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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hehaiguang1123
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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教育AI研究项目自动化配置脚本
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用于配置WorkBuddy自动化任务
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"""
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import sys
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import json
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import datetime
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from pathlib import Path
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def create_automation_config():
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"""创建自动化任务配置"""
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# 获取当前日期和下周日期
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today = datetime.date.today()
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next_monday = today + datetime.timedelta(days=(7 - today.weekday()))
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# 基础配置
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base_config = {
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"project_name": "教育AI研究",
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"project_path": str(Path("D:/TC_UP/2023card/projects/教育AI研究").resolve()),
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"created_date": today.isoformat(),
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"automations": []
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}
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# 自动化任务配置
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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": [str(Path("D:/TC_UP/2023card").resolve())],
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"prompt": """启动本周'AI在教育领域应用'专题研究：
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1. 使用GLM Coding Plan生成本周研究计划
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2. 研究主题按月度轮换：
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- 第1周：个性化学习系统
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- 第2周：智能评测技术
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- 第3周：教育机器人应用
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- 第4周：产业动态与政策
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3. 输出文件命名：projects/教育AI研究/每周报告/{year}-W{week}-研究计划.md
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4. 包含以下内容：
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- 本周研究目标（具体可衡量）
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- 详细时间安排（每日任务）
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- 信息源推荐（知网、万方、Arxiv等）
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- 风险评估与应对
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- 产出物清单""",
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"output_template": "projects/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": [str(Path("D:/TC_UP/2023card").resolve())],
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"prompt": """生成教育AI研究周报：
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输入数据：本周收集的文献、机构、技术进展信息
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使用模板：projects/教育AI研究/templates/04-周报生成.md
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报告结构要求：
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1. 执行摘要（本周研究概述、关键指标）
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2. 详细研究发现（文献、技术、机构、产业）
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3. 深度分析（趋势预测、挑战机遇）
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4. 知识库更新统计
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5. 下周研究建议
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质量要求：
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- 数据准确，有可靠来源
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- 分析深入，有逻辑依据
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- 建议具体，可操作执行
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- 格式规范，符合模板""",
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"output_template": "projects/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": [str(Path("D:/TC_UP/2023card").resolve())],
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"prompt": """进行教育AI研究月度回顾：
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回顾周期：过去一个月（4周）
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回顾内容：
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1. 研究产出统计（文献、卡片、报告数量）
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2. 质量评估（准确性、完整性、时效性）
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3. 成本分析（API使用量、费用统计）
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4. 效率评估（研究耗时、产出密度）
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5. 问题识别与改进建议
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输出要求：
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1. 月度研究报告（详细分析）
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2. 质量改进计划（具体措施）
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3. 下月研究优化建议（调整方案）
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4. 模板和流程更新建议""",
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"output_template": "projects/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": [str(Path("D:/TC_UP/2023card").resolve())],
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"prompt": """同步教育AI研究知识库：
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同步任务：
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1. 检查知识卡片一致性（概念定义、关联关系）
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2. 更新机构档案信息（最新动态、研究成果）
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3. 整合本周新增文献到文献库
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4. 验证跨文档引用和链接
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5. 生成知识库健康报告
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输出要求：
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1. 同步完成确认报告
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2. 发现问题列表（如有）
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3. 知识库统计更新
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4. 维护建议""",
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"output_template": "projects/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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base_config["automations"] = automations
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# 计算预计总成本
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total_tokens_per_month = sum([
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a["estimated_tokens"] * 4 for a in automations if a["id"] != "edu-ai-monthly-review"
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]) + automations[2]["estimated_tokens"] # 月度回顾
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# GLM成本估算（假设0.003元/token）
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glm_cost = total_tokens_per_month * 0.003
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# DeepSeek成本估算（假设0.002元/token）
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deepseek_cost = 4000 * 4 * 0.002 # 知识库同步任务
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base_config["cost_estimation"] = {
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"total_tokens_per_month": total_tokens_per_month,
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"glm_estimated_cost": round(glm_cost, 2),
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"deepseek_estimated_cost": round(deepseek_cost, 2),
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"total_estimated_cost": round(glm_cost + deepseek_cost, 2),
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"estimated_hours_per_month": 15 * 4 # 15小时/周 * 4周
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}
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return base_config
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def generate_workbuddy_commands(config):
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"""生成WorkBuddy自动化配置命令"""
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commands = []
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commands.append("# WorkBuddy自动化配置命令")
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commands.append("# 教育AI研究项目")
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commands.append(f"# 生成时间：{datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
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commands.append("")
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for automation in config["automations"]:
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commands.append(f"## {automation['name']} ({automation['id']})")
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commands.append(f"# 描述：{automation['description']}")
