to": "abc:hr", | |
| 5 | + "task": "紧急修复：Pipeline 执行阻塞！以下 Agent 处于 training 状态无法接收任务，导致 Pipeline 第一步热点抓取无法启动。请立即：<br>1. 完成 trend-hunter 技能装配并设为 ready<br>2. 完成 researcher 技能装配并设为 ready <br>3. 完成 screenwriter 技能装配并设为 ready<br>完成汇报。", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7oi92j", | |
| 10 | + "createdAt": "2026-03-26T16:20:29.425Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7oihet.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7oihet", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "hunter", | |
| 5 | + "task": "【Hunt任务 - trend-hunter热点抓取技能】<br><br>输入：无<br><br>产出路径：../../workspace/hunter/trend-hunter/<br><br>任务描述：调研热点抓取技能需要的知识，包括：<br>1. 调研目标：从网上抓取军事、娱乐、体育等各类时事热点<br>2. 目标来源：微博热搜、百度热搜、抖音热榜、知乎热榜、哔哩哔哩热门、Twitter Trending等<br>3. 数据要求：构建包含约100个热点的选题库<br>4. 输出格式：热点标题、热度指数、话题分类、话题标签、简单描述<br><br>请深度调研并首次爬取数据，输出knowhow_raw.md和crawl_config.json", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7oihet", | |
| 10 | + "createdAt": "2026-03-26T16:20:40.253Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:47:00.174Z", | |
| 13 | + "summary": "完成trend-hunter热点抓取技能调研，产出：knowhow_raw.md（8个数据源调研）、crawl_config.json（爬虫配置）、seed_data.json（50条热点数据）" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7ojzgu.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7ojzgu", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "hunter", | |
| 5 | + "task": "【Hunt任务 - topic-selector选题筛选技能】<br><br>输入：热点选题库 (100个热点)<br><br>产出路径：../../workspace/hunter/topic-selector/<br><br>任务描述：调研选题筛选技能需要的知识，包括：<br>1. 筛选目标：从热点选题库中筛选出5-10个最有价值的选题<br>2. 评估维度：市场热度、IP价值、话题争议性、用户参与度、转化潜力<br>3. 输出要求：按固定格式输出候选选题列表，包含评分和筛选理由<br><br>请深度调研并输出knowhow_raw.md和crawl_config.json", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7ojzgu", | |
| 10 | + "createdAt": "2026-03-26T16:21:51.639Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7okbis.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7okbis", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "hunter", | |
| 5 | + "task": "【Hunt任务 - researcher深度研究技能】<br><br>输入：目标选题<br><br>产出路径：../../workspace/hunter/researcher/<br><br>任务描述：调研深度研究技能需要的知识，包括：<br>1. 研究目标：针对每个目标选题进行背景调查、人物形象塑造<br>2. 输出要求：生成相应的台词和剧本，包含场景描述、角色对白、情绪表达<br>3. 数据来源：百度百科、维基百科、新闻报道、社交媒体讨论<br><br>请深度调研并输出knowhow_raw.md和crawl_config.json", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7okbis", | |
| 10 | + "createdAt": "2026-03-26T16:22:05.914Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T17:22:42.944Z", | |
| 13 | + "summary": "完成researcher深度研究技能调研，产出knowhow_raw.md(5大数据源)、crawl_config.json(爬取配置)、seed_data.json(50条种子数据)" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7okdxt.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7okdxt", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "hunter", | |
| 5 | + "task": "【Hunt任务 - screenwriter剧本整理技能】<br><br>输入：原始剧本/台词<br><br>产出路径：../../workspace/hunter/screenwriter/<br><br>任务描述：调研剧本整理技能需要的知识，包括：<br>1. 整理目标：将深度研究生成的剧本按固定格式整理完毕<br>2. 格式要求：确保符合生产端要求，包含场景分隔、角色标注、台词格式化<br>3. 输出：标准化的剧本文档<br><br>请深度调研并输出knowhow_raw.md和crawl_config.json", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7okdxt", | |
| 10 | + "createdAt": "2026-03-26T16:22:09.041Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7okiaj.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7okiaj", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "hunter", | |
| 5 | + "task": "【Hunt任务 - publisher内容上传技能】<br><br>输入：通过审核的剧本<br><br>产出路径：../../workspace/hunter/publisher/<br><br>任务描述：调研内容上传技能需要的知识，包括：<br>1. 上传目标：按固定格式将剧本上传至生产端的剧中<br>2. 目标平台：模拟内容发布系统（需了解上传接口、格式要求、验证规则）<br>3. 输出：上传结果确认、错误处理<br><br>请深度调研并输出knowhow_raw.md和crawl_config.json", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7okiaj", | |
| 10 | + "createdAt": "2026-03-26T16:22:14.691Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:44:15.927Z", | |
| 13 | + "summary": "已完成Publisher内容上传技能调研，产出：knowhow_raw.md(内容发布系统接口规范、元数据格式、验证规则、错误处理)、crawl_config.json(爬取配置)、seed_data.json(种子数据)至workspace/hunter/publisher/" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7okk6g.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7okk6g", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "hunter", | |
| 5 | + "task": "【Hunt任务 - data-analyst数据分析技能】<br><br>输入：生产端数据<br><br>产出路径：../../workspace/hunter/data-analyst/<br><br>任务描述：调研数据分析技能需要的知识，包括：<br>1. 监控目标：监控生产端数据（市场热度、玩家反馈）<br>2. 评估维度：选题受欢迎程度、用户留存、互动率、收益指标<br>3. 输出：迭代建议报告<br><br>请深度调研并输出knowhow_raw.md和crawl_config.json", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7okk6g", | |
| 10 | + "createdAt": "2026-03-26T16:22:17.354Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7om88w.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7om88w", | |
| 3 | + "from": "agent-creator", | |
| 4 | + "to": "novelai:ceo", | |
| 5 | + "task": "设计 NovelAI Inc. 公司架构：基于番茄小说自动生产需求，完成：1）生产流水线设计（热点采集→选题→大纲→正文→审核→发布）2）团队架构规划（需要哪些专职Agent）3）质量与合规体系（纯AI审核机制）4）产能优化策略（初期不限产量，测试系统上限）5）输出完整公司架构文档到 workspace/novelai_architecture.md", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": "G-002", | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7om88w", | |
| 10 | + "createdAt": "2026-03-26T16:23:34.981Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:34:25.081Z", | |
