--- name: code-module-e2aba22ab9dc description: 'Generate a tree-structured module overview with functional summaries for codebases. Use when user says: "init-modules" OR requests to: (1) Understand the structure of a codebase, (2) Generate a hierarchical view of directories/modules with their purposes, (3) Create modules.md documentation with tree structure and concise function descriptions, (4) Summarize module functionality from leaf directories upward. Triggered by init-modules command.' --- # Code Module Analysis ## Overview Analyzes a codebase directory structure and generates a hierarchical modules.md document with tree view and concise functional summaries for each module. **Key Feature**: Automatic validation ensures every directory has a functional description, with no missing or placeholder entries. ## Workflow ### 阶段 1：生成骨架 (Skeleton Generation) **目标**：先创建完整的目录结构和占位符，用户可以立即查看项目结构。 #### 1.1 构建目录树 使用 `find` 或 `bash` 命令发现当前目录的所有目录，排除常见的忽略模式： ```bash # 获取所有目录作为树结构 find . -type d -not -path '*/node_modules/*' -not -path '*/.git/*' -not -path '*/__pycache__/*' -not -path '*/.venv/*' | sort ``` 构建 markdown 树结构，使用： - `├── ` 表示有后续兄弟节点 - `└── ` 表示组中最后一个项目 - 根据深度层级正确缩进 #### 1.2 生成初始 modules.md 创建或覆盖 `modules.md`，包含： ```markdown # Modules ## Directory Structure <完整的树形结构，从根目录开始> ## Module Descriptions <完整路径> - [待分析] <完整路径> - [待分析] ... ``` **格式规范：** - **Directory Structure 部分**： - 树形结构显示 - 使用 `├──` 和 `└──` 表示层级关系 - 不在树中使用完整路径（仅目录名） - **Module Descriptions 部分**： - 每个目录使用**完整路径**（从根目录开始） - 例如：`myproject/src/api/auth/ - [待分析]` - 按字母顺序排列 - 描述在连字符后，用空格分隔 - **进度头**（在文件末尾）： - 格式：`` - 初始状态：`` ### 阶段 2：增量填充 (Incremental Population) **目标**：逐个分析目录并实时更新，提供进度反馈。 #### 2.1 识别叶子目录 从树中识别叶子目录（没有子目录的目录）： ```bash # 查找不包含其他目录的目录 find . -type d -exec sh -c 'find "$1" -maxdepth 1 -type d | wc -l | grep -q "^1$"' _ {} \; ``` #### 2.2 分析叶子模块（批处理） 对于每个叶子目录： 1. 列出所有代码文件（扩展名：.py, .js, .ts, .tsx, .java, .go, .rs, .cpp, .c, .cs, 等） 2. 使用 `read` 工具读取文件内容 3. 分析功能： - 从文件名和代码结构识别主要目的 - 查找类/函数定义、导入语句和关键逻辑 - 提取核心功能 4. 生成功能描述（10~50 个中文字符） - 聚焦主要目的 - 使用现在时动词（如 "handles", "manages", "processes"） - 具体但简短 **示例摘要：** - "用户认证与权限管理模块" - "通用工具函数与辅助类库" - "RESTful API 接口与路由处理" - "数据模型与实体定义层" **批量更新策略：** - 每处理 5 个叶子目录后，批量更新 modules.md - 更新进度头中的时间戳和当前处理目录 - 继续直到所有叶子目录完成 #### 2.3 分层向上传播（按层级） 对于非叶子目录： 1. **按层级从深到浅处理**： - 从最深的层级开始（叶子目录的父目录） - 每处理完一层，再处理上一层 2. **等待所有子目录完成**： - 只有当某个目录的所有子目录都有描述后，才处理该目录 - 确保向上传播基于完整的子目录信息 3. **合并子目录摘要**： - 如果所有子目录服务于相似目的 → 使用该目的 - 如果子目录有不同目的 → 使用统称 - 描述长度：10~50 个中文字符 - 保持层级性（如 "Auth module" 用于 "login", "register" 等子目录） **示例：** ``` auth/ ├── login/ → "Login handlers" └── register/ → "Registration handlers" → "Auth module" ``` 4. **批量更新**： - 每处理完 5 个父目录，批量更新 modules.md - 更新进度头 - 继续直到根目录 #### 2.4 验证与修复 (Validation & Fix) **目标**：确保所有目录都有有效的功能描述，不会遗漏。 ##### 验证步骤 1. **检查完整性**： - 对比文件系统中的所有目录与 modules.md 中的描述 - 找出缺失、为空或仍标记为 `[待分析]` 的目录 2. **自动修复**： - 对每个缺失描述的目录执行 `analyze_directory_code()` - 基于目录名、文件名和代码内容生成功能描述 - 批量更新 modules.md（每 5 个目录更新一次） 3. **错误处理**： - 单个目录分析失败不影响其他目录 - 失败时使用目录名生成默认描述（如 `{dirname}相关功能模块`） ##### 验证命令 也可以单独使用验证命令检查现有文档： ```bash python scripts/analyze_tree.py --validate /path/to/project ``` 输出示例： ``` ============================================================ Validating module descriptions... ============================================================ Found 150 directories in filesystem. Found 148 descriptions in modules.md. Found 2 directories without valid descriptions. Fixing missing descriptions... Fixed 2 missing descriptions [OK] Validation and fix completed! ``` #### 2.5 完成 1. 移除进度头（``） 2. 运行自动验证步骤（确保所有目录都有描述） 3. 报告完成状态（包含验证结果） ## Constraints - **Summary length**: 10~50 个中文字符（保留明确英文缩写） - **Bottom-up analysis**: Always start with leaf directories, then propagate upward - **Code-aware**: Base summaries on actual code content, not just directory names - **Markdown tree**: Use proper tree symbols (├──, └──) with correct indentation - **Batch update**: Update modules.md every 5 directories analyzed - **Progress tracking**: Always update progress header during analysis - **Full paths**: Use complete paths (e.g., `myproject/src/api/`) in Module Descriptions section - **Path format**: Use forward slashes (`/`) for all paths, ensure cross-platform compatibility - **Propagate timing**: Only propagate summary upward after all children are complete - **Validation required**: Always run validation step after generation to ensure all directories have descriptions - **No missing descriptions**: Final modules.md must have a valid description for every directory (no `[待分析]` placeholders) ## Analysis Strategy The script uses a **three-tier analysis strategy** to maximize speed and accuracy: ### Tier 1: Path-based Heuristics (Fastest) - Matches directory path patterns (e.g., `/platform/service/manage/hr/` → 人力资源管理模块) - Recognizes common project structures - No file I/O required - Covers ~80-90% of typical directories ### Tier 2: Directory Name Patterns - Matches naming conventions (e.g., `service_*.go`, `*_controller.go`, `api_*.go`) - Recognizes file-level patterns - No file content reading - Covers additional ~5-10% of directories ### Tier 3: Code Content Analysis (Fallback) - Only triggered when **`NEED_CODE_ANALYSIS`** is returned from Tier 1/2 - Reads first 5 code files per directory - Analyzes imports, package names, and keywords - Covers remaining edge cases **Performance**: Most projects are analyzed using Tier 1/2 only (0-10 files read), enabling near-instant analysis for large codebases. ## When to Use **This skill requires EXPLICIT user invocation.