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commands.append(f"# 计划：{automation['rrule']}")
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commands.append(f"# 状态：{automation['status']}")
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commands.append(f"# 模型偏好：{automation['model_preference']}")
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commands.append("")
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# 生成automation_update命令
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cmd = f'automation_update(mode="suggested create", '
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cmd += f'name="{automation["name"]}", '
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cmd += f'prompt="{automation["prompt"]}", '
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cmd += f'cwds="{",".join(automation["cwds"])}", '
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cmd += f'status="{automation["status"]}", '
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cmd += f'scheduleType="{automation["schedule_type"]}", '
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cmd += f'rrule="{automation["rrule"]}")'
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commands.append(cmd)
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commands.append("")
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# 添加成本信息
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commands.append("## 成本估算")
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cost = config["cost_estimation"]
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commands.append(f"月度总tokens：{cost['total_tokens_per_month']:,}")
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commands.append(f"GLM估算成本：¥{cost['glm_estimated_cost']}")
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commands.append(f"DeepSeek估算成本：¥{cost['deepseek_estimated_cost']}")
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commands.append(f"总计估算成本：¥{cost['total_estimated_cost']}")
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commands.append(f"月度预计耗时：{cost['estimated_hours_per_month']}小时")
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commands.append("")
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return "\n".join(commands)
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def generate_readme_summary(config):
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"""生成README摘要"""
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summary = []
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summary.append("# 自动化工作流配置")
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summary.append("")
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summary.append("## 已配置的自动化任务")
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summary.append("")
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for automation in config["automations"]:
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summary.append(f"### {automation['name']}")
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summary.append(f"- **ID**：`{automation['id']}`")
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summary.append(f"- **计划**：{automation['rrule']}")
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summary.append(f"- **状态**：{automation['status']}")
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summary.append(f"- **模型**：{automation['model_preference']}")
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summary.append(f"- **输出**：{automation['output_template']}")
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summary.append("")
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summary.append("## 使用说明")
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summary.append("")
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summary.append("### 手动启动任务")
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summary.append("```bash")
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summary.append("# 查看所有自动化任务")
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summary.append("python -c \"import sqlite3; conn=sqlite3.connect(r'C:\\Users\\hhhh2024\\AppData\\Roaming\\WorkBuddy\\automations\\automations.db'); cursor=conn.cursor(); cursor.execute('SELECT id, name, status FROM automations'); print(cursor.fetchall())\"")
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summary.append("")
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summary.append("# 手动触发任务")
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summary.append("# 使用WorkBuddy的automation_update工具")
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summary.append("```")
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summary.append("")
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summary.append("### 监控与维护")
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summary.append("1. **成本监控**：定期检查API使用量和费用")
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summary.append("2. **质量检查**：每周审核产出物质量")
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summary.append("3. **流程优化**：每月回顾并优化工作流程")
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summary.append("4. **备份管理**：定期备份自动化配置和产出物")
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return "\n".join(summary)
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def main():
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"""主函数"""
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print("正在生成教育AI研究项目自动化配置...")
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# 创建配置
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config = create_automation_config()
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# 生成配置文件
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config_file = Path(__file__).parent.parent / "automation_config.json"
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with open(config_file, 'w', encoding='utf-8') as f:
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json.dump(config, f, ensure_ascii=False, indent=2)
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print(f"✓ 配置文件已生成：{config_file}")
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# 生成WorkBuddy命令
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commands = generate_workbuddy_commands(config)
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commands_file = Path(__file__).parent.parent / "workbuddy_commands.txt"
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with open(commands_file, 'w', encoding='utf-8') as f:
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f.write(commands)
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print(f"✓ WorkBuddy命令已生成：{commands_file}")
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# 生成README摘要
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summary = generate_readme_summary(config)
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summary_file = Path(__file__).parent.parent / "AUTOMATION_README.md"
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with open(summary_file, 'w', encoding='utf-8') as f:
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f.write(summary)
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print(f"✓ 自动化README已生成：{summary_file}")
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# 输出关键信息
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print("\n" + "="*60)
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print("自动化配置摘要")
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print("="*60)
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print(f"项目路径：{config['project_path']}")
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print(f"配置时间：{config['created_date']}")
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print(f"自动化任务数量：{len(config['automations'])}")
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print(f"月度估算成本：¥{config['cost_estimation']['total_estimated_cost']}")
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print(f"月度估算耗时：{config['cost_estimation']['estimated_hours_per_month']}小时")
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print("="*60)
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print("\n下一步操作：")
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print("1. 查看 workbuddy_commands.txt 文件中的自动化配置命令")
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print("2. 在WorkBuddy中执行这些命令来创建自动化任务")
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print("3. 定期检查 AUTOMATION_README.md 了解自动化任务状态")
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print("4. 监控 automation_config.json 中的成本估算")
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if __name__ == "__main__":
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main()
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Block a user