| 13 | + "summary": "已完成 NovelAI Inc. 公司架构设计，输出文档至 workspace/novelai_architecture.md。包含：1) 生产流水线设计（6环节）2) 团队架构规划（8类Agent）3) 质量与合规体系（三层AI审核）4) 产能优化策略（三阶段目标）5) 数据流设计与KPI指标" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7omp2a.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7omp2a", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "evolver", | |
| 5 | + "task": "优化 ABC 公司 trend-hunter 技能，目标 90 分以上。<br><br>## 背景<br>- Agent: abc:trend-hunter (热点抓取)<br>- 现有技能: workspace/companies/abc/agents/trend-hunter/.claude/skills/scrapling/SKILL.md (简短版本)<br>- 目标: 90+ 分 (10分制 = 9.0)<br><br>## 要求<br>1. 读取现有技能定义，评估当前水平<br>2. 扩展技能为完整版，包含:<br> - 输入输出规范<br> - 爬虫配置 (平台选择: 微博、知乎、抖音、B站等)<br> - 数据清洗规则<br> - 输出格式 (JSON Schema)<br> - 质量标准<br> - Benchmark 评分维度<br>3. 使用 Evolver 进行迭代训练<br>4. 达标后部署到 Agent 目录", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": "G-abc-001", | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7omp2a", | |
| 10 | + "createdAt": "2026-03-26T16:23:56.773Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7onzpm.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7onzpm", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "hunter", | |
| 5 | + "task": "调研 topic-selector 技能需要。目标：分析选题筛选的最佳实践，产出数据源清单、爬取配置、种子数据。输入：ABC 公司业务场景（AI 互动剧选题筛选）。产出路径：data/workspace/hunter/topic-selector/", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7onzpm", | |
| 10 | + "createdAt": "2026-03-26T16:24:57.226Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7oockp.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7oockp", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "hunter", | |
| 5 | + "task": "调研 trend-hunter 技能需要。目标：分析热点抓取的最佳实践，产出数据源清单、爬取配置、种子数据。输入：ABC 公司业务场景（军事/娱乐/体育等时事热点抓取）。产出路径：data/workspace/hunter/trend-hunter/", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7oockp", | |
| 10 | + "createdAt": "2026-03-26T16:25:13.899Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7oogqz.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7oogqz", | |
| 3 | + "from": "abc:coo", | |
| 4 | + "to": "abc:screenwriter", | |
| 5 | + "task": "[Pipeline第3步] 深度研究与剧本创作。根据 workspace/selected_topics.json 的10个选题，生成短剧剧本。每个选题需产出：1)背景调查报告 2)人物小传 3)剧情大纲 4)核心台词。输出JSON格式，写入 workspace/scripts/script_collection.json", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": "G-001", | |
| 8 | + "nodeId": "G-001.3", | |
| 9 | + "executionConvId": "task-T-mn7oogqz", | |
| 10 | + "createdAt": "2026-03-26T16:25:20.191Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T21:23:48.126Z", | |
| 13 | + "summary": "已完成10个选题的短剧剧本生成，每个选题包含：背景调查报告、人物小传(2-4人)、剧情大纲(3-5个场景)、核心台词。输出至 workspace/scripts/script_collection.json" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7oonnd.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7oonnd", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "hunter", | |
| 5 | + "task": "调研 researcher 技能需要。目标：分析深度研究的最佳实践，产出数据源清单、爬取配置、种子数据。输入：ABC 公司业务场景（互动剧背景调查、人物塑造、台词生成）。产出路径：data/workspace/hunter/researcher/", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7oonnd", | |
| 10 | + "createdAt": "2026-03-26T16:25:28.250Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7oow4f.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7oow4f", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "hunter", | |
| 5 | + "task": "【HUNT 调研任务】为 ABC 公司 trend-hunter（热点抓取员）进行技能调研。<br><br>## 目标 Agent<br>- ID: trend-hunter<br>- 角色: 热点抓取员<br>- 核心能力: 从网上抓取军事、娱乐、体育等各类时事热点，构建包含约100个热点的选题库<br><br>## 调研要求<br>1. 研究如何高效抓取热点：包括数据源选择、爬虫策略、筛选标准<br>2. 识别热门数据源：微博热搜、百度风云榜、知乎热榜、Twitter trending、Reddit 等<br>3. 设计抓取策略：频率、更新机制、分类方式<br>4. 确定输出格式：热点标题、热度值、分类标签、摘要<br><br>## 输出目录<br>`../../workspace/innovator/trend-hunter/hunt/`<br><br>## 输出文件<br>1. `knowhow_raw.md` - 原始调研笔记<br>2. `crawl_config.json` - 爬虫配置（数据源 URLs、频率、筛选规则）<br>3. `toplist.md` - 热门平台清单", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": "G-002", | |
| 8 | + "nodeId": "G-002.1.1", | |
| 9 | + "executionConvId": "task-T-mn7oow4f", | |
| 10 | + "createdAt": "2026-03-26T16:25:39.232Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7op2ps.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7op2ps", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "hunter", | |
| 5 | + "task": "调研 screenwriter 技能需要。目标：分析剧本整理的最佳实践，产出数据源清单、爬取配置、种子数据。输入：ABC 公司业务场景（互动剧剧本格式整理、台词提取、场景划分）。产出路径：data/workspace/hunter/screenwriter/", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7op2ps", | |
| 10 | + "createdAt": "2026-03-26T16:25:47.795Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7opotd.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7opotd", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "hunter", | |
| 5 | + "task": "调研 publisher 技能需要。目标：分析内容发布的最佳实践，产出数据源清单、爬取配置、种子数据。输入：ABC 公司业务场景（互动剧内容上传、生产端对接）。产出路径：data/workspace/hunter/publisher/", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7opotd", | |
| 10 | + "createdAt": "2026-03-26T16:26:17.046Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T17:01:05.576Z", | |
| 13 | + "summary": "publisher技能调研完成：产出数据源清单(含8个发布平台)、爬取配置(crawl_config.json含5个平台详细配置)、种子数据(seed_data.json含10个测试用例)" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7opxje.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7opxje", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "evolver", | |
