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name: work-summary description: Generate performance review summaries from git repositories by analyzing commits, code changes, and contribution patterns over a specified time range. Use when the user needs to create work reports, performance reviews, or contribution summaries from git history. allowed-tools: Read, Bash, Write, Glob

Work Summary Skill

Comprehensive git repository analysis tool for generating performance review content and work summaries.

When to Use

• User asks to generate a work summary or performance review
• User needs to analyze git contributions over a time period
• User wants statistics about their work in one or more repositories
• User requests a report of code changes, commits, or development activity

How to Use

Step 1: Gather Information

Ask the user for:

• Repository path(s): One or more local git repository paths (absolute or relative)
• Time range: Start and end dates (e.g., "2024-01-01 to 2024-12-31", "last 3 months", "Q4 2024")
• Author filter (optional): Git author name/email to filter commits (defaults to current git user)
• Output format (optional): "markdown", "text", or "json" (defaults to markdown)

Step 2: Validate Repository

Use the Bash tool to verify the repository exists and is a valid git repository:

Copycd <repo_path> && git rev-parse --git-dir

Step 3: Run Analysis Script

Execute the work summary analysis script:

Copypython $SKILL_ROOT/scripts/analyze_work.py \
  --repo <absolute_repo_path> \
  --start-date <YYYY-MM-DD> \
  --end-date <YYYY-MM-DD> \
  --author "<author_name_or_email>" \
  --output <output_json_path> \
  --format <markdown|text|json>

For multiple repositories:

Copypython $SKILL_ROOT/scripts/analyze_work.py \
  --repo <repo1> <repo2> <repo3> \
  --start-date <YYYY-MM-DD> \
  --end-date <YYYY-MM-DD> \
  --author "<author_name_or_email>" \
  --output <output_json_path>

Step 4: Parse and Present Results

Read the generated output file and present the work summary to the user in a well-formatted report. Include:

1. Overview Section

   • Time period covered
   • Total commits, files changed, lines added/removed
   • Repositories analyzed

2. Key Achievements

   • Major features or changes based on commit messages
   • Significant file modifications
   • Pattern analysis of work type

3. Contribution Statistics

   • Commit frequency over time
   • Code churn metrics
   • File type breakdown
   • Most active areas of codebase

4. Detailed Timeline (optional)

   • Week-by-week or month-by-month breakdown
   • Notable commits and changes

Step 5: Offer Enhancements

Ask the user if they want:

• To filter by specific file patterns or directories
• To exclude certain types of commits (e.g., merges, automated commits)
• To add more repositories to the analysis
• To export in a different format
• To generate visualizations (commit heatmap, language breakdown, etc.)

Script Dependencies

The analysis script requires:

• Python 3.8+
• GitPython library for git operations

Install with:

Copypip install -r $SKILL_ROOT/scripts/requirements.txt

Output Structure

The script generates a JSON file with the following structure:

Copy{
  "summary": {
    "time_range": {"start": "...", "end": "..."},
    "total_commits": 123,
    "total_files_changed": 456,
    "total_insertions": 7890,
    "total_deletions": 1234,
    "repositories": ["repo1", "repo2"]
  },
  "commits": [
    {
      "hash": "abc123",
      "date": "2024-01-15T10:30:00",
      "message": "Add new feature",
      "files_changed": 5,
      "insertions": 120,
      "deletions": 30,
      "files": ["path/to/file.py", ...]
    }
  ],
  "statistics": {
    "commits_by_week": {...},
    "files_by_extension": {...},
    "most_modified_files": [...],
    "largest_commits": [...]
  },
  "achievements": [
    "Implemented authentication system (23 commits)",
    "Refactored database layer (15 files changed)",
    ...
  ]
}

Tips for Best Results

1. Meaningful Commit Messages: The quality of the summary depends on commit message quality
2. Time Alignment: Align time ranges with review periods (quarters, months, etc.)
3. Multiple Repos: Analyze all relevant repositories for complete picture
4. Author Matching: Ensure author filter matches git config user (name or email)
5. Exclude Noise: Consider filtering out automated commits, merges, or trivial changes

Example Usage

User: "Generate my work summary for Q4 2024 from the ~/projects/my-app repository"

Claude:

1. Confirms repository path and time range (Oct 1 - Dec 31, 2024)
2. Runs analysis script with appropriate parameters
3. Generates comprehensive markdown report with:
   • 47 commits over 3 months
   • Key features: user authentication, API optimization, bug fixes
   • 2,345 lines added across 89 files
   • Primary work areas: backend services, database migrations
   • Contribution timeline with weekly breakdown

Troubleshooting

• Invalid git repository: Ensure path points to a directory with .git folder
• No commits found: Check author filter matches git user configuration
• Date parsing errors: Use YYYY-MM-DD format for dates
• Permission errors: Ensure read access to git repository

Related

See reference.md for detailed explanation of metrics and examples.md for sample outputs.