Phase 0-2: Schema cleanup, typed relations, event-driven automation
- Phase 0: AGENTS.md cleanup (dedup quotes, renumber sections, merge qmd) - Phase 1: typed relations (manage-relations.py, graph-search.py, check-staleness.py, detect-conflicts.py) - Phase 2: frontmatter validator, weekly lint, knowledge promotion, git hooks - Fix .gitignore to track tools/ and .githooks/ - Fix git remote URL (remove plaintext token) - New wiki pages: 504 pages, 34 raw sources
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{
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"_status": "已废弃 - 迁移至 WorkBuddy TOML 自动化",
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"_superseded_date": "2026-04-20",
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"_new_config_doc": "高等教育AI专题-自动化工作流配置v3.md",
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"_new_toml_dir": ".workbuddy/automations/",
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"_note": "旧版JSON配置已迁移,WorkBuddy将自动加载 .toml 文件",
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"project_name": "教育AI研究",
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"project_path": "D:\\TC_UP\\2023card\\projects\\openclaw\\教育AI研究",
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"created_date": "2026-03-31",
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"automations": [
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{
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"id": "edu-ai-weekly-start",
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"name": "教育AI-周一研究启动",
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"description": "每周一启动新一周的教育AI研究,生成研究计划",
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"schedule_type": "recurring",
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"rrule": "FREQ=WEEKLY;BYDAY=MO;BYHOUR=9;BYMINUTE=0",
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"status": "ACTIVE",
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"cwds": [
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"D:\\TC_UP\\2023card"
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],
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"prompt": "启动本周'AI在教育领域应用'专题研究:\n\n1. 使用GLM Coding Plan生成本周研究计划\n2. 研究主题按月度轮换:\n - 第1周:个性化学习系统\n - 第2周:智能评测技术\n - 第3周:教育机器人应用\n - 第4周:产业动态与政策\n\n3. 输出文件命名:projects/openclaw/教育AI研究/每周报告/{year}-W{week}-研究计划.md\n4. 包含以下内容:\n - 本周研究目标(具体可衡量)\n - 详细时间安排(每日任务)\n - 信息源推荐(知网、万方、Arxiv等)\n - 风险评估与应对\n - 产出物清单",
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"output_template": "projects/openclaw/教育AI研究/每周报告/{year}-W{week}-研究计划.md",
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"model_preference": "glm-coding",
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"estimated_tokens": 5000,
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"estimated_duration": "30分钟"
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},
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{
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"id": "edu-ai-weekly-report",
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"name": "教育AI-周五报告生成",
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"description": "每周五生成教育AI研究周报,整合本周发现",
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"schedule_type": "recurring",
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"rrule": "FREQ=WEEKLY;BYDAY=FR;BYHOUR=18;BYMINUTE=0",
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"status": "ACTIVE",
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"cwds": [
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"D:\\TC_UP\\2023card"
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],
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"prompt": "生成教育AI研究周报:\n\n输入数据:本周收集的文献、机构、技术进展信息\n使用模板:projects/openclaw/教育AI研究/templates/04-周报生成.md\n\n报告结构要求:\n1. 执行摘要(本周研究概述、关键指标)\n2. 详细研究发现(文献、技术、机构、产业)\n3. 深度分析(趋势预测、挑战机遇)\n4. 知识库更新统计\n5. 下周研究建议\n\n质量要求:\n- 数据准确,有可靠来源\n- 分析深入,有逻辑依据\n- 建议具体,可操作执行\n- 格式规范,符合模板",
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"output_template": "projects/openclaw/教育AI研究/每周报告/{year}-W{week}-研究报告.md",
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"model_preference": "glm-coding",
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"estimated_tokens": 8000,
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"estimated_duration": "45分钟"
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},
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{
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"id": "edu-ai-monthly-review",
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"name": "教育AI-月度研究回顾",
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"description": "每月末进行教育AI研究回顾和优化",
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"schedule_type": "recurring",
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"rrule": "FREQ=MONTHLY;BYMONTHDAY=-1;BYHOUR=20;BYMINUTE=0",
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"status": "ACTIVE",
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"cwds": [
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"D:\\TC_UP\\2023card"
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],
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"prompt": "进行教育AI研究月度回顾:\n\n回顾周期:过去一个月(4周)\n回顾内容:\n1. 研究产出统计(文献、卡片、报告数量)\n2. 质量评估(准确性、完整性、时效性)\n3. 成本分析(API使用量、费用统计)\n4. 效率评估(研究耗时、产出密度)\n5. 问题识别与改进建议\n\n输出要求:\n1. 月度研究报告(详细分析)\n2. 质量改进计划(具体措施)\n3. 下月研究优化建议(调整方案)\n4. 模板和流程更新建议",
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"output_template": "projects/openclaw/教育AI研究/每月回顾/{year}-{month}-回顾报告.md",
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"model_preference": "glm-coding",
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"estimated_tokens": 6000,
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"estimated_duration": "40分钟"
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},
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{
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"id": "edu-ai-knowledge-sync",
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"name": "教育AI-知识库同步",
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"description": "每周日同步更新知识库,确保信息一致性",
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"schedule_type": "recurring",
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"rrule": "FREQ=WEEKLY;BYDAY=SU;BYHOUR=22;BYMINUTE=0",
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"status": "ACTIVE",
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"cwds": [
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"D:\\TC_UP\\2023card"
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],
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"prompt": "同步教育AI研究知识库:\n\n同步任务:\n1. 检查知识卡片一致性(概念定义、关联关系)\n2. 更新机构档案信息(最新动态、研究成果)\n3. 整合本周新增文献到文献库\n4. 验证跨文档引用和链接\n5. 生成知识库健康报告\n\n输出要求:\n1. 同步完成确认报告\n2. 发现问题列表(如有)\n3. 知识库统计更新\n4. 维护建议",
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"output_template": "projects/openclaw/教育AI研究/知识库同步/{year}-W{week}-同步报告.md",
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"model_preference": "deepseek",
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"estimated_tokens": 4000,
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"estimated_duration": "25分钟"
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}
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],
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"cost_estimation": {
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"total_tokens_per_month": 74000,
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"glm_estimated_cost": 222.0,
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"deepseek_estimated_cost": 32.0,
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"total_estimated_cost": 254.0,
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"estimated_hours_per_month": 60
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}
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}
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