| 5 | + "task": "创建并训练 ABC 公司 screenwriter 技能，目标 90 分以上。<br><br>## 背景<br>- Agent: abc:screenwriter (剧本整理)<br>- 现有状态: 无技能定义<br>- 目标: 90+ 分 (10分制 = 9.0)<br><br>## 流水线位置<br> screenwriter 是 ABC 内容生产流水线的第 4 步：<br> 1. trend-hunter (热点抓取)<br> 2. topic-selector (选题筛选)<br> 3. researcher (深度研究)<br> 4. screenwriter (剧本整理) ← 本任务<br> 5. publisher (内容发布)<br> 6. data-analyst (数据监控)<br><br>## 技能要求<br>1. 输入: researcher 产出的调研结果 (JSON)<br>2. 输出: 标准化剧本 (JSON)<br>3. 能力:<br> - 将调研素材改编为短剧剧本<br> - 场景划分、人物塑造、对话编写<br> - 符合短剧节奏 (冲突-反转-高潮)<br> - 输出格式符合 publisher 的输入规范<br><br>## 产出要求<br>1. 创建 skills/screenwriter/SKILL.md (完整版)<br>2. 进行迭代训练直到 90+ 分<br>3. 部署到 data/companies/abc/agents/screenwriter/.claude/skills/", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": "G-abc-001", | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7opxje", | |
| 10 | + "createdAt": "2026-03-26T16:26:28.389Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T17:01:02.743Z", | |
| 13 | + "summary": "训练完成，screenwriter 技能已达标 95 分，部署至 data/companies/abc/agents/screenwriter/.claude/skills/main_skill/SKILL.md" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7oqle8.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7oqle8", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "hunter", | |
| 5 | + "task": "调研 data-analyst 技能需要。目标：分析数据分析的最佳实践，产出数据源清单、爬取配置、种子数据。输入：ABC 公司业务场景（互动剧数据监控、市场热度、玩家反馈分析）。产出路径：data/workspace/hunter/data-analyst/", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7oqle8", | |
| 10 | + "createdAt": "2026-03-26T16:26:58.670Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:54:18.814Z", | |
| 13 | + "summary": "data-analyst技能调研完成：产出17个数据源、爬取配置、50条种子数据" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7or3w7.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7or3w7", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "hunter", | |
| 5 | + "task": "【HUNT 调研任务】为 ABC 公司 topic-selector（选题筛选员）进行技能调研。<br><br>## 目标 Agent<br>- ID: topic-selector<br>- 角色:选题筛选员<br>- 核心能力:从热点库中筛选 5-10 个最具潜力的目标选题，评估排序后传递给深度研究<br><br>## 调研要求<br>1. 研究选题筛选方法论：筛选标准、评分模型、排序算法<br>2. 分析热门内容特征：时效性、话题性、改编潜力、受众面<br>3. 设计评估维度：热度、时效性、争议性、改编潜力<br>4. 确定输出格式：选题列表、评分、排序理由<br><br>## 输出目录<br>`../../workspace/innovator/topic-selector/hunt/`<br><br>## 输出文件<br>1. `knowhow_raw.md` - 原始调研笔记<br>2. `selection_criteria.md` - 筛选标准文档<br>3. `scoring_model.json` - 评分模型", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": "G-002", | |
| 8 | + "nodeId": "G-002.1.2", | |
| 9 | + "executionConvId": "task-T-mn7or3w7", | |
| 10 | + "createdAt": "2026-03-26T16:27:22.663Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T17:09:08.925Z", | |
| 13 | + "summary": "topic-selector技能调研完成。产出：knowhow_raw.md（热点数据源调研）、selection_criteria.md（筛选标准）、scoring_model.json（5维度评分模型）" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7or59u.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7or59u", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "digester", | |
| 5 | + "task": "消化 abc:data-analyst 的调研数据。skillName: data_analysis, targetAgentId: data-analyst。输入: data/workspace/data-analyst/hunt/ 的 knowhow_raw.md 和 seed_data.json。输出到: data/workspace/data-analyst/digest/ 和 data/companies/abc/agents/data-analyst/digest/。完成后汇报产出路径和内容摘要。", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7or59u", | |
| 10 | + "createdAt": "2026-03-26T16:27:24.842Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T17:17:06.845Z", | |
| 13 | + "summary": "data-analyst 技能已消化完成。产出: knowhow.md(知识手册)、SKILL.md(技能定义)、benchmark.md(评分标准)、train.json(8条训练数据)、test.json(2条测试数据)、industry_avg.json(行业基准82分)。已同步至 data/workspace/data-analyst/digest/ 和 data/companies/abc/agents/data-analyst/digest/" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7osx7a.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7osx7a", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "evolver", | |
| 5 | + "task": "创建并训练 ABC 公司 researcher 技能，目标 90 分以上。<br><br>## 背景<br>- Agent: abc:researcher (深度研究)<br>- 现有状态: 有基础技能 (skills/research/SKILL.md)<br>- 目标: 90+ 分 (10分制 = 9.0)<br><br>## 流水线位置<br> researcher 是 ABC 内容生产流水线的第 3 步：<br> 1. trend-hunter (热点抓取)<br> 2. topic-selector (选题筛选)<br> 3. researcher (深度研究) ← 本任务<br> 4. screenwriter (剧本整理)<br> 5. publisher (内容发布)<br> 6. data-analyst (数据监控)<br><br>## 技能要求<br>1. 输入: topic-selector 选中的精选选题 (JSON)<br>2. 输出: 深度调研报告 (JSON)<br>3. 能力:<br> - 背景资料收集<br> - 人物信息整理<br> - 关键事件梳理<br> - 台词/对话素材提取<br> - 短剧改编建议<br><br>## 产出要求<br>1. 扩展 skills/research/SKILL.md 为完整版<br>2. 进行迭代训练直到 90+ 分<br>3. 部署到 data/companies/abc/agents/researcher/.claude/skills/", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": "G-abc-001", | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7osx7a", | |
| 10 | + "createdAt": "2026-03-26T16:28:48.162Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7ou99j.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7ou99j", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "hunter", | |
| 5 | + "task": "【HUNT 调研任务】为 ABC 公司 researcher（深度研究员）进行技能调研。<br><br>## 目标 Agent<br>- ID: researcher<br>- 角色:深度研究员<br>- 核心能力:接收 topic-selector 提供的选题，进行背景调查、人物形象塑造，生成对话台词和剧本内容<br><br>## 调研要求<br>1. 研究深度研究方法论：背景调查、资料收集技巧<br>2. 分析人物塑造方法：性格设计、对话生成、情感表达<br>3. 研究剧本创作：结构设计、场景划分、台词风格<br>4. 确定输出格式：背景资料、人物卡、剧本结构<br><br>## 输出目录<br>`../../workspace/innovator/researcher/hunt/`<br><br>## 输出文件<br>1. `knowhow_raw.md` - 原始调研笔记<br>2. `research_methodology.md` - 研究方法论文档<br>3. `character_template.md` - 人物模板<br>4. `script_structure.md` - 剧本结构模板", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": "G-002", | |
