work-summary
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.zipInstalls 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:
-
Overview Section
- Time period covered
- Total commits, files changed, lines added/removed
- Repositories analyzed
-
Key Achievements
- Major features or changes based on commit messages
- Significant file modifications
- Pattern analysis of work type
-
Contribution Statistics
- Commit frequency over time
- Code churn metrics
- File type breakdown
- Most active areas of codebase
-
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
- Meaningful Commit Messages: The quality of the summary depends on commit message quality
- Time Alignment: Align time ranges with review periods (quarters, months, etc.)
- Multiple Repos: Analyze all relevant repositories for complete picture
- Author Matching: Ensure author filter matches git config user (name or email)
- 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:
- Confirms repository path and time range (Oct 1 - Dec 31, 2024)
- Runs analysis script with appropriate parameters
- 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
.gitfolder - 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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