work-summary

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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.

Install

mkdir -p .claude/skills/work-summary && curl -L -o skill.zip "https://mcp.directory/api/skills/download/5347" && unzip -o skill.zip -d .claude/skills/work-summary && rm skill.zip

Installs to .claude/skills/work-summary

About this skill

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:

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

Step 3: Run Analysis Script

Execute the work summary analysis script:

python $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:

python $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:

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

Output Structure

The script generates a JSON file with the following structure:

{
  "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.

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