| 8 | + "nodeId": "G-002.1.3", | |
| 9 | + "executionConvId": "task-T-mn7ou99j", | |
| 10 | + "createdAt": "2026-03-26T16:29:50.313Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T16:43:27.511Z", | |
| 13 | + "summary": "Researcher技能调研完成，产出4个文件：knowhow_raw.md(调研笔记)、research_methodology.md(研究方法论)、character_template.md(人物模板)、script_structure.md(剧本结构模板)，涵盖背景调查、人物塑造、剧本创作方法论" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7owylu.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7owylu", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "evolver", | |
| 5 | + "task": "评估并优化 ABC 公司 topic-selector 技能，目标 90 分以上。<br><br>## 背景<br>- Agent: abc:topic-selector (选题筛选)<br>- 现有技能: skills/topic-selector/SKILL.md (完整版 v1.0)<br>- 目标: 90+ 分 (10分制 = 9.0)<br><br>## 流水线位置<br> topic-selector 是 ABC 内容生产流水线的第 2 步：<br> 1. trend-hunter (热点抓取)<br> 2. topic-selector (选题筛选) ← 本任务<br> 3. researcher (深度研究)<br> 4. screenwriter (剧本整理)<br> 5. publisher (内容发布)<br> 6. data-analyst (数据监控)<br><br>## 要求<br>1. 读取现有技能定义<br>2. 设计 Benchmark 评测标准<br>3. 进行迭代训练优化到 90+ 分<br>4. 如已达 90+，更新技能状态为 verified<br><br>## 技能已有内容<br>- 六大评估维度 (短剧化潜力、时效性、商业价值等)<br>- 评级标准 (S/A/B/C/D)<br>- 工作流程 (5步)<br>- 输出格式 (JSON Schema)<br>- 自检清单", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": "G-abc-001", | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7owylu", | |
| 10 | + "createdAt": "2026-03-26T16:31:55.714Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7oz4jj.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7oz4jj", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "evolver", | |
| 5 | + "task": "评估并优化 ABC 公司 publisher 技能，目标 90 分以上。<br><br>## 背景<br>- Agent: abc:publisher (内容发布)<br>- 现有技能: .claude/skills/content-publisher/SKILL.md (v1.0)<br>- 目标: 90+ 分 (10分制 = 9.0)<br><br>## 流水线位置<br> publisher 是 ABC 内容生产流水线的第 5 步：<br> 1. trend-hunter (热点抓取)<br> 2. topic-selector (选题筛选)<br> 3. researcher (深度研究)<br> 4. screenwriter (剧本整理)<br> 5. publisher (内容发布) ← 本任务<br> 6. data-analyst (数据监控)<br><br>## 现有技能内容<br>- 剧本数据格式 (JSON Schema)<br>- 字段映射规则<br>- Genre → Category 映射 (20类)<br>- API 调用规范<br>- 错误处理策略 (5种)<br>- Benchmark: 目标95分, 及格80分<br><br>## 要求<br>1. 读取现有技能定义<br>2. 进行迭代训练优化到 90+ 分<br>3. 更新 Benchmark 目标为 90+<br>4. 部署到 Agent 目录", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": "G-abc-001", | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7oz4jj", | |
| 10 | + "createdAt": "2026-03-26T16:33:36.725Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7p0ua9.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7p0ua9", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "evolver", | |
| 5 | + "task": "创建并训练 ABC 公司 data-analyst 技能，目标 90 分以上。<br><br>## 背景<br>- Agent: abc:data-analyst (数据监控)<br>- 现有状态: 无技能定义<br>- 目标: 90+ 分 (10分制 = 9.0)<br><br>## 流水线位置<br> data-analyst 是 ABC 内容生产流水线的第 6 步（最后一步）：<br> 1. trend-hunter (热点抓取)<br> 2. topic-selector (选题筛选)<br> 3. researcher (深度研究)<br> 4. screenwriter (剧本整理)<br> 5. publisher (内容发布)<br> 6. data-analyst (数据监控) ← 本任务<br><br>## 技能要求<br>1. 输入: publisher 上传后返回的内容ID<br>2. 输出: 每日数据监控报告 (JSON)<br>3. 能力:<br> - 监控内容曝光量、点击率、完播率<br> - 收集用户反馈和评价<br> - 分析选题受欢迎程度<br> - 生成迭代建议<br> - 推送日报给 COO/CEO<br><br>## 产出要求<br>1. 创建 skills/data-analyst/SKILL.md (完整版)<br>2. 进行迭代训练直到 90+ 分<br>3. 部署到 data/companies/abc/agents/data-analyst/.claude/skills/", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": "G-abc-001", | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7p0ua9", | |
| 10 | + "createdAt": "2026-03-26T16:34:56.726Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7p1edg.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7p1edg", | |
| 3 | + "from": "master", | |
| 4 | + "to": "abc:coo", | |
| 5 | + "task": "【Pipeline 推进任务】Pipeline R-mn7o642g 已停留在 Step 0（热点抓取完成）很长时间。请执行以下操作：<br><br>1. 检查 trend-hunter 产出：workspace/trend_report.json 是否存在且质量合格<br>2. 如果合格，调用 Pipeline API 推进到 Step 1（选题筛选）<br>3. 如果不合格，驳回并要求重新抓取<br><br>这是你作为 COO 的核心职责 - 监督和推进流水线运转。", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7p1edg", | |
| 10 | + "createdAt": "2026-03-26T16:35:22.767Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7p7jco.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7p7jco", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "hunter", | |
| 5 | + "task": "【HUNT 调研任务】为 ABC 公司 screenwriter（剧本整理员）进行技能调研。<br><br>## 目标 Agent<br>- ID: screenwriter<br>- 角色:剧本整理员<br>- 核心能力:将编剧产出的剧本按照固定格式整理成可上传的生产文件<br><br>## 调研要求<br>1. 研究剧本整理规范：格式要求、结构标准<br>2. 分析上传格式：JSON schema、字段定义<br>3. 设计质量检查项：完整性、规范性、一致性<br>4. 确定输出格式：结构化剧本、校验报告<br><br>## 输出目录<br>`../../workspace/innovator/screenwriter/hunt/`<br><br>## 输出文件<br>1. `knowhow_raw.md` - 原始调研笔记<br>2. `format_standard.md` - 格式规范文档<br>3. `validation_rules.md` - 校验规则", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": "G-002", | |
| 8 | + "nodeId": "G-002.1.4", | |
| 9 | + "executionConvId": "task-T-mn7p7jco", | |
| 10 | + "createdAt": "2026-03-26T16:40:11.657Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7p9e6n.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7p9e6n", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "digester", | |
| 5 | + "task": "对 abc:data-analyst 进行 Phase 2 DIGEST（知识消化）。基于 Hunt 阶段的产出（knowhow_raw.md + seed_data.json），构建结构化知识 knowhow.md、benchmark.md、训练/测试数据集、industry_avg_score。产出写入 data/workspace/data-analyst/digest/。完成后汇报产出路径和内容摘要。", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7p9e6n", | |
