--- name: codex-collaboration-8a4a17c5b401 description: 管理与Codex的协作流程，包括深度分析、复杂逻辑设计、代码审查等。当任务需要深度推理、>10行核心逻辑设计或质量审查时使用。 --- # Codex Collaboration Skill ## 🚀 功能概述 本 skill 封装了与 Codex 协作的完整流程和规范，让主AI能够： - 进行深度分析和复杂推理 - 设计复杂逻辑（>10行核心逻辑） - 执行代码质量审查和风险评估 - 收集和管理上下文信息 - 自动化执行协作流程 ## 🤝 审查协作规范 | instruction | notes | |--------------------------------------------------------------------------------------------------|---------------------------------| | 主AI职责 - 定义审查清单,明确审查关注点、检查项、评分标准和决策规则 | 一次性传递完整审查要求 | | Codex职责 - 执行深度审查分析,使用sequential-thinking进行推理,输出评分、建议和论据 | 充分利用Codex推理能力 | | 审查清单必须包含:需求字段完整性(目标、范围、交付物、审查要点)、覆盖原始意图无遗漏或歧义、交付物映射明确(代码、文档、测试、验证报告)、依赖与风险评估完毕、审查结论已留痕(含时间戳与责任人) | 缺项时主AI需补充 | | Codex审查输出:技术维度评分(代码质量、测试覆盖、规范遵循)、战略维度评分(需求匹配、架构一致、风险评估)、综合评分(0-100)、明确建议(通过/退回/需讨论)、支持论据和关键发现 | 完整结构化输出 | | 主AI调用Codex MCP执行审查,传递完整审查清单与验收标准 | 审查执行沿用 `mcp__codex__codex` 调用格式 | | Codex生成 `.claude/review-report.md` 审查报告,包含:元数据、评分详情(技术+战略+综合)、明确建议、核对结果、风险与阻塞、留痕文件、反馈通道 | 结果可追溯 | | 主AI决策规则:综合评分≥90分且Codex建议"通过"→直接确认通过;综合评分<80分且Codex建议"退回"→直接确认退回;其他情况(80-89分或建议"需讨论")→仔细审阅报告后决策 | 信任明确案例,聚焦边界案例 | | 主AI保留随时推翻Codex建议的权力,但需记录推翻原因用于优化Codex审查标准 | 最终决策权在主AI | ## 👥 职责分离详细规范 ### 主AI（Claude Code）- 规划者 + 执行者 - ✅ 任务规划和拆解（使用 TodoWrite） - ✅ **直接代码编写**（使用 Read/Edit/Write，无 MCP 延迟） - ✅ 简单逻辑实现（CRUD、格式化等，<10 行核心逻辑） - ✅ 最终决策确认（基于 Codex 建议） - ✅ 决策记录留痕（operations-log.md） - ✅ 使用 sequential-thinking 进行任务规划推理 ### Codex（支持AI）- 分析者 + 审查者 - ✅ **深度推理分析**（使用 sequential-thinking，这是核心优势） - ✅ **全面代码检索**（充分时间进行代码库扫描） - ✅ 复杂逻辑设计（算法、架构决策，>10 行核心逻辑） - ✅ 上下文收集和分析（输出到 `.claude/context-*.json`） - ✅ 质量审查评分（代码审查、风险识别） - ✅ 技术建议输出（非决策，是输入） ### 关键边界规则 1. **强制前置**：主AI 编码前必须有 Codex 的分析报告 2. **复杂度阈值**：>10 行核心逻辑必须委托 Codex 设计 3. **质量闸门**：所有代码必须经 Codex 审查 4. **决策留痕**：主AI 可推翻 Codex 建议，但必须记录原因 ## 🔄 完整协作流程 ### 阶段0：需求理解与上下文收集 - 快速通道：简单任务（<30字，单一目标）直接进入上下文收集 - 复杂任务：先结构化需求（生成 `.claude/structured-request.json`） - 上下文收集：Codex 执行结构化快速扫描 → 主AI 识别关键疑问 → Codex 针对性深挖（≤3次）→ 主AI 充分性检查 ### 阶段1：任务规划 - 主AI 使用 sequential-thinking 分析 Codex 提供的上下文 - 主AI 使用 TodoWrite 记录任务计划和验收标准 - 基于完整上下文定义接口规格、边界条件、性能要求、测试标准 ### 阶段2：代码执行 - **主AI 直接编码**（使用 Read/Edit/Write，无 MCP 延迟） - 简单逻辑（<10 行）：主AI 直接实现 - 复杂逻辑（>10 行）：调用 Codex 设计，主AI 实现 - 实时更新 `coding-progress.json` 状态 - 遇到问题时调用 Codex 分析 ### 阶段3：质量验证 - 主AI 定义审查清单与决策规则 - Codex 使用 sequential-thinking 深度审查并生成评分+建议（写入 `.claude/review-report.md`） - 主AI 基于建议快速决策： - ≥90分且建议"通过" → 直接确认通过 - <80分且建议"退回" → 直接确认退回 - 80-89分或建议"需讨论" → 仔细审阅后决策 ## 📁 文件管理和状态同步 ### 路径规范 所有任务执行产生的工作文件必须写入项目本地 `.claude/` 目录（而非全局 `~/.claude/`）： ``` /.claude/ ├── context-initial.json ← 初步收集（Codex 输出） ├── context-question-N.json ← 深度分析（Codex 输出） ├── coding-progress.json ← 实时编码状态（主AI 输出） ├── operations-log.md ← 决策记录（主AI 输出） ├── review-report.md ← 审查报告（Codex 输出） └── codex-sessions.json ← 会话管理（Codex 持久化） ``` ### 上下文同步和状态管理 **共享状态文件**（`.claude/` 目录）： - `context-*.json`：Codex 输出的分析上下文（主AI 只读） - `operations-log.md`：主AI 记录的决策和进度（Codex 可读） - `coding-progress.json`：实时编码状态（主AI 维护） ```json { "current_task_id": "task-123", "files_modified": ["src/foo.ts"], "last_update": "2025-10-03T10:30:00Z", "status": "coding|review_needed|completed", "pending_questions": ["如何处理边界情况X？"], "complexity_estimate": "simple|moderate|complex" } ``` **同步时序**： 1. Codex 分析完成 → 写入 `context-*.json` → 主AI 读取开始编码 2. 主AI 编码中 → 更新 `coding-progress.json` → Codex 可监控 3. 主AI 完成编码 → 标记 `review_needed` → 触发 Codex 审查 4. Codex 审查完 → 写入 `review-report.md` → 主AI 读取决策 ## 🔧 MCP 调用规范 ### 首次调用（创建新会话） - 工具：`mcp__codex__codex` - 必需参数：`prompt`（任务描述） - 推荐参数： - `model`："gpt-5-codex"（默认）/ "o3" / "o4-mini" - `sandbox`："danger-full-access"（默认）/ "read-only" / "workspace-write" - `approval-policy`："on-failure"（推荐）/ "untrusted" / "never" ### conversationId 获取方法 **职责分离**： - **主AI**：生成task_marker、调用Codex、记录conversationId - **Codex**：查询conversationId、持久化到文件、返回给主AI **主AI操作**： 1. 生成唯一task_marker：`[TASK_MARKER: YYYYMMDD-HHMMSS-XXXX]` 2. 将task_marker嵌入prompt开头（第一行） 3. 调用mcp__codex__codex 4. 从响应提取`[CONVERSATION_ID]: xxx` 5. 记录conversationId用于后续codex-reply调用 **Codex操作**（在prompt末尾要求）： 1. 提取task_marker（prompt第一行） 2. 查询conversationId：遍历`~/.codex/sessions`目录下最近5个会话文件，匹配task_marker 3. 持久化到`.claude/codex-sessions.json` 4. 在响应末尾附加：`[CONVERSATION_ID]: ` 5. 如果未找到：`[CONVERSATION_ID]: NOT_FOUND` **继续会话**（codex-reply）： - 使用之前记录的conversationId - 调用`mcp__codex__codex-reply(conversationId=xxx, prompt=yyy)` - 无需从响应提取conversationId **异常处理**： - NOT_FOUND：记录到operations-log.md，创建新会话 - 持久化失败：记录警告，不影响当前任务执行 - 主AI不得执行会话ID提取脚本，所有查询由Codex完成 ### 上下文收集前置原则 - 必须先通过 Codex 收集完整上下文并写入 `.claude/` 文件，再进行任务规划。 - 主AI读取上下文摘要，Codex 执行时读取完整上下文文件，避免信息经主AI转述损耗。 ## 🤖 自动化执行策略 **核心原则：默认自动执行，极少数例外才需确认** **绝对不需要确认的场景**（使用 `approval-policy="on-failure"` 或 `"never"`）： - ✅ 所有文件读写操作（在 sandbox="danger-full-access" 模式下） - ✅ 标准工具调用（code-index、exa、grep、find 等） - ✅ 按既定计划执行的所有任务步骤 - ✅ 代码编写、修改、重构 - ✅ 文档生成和更新 - ✅ 测试执行和验证脚本运行 - ✅ 依赖安装和包管理操作 - ✅ Git 操作（add、commit、diff、status 等，push 除外） - ✅ 构建和编译操作 - ✅ 任务规划和分解、上下文收集、深度思考推理、质量验证审查 - ✅ 错误修复和重试（最多3次） - ✅ 调用 mcp__codex__codex 或 codex-reply **极少数例外情况**（仅这些才需要 Codex 主动请求确认）： - ⚠️ 删除核心配置文件（package.json、tsconfig.json、.env 等） - ⚠️ 数据库 schema 的破坏性变更（DROP TABLE、ALTER COLUMN 等） - ⚠️ Git push 到远程仓库（特别是 main/master 分支） - ⚠️ 连续3次相同错误后需要策略调整 - ⚠️ 用户明确要求确认的操作 **主AI职责边界**： - 规划阶段：需要确认技术方案和架构选型 - 执行阶段：完全自动化，Codex 自主决策实现细节 - 验证阶段：自动评分，仅边界案例（80-89分）才人工决策 ## 📋 渐进式上下文收集流程 ### 核心哲学 - **问题驱动**：基于关键疑问收集，而非机械执行固定流程 - **充分性优先**：追求"足以支撑决策和规划"，而非"信息100%完整" - **动态调整**：根据实际需要决定深挖次数（建议≤3次），避免过度收集 - **成本意识**：每次深挖都要明确"为什么需要"和"解决什么疑问" ### 步骤1：结构化快速扫描（必须） 通过 Codex 进行框架式收集，输出到 `.claude/context-initial.json`： - 位置：功能在哪个模块/文件？ - 现状：现在如何实现？找到1-2个相似案例 - 技术栈：使用的框架、语言、关键依赖 - 测试：现有测试文件和验证方式 - **观察报告**：Codex 作为专家，报告发现的异常、信息不足之处和建议深入的方向 ### 步骤2：识别关键疑问（必须） 主AI使用 sequential-thinking 分析初步收集和观察报告，识别关键疑问： - 我理解了什么？（已知） - 还有哪些疑问影响规划？（未知） - 这些疑问的优先级如何？（高/中/低） - 输出：优先级排序的疑问列表 ### 步骤3：针对性深挖（按需，建议≤3次） 仅针对高优先级疑问，通过 Codex 深挖： - 聚焦单个疑问，不发散 - 提供代码片段证据，而非猜测 - 输出到 `.claude/context-question-N.json` - **成本提醒**：第3次深挖时提醒"评估成本"，第4次及以上警告"建议停止，避免过度收集" ### 步骤4：充分性检查（必须） 在进入任务规划前，主AI必须回答充分性检查清单： - □ 我能定义清晰的接口契约吗？（知道输入输出、参数约束、返回值类型） - □ 我理解关键技术选型的理由吗？（为什么用这个方案？为什么有多种实现？） - □ 我识别了主要风险点吗？（并发、边界条件、性能瓶颈） - □ 我知道如何验证实现吗？（测试框架、验证方式、覆盖标准） **决策**： - ✓ 全部打勾 → 收集完成，进入任务规划和实施 - ✗ 有未打勾 → 列出缺失信息，补充1次针对性深挖 ### 回溯补充机制 允许"先规划→发现不足→补充上下文→完善实现"的迭代： - 如果在规划或实施阶段发现信息缺口，记录到 `operations-log.md` - 补充1次针对性收集，更新相关 context 文件 - 避免"一步错、步步错"的僵化流程 ### 禁止事项 - ❌ 跳过步骤1（结构化快速扫描）或步骤2（识别关键疑问） - ❌ 跳过步骤4（充分性检查），在信息不足时强行规划 - ❌ 深挖时不说明"为什么需要"和"解决什么疑问" - ❌ 主AI自行收集代码/文档，必须委托 Codex 执行 - ❌ 上下文文件写入错误路径（必须是 `.claude/` 而非 `~/.claude/`） ## 📊 评分和决策机制 ### 评分维度 **技术维度评分**： - 代码质量：可读性、维护性、性能 - 测试覆盖：单元测试、集成测试、边界条件 - 规范遵循：编码标准、架构原则、安全要求 **战略维度评分**： - 需求匹配：功能完整性、用户体验 - 架构一致：系统设计、模块化程度 - 风险评估：潜在问题、扩展性、兼容性 **综合评分**：0-100分 ### 决策规则 - **≥90分且建议"通过"** → 直接确认通过 - **<80分且建议"退回"** → 直接确认退回 - **80-89分或建议"需讨论"** → 仔细审阅后决策 ### 留痕要求 - 所有决策必须记录到 `operations-log.md` - 推翻 Codex 建议时必须记录详细理由 - 保留完整的审查报告和评分依据 ## 🎯 使用示例 ### 深度分析示例 ``` 使用 codex-collaboration skill 分析项目架构，识别潜在的性能瓶颈和改进机会 ``` ### 复杂设计示例 ``` 使用 codex-collaboration skill 设计一个支持高并发的分布式任务队列系统 ``` ### 代码审查示例 ``` 使用 codex-collaboration skill 审查用户认证模块的代码质量和安全性 ``` --- *本 skill 专注于 Codex 协作流程，确保深度分析、复杂设计和质量审查的标准化和高效执行。* --- Source attribution: David-Li0406 Original project: https://github.com/David-Li0406/meta-skill-evloving/blob/ca3a335628981df10c36e00cb9850df2c247ab9a/skill-flow/data/skills-refined-36k/skillsmp/codex-collaboration/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. # Mega AI Hub resource metadata Creator / publisher: David-Li0406 Original project: https://github.com/David-Li0406/meta-skill-evloving/blob/ca3a335628981df10c36e00cb9850df2c247ab9a/skill-flow/data/skills-refined-36k/skillsmp/codex-collaboration/SKILL.md Attribution basis: Source repository record Fields of work: Development & Engineering, Product Management Roles: Backend Developer, ML / AI Engineer, Product Manager Specialties: Backend Tools: Codex, Sequential Thinking Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 7836b4af454c3a3a175f9a727e99300ca2ebc2ef7d86ac31aff48466cca12066