** Use this skill ONLY when: - User says "init-modules" command - User explicitly requests to generate or create modules documentation - User explicitly asks to analyze codebase structure and summarize modules - User explicitly requests module overview or tree structure generation **DO NOT auto-invoke** this skill for any other reason, even when analyzing codebases for other purposes. ## Process Notes - Always read actual code files before summarizing - Skip common directories: node_modules, .git, __pycache__, .venv, dist, build - For large files, focus on: imports, class definitions, main functions, comments - If directory has no code files, mark as "Configuration" or "Empty" - Use complete paths (e.g., `myproject/src/api/`) in Module Descriptions section - Use forward slashes (`/`) for all paths to ensure cross-platform compatibility - Update modules.md every 5 directories analyzed for progress tracking - Remove progress header when analysis is complete - **Always run validation after generation** to ensure completeness - **Use `--validate` command** to check and fix existing modules.md files - **No directory should be left as `[待分析]`** in the final output ## Usage This skill uses a Python script (`scripts/analyze_tree.py`) for automation. ### Generate modules.md (with automatic validation) ```bash # Generate for current directory python scripts/analyze_tree.py --generate . # Generate for specific directory python scripts/analyze_tree.py --generate /path/to/project ``` The `--generate` command: 1. Creates initial skeleton with all directories 2. Analyzes leaf directories and generates descriptions 3. Propagates summaries upward to parent directories 4. **Automatically validates** and fixes any missing descriptions ### Validate existing modules.md ```bash # Check and fix missing descriptions python scripts/analyze_tree.py --validate /path/to/project ``` The `--validate` command: - Scans filesystem directories - Compares with existing modules.md descriptions - Identifies missing or placeholder (`[待分析]`) descriptions - Automatically analyzes and fixes missing entries ### Interactive options When running `--generate` on an existing `modules.md`, you'll be prompted: 1. **Full overwrite**: Backup existing file and regenerate from scratch 2. **Continue unanalyzed**: Only process directories marked as `[待分析]` 3. **Check new directories**: Only process directories not in existing list ### Expected output ``` Generating modules.md for: . ============================================================ Phase 1: Creating skeleton... ============================================================ Created: modules.md Found 150 directories Phase 1 completed. Found 150 directories, 80 leaf dirs. ============================================================ Phase 2: Incremental population... ============================================================ Analyzing 80 leaf directories... [5/80] Updated 5 leaf directories ... [80/80] Updated 5 leaf directories Propagating summaries upward... Depth 5: processing 20 directories... ... Depth 0: processing 1 directories... ============================================================ Phase 2 completed! ============================================================ ============================================================ Validating module descriptions... ============================================================ Found 150 directories in filesystem. Found 150 descriptions in modules.md. [OK] All directories have valid descriptions! [OK] Generated modules.md successfully! Total directories: 150 Leaf directories analyzed: 80 Parent directories propagated: 70 ``` --- Source attribution: David-Li0406 Original project: https://github.com/David-Li0406/meta-skill-evloving/blob/ca3a335628981df10c36e00cb9850df2c247ab9a/skill-flow/data/skills-refined-36k/skillsmp/code-module/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. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. 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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/code-module/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering, Product Management Roles: Backend Developer, Data Analyst, Product Manager Specialties: API, Backend Tools: Find Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: a1112eb8477fa52656089439b8496ba6b4806cfcfc77321bf5443569ee1e3d03