| 10 | + "createdAt": "2026-03-26T16:41:35.855Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7poily.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7poily", | |
| 3 | + "from": "abc:hr", | |
| 4 | + "to": "innovator", | |
| 5 | + "task": "我已为公司ABC的执行层Agent装配了以下技能，请启动RL进化循环优化到90分以上：<br>1. trend-hunter/.claude/skills/scrapling - 热点抓取技能<br>2. topic-selector/.claude/skills/analytics - 选题筛选技能<br>3. researcher/.claude/skills/research - 深度调研技能<br>4. screenwriter/.claude/skills/screenplay - 剧本编写技能<br>5. publisher/.claude/skills/publish - 内容发布技能<br>6. data-analyst/.claude/skills/monitor - 数据监控技能<br><br>请逐个优化这些技能，确保Benchmark评分≥90分。", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7poily", | |
| 10 | + "createdAt": "2026-03-26T16:53:21.430Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7pt86t.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7pt86t", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "hunter", | |
| 5 | + "task": "【HUNT 调研任务】为 ABC 公司 publisher（内容发布员）进行技能调研。<br><br>## 目标 Agent<br>- ID: publisher<br>- 角色:内容发布员<br>- 核心能力:按固定格式将剧本上传至生产端的剧中，完成初步生产<br><br>## 调研要求<br>1. 研究上传流程：API 接口、认证方式、错误处理<br>2. 分析上传规范：文件格式、字段要求、大小限制<br>3. 设计重试机制：失败处理、日志记录<br>4. 确定输出格式：上传结果、返回信息<br><br>## 输出目录<br>`../../workspace/innovator/publisher/hunt/`<br><br>## 输出文件<br>1. `knowhow_raw.md` - 原始调研笔记<br>2. `upload_api.md` - 上传 API 文档<br>3. `error_handling.md` - 错误处理规范", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": "G-002", | |
| 8 | + "nodeId": "G-002.1.5", | |
| 9 | + "executionConvId": "task-T-mn7pt86t", | |
| 10 | + "createdAt": "2026-03-26T16:57:02.096Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7pw42p.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7pw42p", | |
| 3 | + "from": "master", | |
| 4 | + "to": "novelai:ceo", | |
| 5 | + "task": "NovelAI Inc. 已完成公司架构设计文档 (workspace/novelai_architecture.md)。现在你需要开始招聘核心团队。根据架构文档，你需要招聘：1) CTO (技术总监) - 向你汇报，负责技术架构和Agent创建；2) COO (运营总监) - 向你汇报，负责运营管理；3) 招聘流程：先设计岗位需求文档，输出到 workspace/job_xxx.md，然后通过 agent-creator 创建 Agent。完成后向我汇报。", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7pw42p", | |
| 10 | + "createdAt": "2026-03-26T16:59:16.533Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7qb9dj.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7qb9dj", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "hunter", | |
| 5 | + "task": "【HUNT 调研任务】为 ABC 公司 data-analyst（数据分析师）进行技能调研。<br><br>## 目标 Agent<br>- ID: data-analyst<br>- 角色:数据分析师<br>- 核心能力:监控生产端数据、分析热点受欢迎程度、生成数据报告辅助决策<br><br>## 调研要求<br>1. 研究数据分析方法：数据采集、指标设计、分析模型<br>2. 分析数据源：生产端 API、数据库、日志系统<br>3. 设计报告模板：KPI 指标、可视化维度<br>4. 确定输出格式：数据报告、趋势分析、建议<br><br>## 输出目录<br>`../../workspace/innovator/data-analyst/hunt/`<br><br>## 输出文件<br>1. `knowhow_raw.md` - 原始调研笔记<br>2. `data_sources.md` - 数据源文档<br>3. `metrics_model.md` - 指标模型", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": "G-002", | |
| 8 | + "nodeId": "G-002.1.6", | |
| 9 | + "executionConvId": "task-T-mn7qb9dj", | |
| 10 | + "createdAt": "2026-03-26T17:11:03.483Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7yssn1.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7yssn1", | |
| 3 | + "from": "master", | |
| 4 | + "to": "abc:coo", | |
| 5 | + "task": "【紧急】Pipeline R-mn7o642g 必须推进。请立即执行：<br><br>1. 确认 selected_topics.json (10个选题) 已存在且质量合格<br>2. 调用 Pipeline API 推进到 Step 2 (深度研究)<br>3. 将 topic-selector 产出的选题传递给 researcher<br><br>数据已就绪：workspace/selected_topics.json<br><br>如果无法调用 API，直接回复，我将采取其他措施。", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7yssn1", | |
| 10 | + "createdAt": "2026-03-26T21:08:38.096Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T22:40:13.295Z", | |
| 13 | + "summary": "已确认 selected_topics.json 包含10个高质量选题(AI短剧、股市震荡、职场35岁危机等)，推进 Pipeline 到 Step 3(深度研究)，并已将选题分发给 researcher 进行深度研究，任务ID: T-mn81bf7p" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7ytrcu.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7ytrcu", | |
| 3 | + "from": "master", | |
| 4 | + "to": "novelai:ceo", | |
| 5 | + "task": "立即开始招聘核心团队。根据 workspace/job_cto.md 和 workspace/job_coo.md，你已经完成了岗位设计文档。现在你需要：1) 通过 agent-creator 创建 CTO Agent；2) 通过 agent-creator 创建 COO Agent。完成后向我汇报。", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7ytrcu", | |
| 10 | + "createdAt": "2026-03-26T21:09:23.337Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T23:42:19.877Z", | |
| 13 | + "summary": "已成功创建两名核心团队成员：1) CTO Agent (novelai:cto) - 负责技术架构设计和Agent系统开发；2) COO Agent (novelai:coo) - 负责运营管理和生产流水线协调。两人将协助CEO共同推进NovelAI Inc.的7x24小时短篇小说自动生产体系建设。" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7z1m13.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7z1m13", | |
| 3 | + "from": "abc:coo", | |
| 4 | + "to": "abc:researcher", | |
| 5 | + "task": "[Pipeline第3步] 深度研究。针对 workspace/selected_topics.json 中的10个话题进行深度研究，每个话题需产出：1)背景调查（事件完整脉络）；2)人物形象设计；3)台词/剧本片段（至少3幕）。输出格式：JSON数组，每个元素包含 topicId, title, background, characters[{}], scenes[{}]。写入 workspace/research_output.json", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": "G-001", | |
| 8 | + "nodeId": "G-001.3", | |
| 9 | + "executionConvId": "task-T-mn7z1m13", | |
| 10 | + "createdAt": "2026-03-26T21:15:39.123Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T22:23:32.810Z", | |
