git-daily-work-report

Automatically generate daily work reports by scanning Git repositories. Use this skill when the user asks to: (1) Generate a daily report from git commits, (2) Summarize work done on a specific date based on code changes, (3) Check commits and create work summary for a date. The skill scans all git repositories under a root directory, filters by author, retrieves commit records with file changes, and generates a summarized work content description using LLM analysis.

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Install skill "git-daily-work-report" with this command: npx skills add wangdengyue/git-daily-work-report/wangdengyue-git-daily-work-report-git-daily-work-report

Git Daily Work Report

Automatic daily work report generation by scanning Git repositories.

When to Use

Use this skill when:

  • User asks to generate a daily report from git commits
  • User wants to summarize work done on a specific date
  • User requests work summary based on code changes in a monorepo or multi-repo project
  • User needs to check commits by a specific author

Workflow

Step 1: Scan Git Repositories

Use the bundled script to scan all git repositories under a root directory:

python scripts/get_git_log.py <root_path> <date> [--author <name>] [--json]

Parameters:

  • root_path: Root directory containing multiple git repositories
  • date: Date in YYYY-MM-DD format
  • --author (optional): Filter commits by author name
  • --json: Output in JSON format for LLM processing (default: formatted report)

Example:

# Scan all repos for commits on a specific date
python scripts/get_git_log.py /path/to/project 2026-01-23

# Filter by author
python scripts/get_git_log.py /path/to/project 2026-01-23 --author "dengyue"

# Output JSON for LLM processing
python scripts/get_git_log.py /path/to/project 2026-01-23 --json

Note: The script automatically scans all subdirectories and finds git repositories. It's designed for monorepo layouts where multiple projects exist under a single root directory (e.g., D:\work\ containing projects like Libraries, core, Admin, etc.).

Step 2: Analyze Commits with LLM

Process the git log output to generate work summary:

  1. Group commits by repository/project
  2. Summarize what was changed based on commit messages and file changes
  3. Identify major features, bug fixes, or improvements
  4. Generate clear, professional work content description

Step 3: Submit Report (via MCP)

Use browser automation MCP to submit the generated work content to the internal reporting system.

Output Format

The script generates a formatted report by default:

   dengyue 提交日报(2026-01-23)
==========================================

[Libraries]
- 连接redis的slave
  文件变更:
    M       src/redis/connection.py

[Admin]
- 修复用户登录bug
  文件变更:
    M       controllers/auth.go
    M       models/user.go

For LLM processing, use --json to get structured data. The final work summary should be:

  • Clear and concise: Describe what work was done
  • Categorized: Group related changes by repository
  • Professional: Use appropriate language for daily reporting

Example final output format:

## 2026-01-23 工作总结

### EMLibraries
- 修改 Redis 连接配置,切换到 slave 节点以实现读写分离

### Admin
- 修复用户登录时的 session 验证 bug
- 优化用户信息查询性能

### Bug 修复
- 解决登录超时问题
- 修复权限验证逻辑错误

### 代码优化
- 重构 Redis 连接池管理

Notes

  • Ensure git is installed and accessible in the system PATH
  • The script sets UTF-8 encoding for output on Windows platforms automatically
  • The script handles UTF-8 encoding for commit messages (with error tolerance)
  • File changes are included for each commit to provide context
  • The script skips nested git repositories (only scans top-level repos)
  • For projects with many repositories, the scan may take some time

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