| 13 | + "summary": "已完成10个话题深度研究并生成research_output.json，包含背景调查、人物设计和3幕以上台词剧本" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7zozvp.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7zozvp", | |
| 3 | + "from": "master", | |
| 4 | + "to": "novelai:ceo", | |
| 5 | + "task": "催促：你的 workspace/ 中已经有 job_cto.md 和 job_coo.md 岗位设计文档。现在请立即执行以下任务：1) 通过 agent-creator 创建 CTO Agent (novelai:cto)；2) 通过 agent-creator 创建 COO Agent (novelai:coo)。完成后向我汇报。", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7zozvp", | |
| 10 | + "createdAt": "2026-03-26T21:33:42.583Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7zp7jw.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7zp7jw", | |
| 3 | + "from": "abc:coo", | |
| 4 | + "to": "abc:topic-selector", | |
| 5 | + "task": "[Pipeline任务] 执行每日内容生产流水线第2步：选题筛选。请从100个热点中筛选出5-10个最具潜力的目标选题。评估标准：话题热度、改编潜力、时效性、目标受众覆盖面。输出精选选题列表（JSON格式）及筛选理由。文件写入 workspace/selected_topics.json", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7zp7jw", | |
| 10 | + "createdAt": "2026-03-26T21:33:49.825Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T23:19:01.241Z", | |
| 13 | + "summary": "已完成选题筛选任务：从100个热点中筛选出10个最具潜力的候选选题，涵盖AI冲击影视、家庭暴力、名人纠纷、遗产悬念、金融波动、职场健康、消费维权、教育乱收费、职场35岁危机、AI替代人工等领域。所有选题均具备情感冲突强、社会讨论空间大、适合互动剧分支叙事的特点。筛选结果已写入 workspace/selected_topics.json" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn7zsy91.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn7zsy91", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "evolver", | |
| 5 | + "task": "训练 abc:data-analyst 的 data_analysis 技能。targetAgentId: data-analyst。输入: data/workspace/data-analyst/digest/ 的 SKILL.md, benchmark.md, train.json, test.json。输出到: data/workspace/data-analyst/evolve/ 和 data/companies/abc/agents/data-analyst/evolve/。训练要求: 1) 目标分数 ≥ 95 分 (100分制); 2) 最大迭代 10 轮; 3) 每轮分析弱维度并优化; 4) 训练完成后部署 SKILL.md 到 data/companies/abc/agents/data-analyst/.claude/skills/main_skill/SKILL.md。完成后汇报最终分数和产出路径。", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn7zsy91", | |
| 10 | + "createdAt": "2026-03-26T21:36:45.763Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn810o7l.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn810o7l", | |
| 3 | + "from": "novelai:ceo", | |
| 4 | + "to": "agent-creator", | |
| 5 | + "task": "创建 CTO Agent。要求：id=novelai:cto, type=cxo, 汇报对象=novelai:ceo, 部门=技术部, 核心职责=技术架构设计、Agent创建与管理、系统设计、技术团队管理。岗位设计文档已存在于 data/companies/novelai/workspace/job_cto.md", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn810o7l", | |
| 10 | + "createdAt": "2026-03-26T22:10:45.709Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn816iew.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn816iew", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "evolver", | |
| 5 | + "task": "【FDE Phase 3 - data-analyst 技能进化训练】<br><br>## 输入资料<br>- 知识体系: data/workspace/data-analyst/digest/knowhow.md<br>- 评分标准: data/workspace/data-analyst/digest/benchmark.md (pass_score = 90)<br>- 训练集: data/workspace/data-analyst/digest/train.json<br>- 测试集: data/workspace/data-analyst/digest/test.json<br>- 初始 SKILL: data/workspace/data-analyst/digest/SKILL.md<br><br>## 训练要求<br>1. 以 SKILL.md 为起点，继续迭代优化<br>2. 硬性及格线: 总分 >= 90 (100分制)，低于此分数不能标记为完成<br>3. 最大迭代次数: 10 轮<br>4. 每轮基于测试集评分，分析弱维度，针对性修改 Skill 定义<br><br>## 产出要求<br>- 产出目录: data/workspace/data-analyst/evolve/<br>- 文件1: skill_data_analyst.md (迭代后的技能定义)<br>- 文件2: evolution_log.json (训练记录，每轮评分和修改说明)<br><br>## 部署要求（训练达标后必须执行）<br>1. 将最终 SKILL.md 复制到 data/companies/abc/agents/data-analyst/.claude/skills/data-analysis/SKILL.md<br>2. 更新 data/companies/abc/agents/data-analyst/.claude/skills/skills.md 添加技能清单<br>3. 更新 data/companies/abc/agents/data-analyst/config.json 中 status 为 ready<br><br>完成训练后，汇报最终评分和部署状态。", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn816iew", | |
| 10 | + "createdAt": "2026-03-26T22:15:17.916Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn8184nf.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn8184nf", | |
| 3 | + "from": "abc:coo", | |
| 4 | + "to": "abc:publisher", | |
| 5 | + "task": "[Pipeline第4步] 剧本整理与格式化。将 workspace/scripts/script_collection.json 中的10个剧本按照生产端格式要求进行整理。每个剧本需包含：1)标题 2)分类 3)剧情概要(100字) 4)人物列表 5)分场景剧本(完整对白)。输出JSON格式，写入 workspace/scripts/formatted_scripts.json", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": "G-001", | |
| 8 | + "nodeId": "G-001.4", | |
| 9 | + "executionConvId": "task-T-mn8184nf", | |
| 10 | + "createdAt": "2026-03-26T22:16:32.155Z", | |
| 11 | + "status": "done", | |
| 12 | + "completedAt": "2026-03-26T23:32:11.498Z", | |
| 13 | + "summary": "已完成10个剧本的整理与格式化。输出文件: workspace/scripts/formatted_scripts.json，包含标题、分类、剧情概要(100字)、人物列表、分场景剧本(完整对白)。" | |
| 14 | +} | |
| 0 | 15 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn81b15p.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn81b15p", | |
| 3 | + "from": "novelai:ceo", | |
| 4 | + "to": "agent-creator", | |
| 5 | + "task": "创建 COO Agent。要求：id=novelai:coo, type=cxo, 汇报对象=novelai:ceo, 部门=运营部, 核心职责=运营策略制定、生产运营管理、数据分析、质量与合规管理、团队管理。岗位设计文档已存在于 data/companies/novelai/workspace/job_coo.md", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn81b15p", | |
| 10 | + "createdAt": "2026-03-26T22:18:48.708Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn81bf7p.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn81bf7p", | |
| 3 | + "from": "abc:coo", | |
| 4 | + "to": "abc:researcher", | |
| 5 | + "task": "【深度研究任务】请对以下10个选题进行深度研究，生成研究报告。每个选题需要包含：1)背景调查 2)人物形象 3)剧情大纲 4)核心台词。选题数据：data/companies/abc/workspace/selected_topics.json", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": "G-001", | |
| 8 | + "nodeId": "G-001.2", | |
| 9 | + "executionConvId": "task-T-mn81bf7p", | |
| 10 | + "createdAt": "2026-03-26T22:19:10.018Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn81l94h.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn81l94h", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "digester", | |
| 5 | + "task": "【DIGEST 知识消化任务】为 ABC 公司 researcher（深度研究员）构建知识体系。<br><br>## 目标 Agent<br>- ID: researcher<br>- 角色: 深度研究员<br>- 核心能力: 接收 topic-selector 提供的选题，进行背景调查、人物形象塑造，生成对话台词和剧本内容<br><br>## 输入 (来自 Hunt 产出)<br>- 原始调研数据: `../../workspace/innovator/abc-researcher/knowhow_raw.md`<br>- 爬取配置: `../../workspace/innovator/abc-researcher/crawl_config.json`<br>- 种子数据: `../../workspace/innovator/abc-researcher/seed_dataset.json`<br><br>## DIGEST 任务要求<br>1. 整合 Hunt 产出，生成结构化 `knowhow.md`（含技能定义、方法论、输出格式）<br>2. 创建 `benchmark.md`（评分标准、维度、权重、pass_score=9.0）<br>3. 生成训练数据集 `training_dataset.json` 和 `test_dataset.json`<br>4. 提取 `industry_avg_score`（行业基准分数）<br><br>## 输出目录<br>`../../workspace/innovator/abc-researcher/`<br><br>## 输出文件<br>1. `knowhow.md` - 知识体系定义<br>2. `benchmark.md` - 评分标准<br>3. `training_dataset.json` - 训练数据集<br>4. `test_dataset.json` - 测试数据集<br><br>## 验收标准<br>- knowhow.md 包含 3+ 研究维度，涵盖背景调查/人物塑造/剧本创作<br>- benchmark.md 包含 5+ 评分维度，pass_score = 9.0 (90分)<br>- 训练/测试数据集各 10+ 条数据", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": "G-001", | |
| 8 | + "nodeId": "G-001.2.2", | |
| 9 | + "executionConvId": "task-T-mn81l94h", | |
| 10 | + "createdAt": "2026-03-26T22:26:47.724Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn81megg.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn81megg", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "digester", | |
| 5 | + "task": "【Digest任务 - topic-selector技能消化】 请基于 Hunter 产出的原始调研资料，构建结构化知识体系。输入: workspace/hunter/topic-selector/knowhow_raw.md 和 crawl_config.json。输出目录: workspace/digester/topic-selector/。产出要求: 1) knowhow.md - 结构化知识体系（筛选方法论、评分维度、数据来源） 2) benchmark.md - 评分标准（至少3个维度，pass_score=9.0） 3) training_dataset.json - 训练数据集（20+条）", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn81megg", | |
| 10 | + "createdAt": "2026-03-26T22:27:38.829Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn81t2pw.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn81t2pw", | |
| 3 | + "from": "novelai:ceo", | |
| 4 | + "to": "agent-creator", | |
| 5 | + "task": "创建 CTO Agent (技术总监)。要求：id=novelai:cto, type=cxo, 能力描述=技术架构设计、Agent系统设计、热点采集系统、选题大纲系统、正文写作系统、AI审核系统、发布管理系统设计与实现, 性格特征=技术前瞻、严谨务实、数据驱动、追求高质量", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": "G-002", | |
| 8 | + "nodeId": "G-002.1.1", | |
| 9 | + "executionConvId": "task-T-mn81t2pw", | |
| 10 | + "createdAt": "2026-03-26T22:32:49.421Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn81zi53.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn81zi53", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "digester", | |
| 5 | + "task": "## Phase 2: DIGEST 知识消化<br><br>## 输入（Hunt 产出）<br>- 知识素材: data/workspace/hunter/researcher/knowhow_raw.md<br>- 爬取配置: data/workspace/hunter/researcher/crawl_config.json<br>- 种子数据: data/workspace/hunter/researcher/seed_data.json<br><br>## 输出要求<br>创建以下文件到 data/workspace/digester/researcher/:<br>1. **knowhow.md** - 结构化知识体系（研究方法论、剧本创作方法论、深度研究能力要求）<br>2. **benchmark.md** - 评分标准（输入验证、输出验证、能力维度、pass_score=9.0）<br>3. **train_dataset.json** - 训练数据集（10-20条标注数据）<br>4. **test_dataset.json** - 测试数据集（5条标注数据）<br><br>## 验收标准<br>- knowhow.md 包含 5+ 研究维度 ✓<br>- knowhow.md 包含剧本创作方法论（三幕式结构、人物弧光、场景构建）✓<br>- benchmark.md 评分标准可执行 ✓<br>- train_dataset.json 至少 10 条 ✓", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn81zi53", | |
| 10 | + "createdAt": "2026-03-26T22:37:50.299Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn825pbe.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn825pbe", | |
| 3 | + "from": "abc:coo", | |
| 4 | + "to": "abc:screenwriter", | |
| 5 | + "task": "[Pipeline第4步] 剧本整理。将 workspace/research_output.json 中的深度研究内容整理为完整的短剧剧本。需包含：1)剧名；2)剧情简介(100字)；3)分集大纲(每集3-5幕)；4)完整台词(每集至少2000字)；5)人物卡。选取其中1-2个最具潜力的选题进行深度创作。输出格式：JSON，写入 workspace/screenplay_output.json", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": "G-001", | |
| 8 | + "nodeId": "G-001.4", | |
| 9 | + "executionConvId": "task-T-mn825pbe", | |
| 10 | + "createdAt": "2026-03-26T22:42:41.429Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn82bigk.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn82bigk", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "digester", | |
| 5 | + "task": "【DIGEST任务 - researcher深度研究技能】<br><br>输入路径：<br>- 调研产出: ../../workspace/hunter/researcher/knowhow_raw.md<br>- 爬取配置: ../../workspace/hunter/researcher/crawl_config.json<br>- 种子数据: ../../workspace/hunter/researcher/seed_data.json<br><br>输出路径：../../workspace/digester/researcher/<br><br>任务描述：<br>将 Hunter 的原始调研产出进行结构化消化，生成：<br>1. knowhow.md - 结构化知识体系（研究方法论、剧本创作流程、数据源清单）<br>2. benchmark.md - 评测标准（输入输出格式、能力维度、评分规则、pass_score=90）<br>3. train_dataset.json - 训练数据集（30+条精选样本，含输入输出对）<br><br>验收标准：<br>- knowhow.md 包含 3+ 研究维度、剧本创作方法论<br>- benchmark.md 定义清晰的评分标准和 pass_score=90<br>- train_dataset.json 提供可直接用于训练的样本", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn82bigk", | |
| 10 | + "createdAt": "2026-03-26T22:47:13.232Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn82p167.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn82p167", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "digester", | |
| 5 | + "task": "【Digest任务 - trend-hunter技能消化】 请基于 Hunter 产出的原始调研资料，构建结构化知识体系。输入: workspace/hunter/trend-hunter/knowhow_raw.md 和 crawl_config.json。输出目录: workspace/digester/trend-hunter/。产出要求: 1) knowhow.md - 结构化知识体系（抓取方法论、数据源分类、热点分类） 2) benchmark.md - 评分标准（至少3个维度，pass_score=9.0） 3) training_dataset.json - 训练数据集（20+条）", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn82p167", | |
| 10 | + "createdAt": "2026-03-26T22:57:41.140Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn82q147.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn82q147", | |
| 3 | + "from": "innovator", | |
| 4 | + "to": "evolver", | |
| 5 | + "task": "训练 data-analyst 技能到90分以上。<br><br>## 背景<br>- targetAgentId: data-analyst<br>- skillName: data_analysis<br>- 已在 Phase 1 (Hunt) 和 Phase 2 (Digest) 完成知识构建<br><br>## 输入路径<br>- 知识手册: data/workspace/data-analyst/digest/knowhow.md<br>- 评分标准: data/workspace/data-analyst/digest/benchmark.md<br>- 训练集: data/workspace/data-analyst/digest/train.json<br>- 测试集: data/workspace/data-analyst/digest/test.json<br>- 行业基准: data/workspace/data-analyst/digest/industry_avg.json (82分)<br><br>## 目标<br>- 使用 Evolver 的进化循环将技能评分优化到 90 分以上<br>- 训练完成后部署到 data/companies/abc/agents/data-analyst/<br><br>## 输出路径<br>- 技能定义: data/workspace/data-analyst/evolve/skill_data_analyst.md<br>- 进化日志: data/workspace/data-analyst/evolve/evolution_log.json<br><br>## 要求<br>1. 以 benchmark.md 中的 4 个维度进行评分：数据准确性(30%)、分析深度(25%)、报告质量(25%)、工具使用(20%)<br>2. 最大迭代 10 轮<br>3. 每轮分析弱维度，针对性修改 SKILL 定义<br>4. 及格线: 总分 >= 90 (100分制)<br><br>完成后汇报最终评分和迭代次数。", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn82q147", | |
| 10 | + "createdAt": "2026-03-26T22:58:28.241Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn82zg2h.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn82zg2h", | |
| 3 | + "from": "novelai:ceo", | |
| 4 | + "to": "agent-creator", | |
| 5 | + "task": "创建 COO Agent (运营总监)。要求：id=novelai:coo, type=cxo, 能力描述=运营策略制定、生产运营管理、数据分析与决策、质量与合规管理、团队管理, 性格特征=强执行力、数据驱动、跨部门协调、风险意识", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": "G-002", | |
| 8 | + "nodeId": "G-002.1.2", | |
| 9 | + "executionConvId": "task-T-mn82zg2h", | |
| 10 | + "createdAt": "2026-03-26T23:05:49.551Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn838t5m.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn838t5m", | |
| 3 | + "from": "abc:coo", | |
| 4 | + "to": "abc:publisher", | |
| 5 | + "task": "[Pipeline第5步] 内容发布。将 workspace/scripts/script_collection.json 中的剧本按固定格式上传至生产端。确保剧本格式符合生产端要求。输出：确认发布成功的状态报告，写入 workspace/publish_report.json", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": "G-001", | |
| 8 | + "nodeId": "G-001.5", | |
| 9 | + "executionConvId": "task-T-mn838t5m", | |
| 10 | + "createdAt": "2026-03-26T23:13:06.557Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn83eb94.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn83eb94", | |
| 3 | + "from": "abc:coo", | |
| 4 | + "to": "abc:screenwriter", | |
| 5 | + "task": "[Pipeline第4步] 剧本整理。将 workspace/research_output.json 中的10个话题整理为可直接拍摄的剧本格式，每个话题需产出：1)完整的分镜脚本；2)台词最终版；3)场景描述；4)拍摄建议。输出格式：JSON数组，写入 workspace/screenplay_output.json", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": "G-001", | |
| 8 | + "nodeId": "G-001.1.4", | |
| 9 | + "executionConvId": "task-T-mn83eb94", | |
| 10 | + "createdAt": "2026-03-26T23:17:21.503Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn83ibg8.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn83ibg8", | |
| 3 | + "from": "abc:coo", | |
| 4 | + "to": "abc:data-analyst", | |
| 5 | + "task": "[Pipeline第6步] 数据分析。监控生产端数据（市场热度、玩家反馈），评估选题受欢迎程度。读取已发布的剧本数据，分析表现并生成迭代建议。输出报告写入 workspace/analytics_report.json", | |
| 6 | + "priority": "P2", | |
| 7 | + "goalId": "G-001", | |
| 8 | + "nodeId": "G-001.6", | |
| 9 | + "executionConvId": "task-T-mn83ibg8", | |
| 10 | + "createdAt": "2026-03-26T23:20:26.771Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn83n6pq.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn83n6pq", | |
| 3 | + "from": "master", | |
| 4 | + "to": "novelai:ceo", | |
| 5 | + "task": "紧急催促：你的 todo 中有 4 个未开始的任务 (T-mn7pw42p, T-mn7ytrcu, T-mn7zozvp)。workspace 中已有 job_cto.md 和 job_coo.md。请立即：1) 通过 agent-creator 创建 novelai:cto；2) 通过 agent-creator 创建 novelai:coo。完成后汇报进度。", | |
| 6 | + "priority": "P0", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn83n6pq", | |
| 10 | + "createdAt": "2026-03-26T23:24:13.895Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |
data/agents/.tasks/T-mn841ile.json
0 → 100644
| 1 | +{ | |
| 2 | + "taskId": "T-mn841ile", | |
| 3 | + "from": "abc:coo", | |
| 4 | + "to": "abc:publisher", | |
| 5 | + "task": "[Pipeline第5步] 内容发布。将 workspace/scripts/formatted_scripts.json 或 workspace/scripts/script_collection.json 中的剧本按生产端格式上传。上传完成后报告上传数量和状态。", | |
| 6 | + "priority": "P1", | |
| 7 | + "goalId": null, | |
| 8 | + "nodeId": null, | |
| 9 | + "executionConvId": "task-T-mn841ile", | |
| 10 | + "createdAt": "2026-03-26T23:35:23.595Z", | |
| 11 | + "status": "dispatched" | |
| 12 | +} | |
| 0 | 13 | \ No newline at end of file | ... | ... |