github-analysis

name	github-analysis
description	Analyze GitHub commits, generate PR reviews, calculate contributor leaderboards, and assess code quality. Use when analyzing git commits, reviewing code, generating GitHub activity reports, or tracking developer contributions.

GitHub Analysis

Analyze GitHub activity, review code, and track contributions.

Quick Start

Analyze commits from JSON file:

python scripts/analyze_commits.py commits.json

Generate leaderboard:

python scripts/calculate_leaderboard.py commits.json --period week

Commit Analysis

What to Extract

From each commit, analyze:

• Author & timestamp
• Commit message quality
  • Clear (explains what and why)
  • Vague (just what, no why)
  • Cryptic (no context)
• Files changed (count and types)
• Lines added/removed
• Code quality indicators
  • TODOs added
  • FIXMEs added
  • Console.log/debugging code
  • Commented code
  • Large file changes (>500 lines)

Quality Scoring

Commit Message Quality:

• Excellent (8-10): Clear what + why, follows conventions
• Good (5-7): Clear what, some context
• Poor (1-4): Vague or no context
• Bad (0): Single word, "wip", "test"

Code Quality Indicators:

# Check for debugging code
grep -r "console.log\|debugger\|print(" changed_files/

# Check for TODOs
grep -r "TODO\|FIXME" changed_files/ | wc -l

# Check for commented code
grep -r "^[[:space:]]*//.*=\|^[[:space:]]*/\*" changed_files/

PR Review Template

Use this structure for code reviews:

# Pull Request Review

## Summary
[1-2 sentence overview of changes]

## Code Quality Assessment

### Structure & Organization
- ✅ **Good**: Well-organized, clear separation of concerns
- ⚠️  **Needs Work**: Mixed responsibilities, unclear structure
- 🔴 **Issues**: Significant structural problems

### Naming & Readability
- **Variables**: [clear/unclear/inconsistent]
- **Functions**: [descriptive/vague/confusing]
- **Comments**: [helpful/missing/outdated]

### Testing
- [ ] Unit tests included
- [ ] Integration tests updated
- [ ] Edge cases covered
- [ ] Test coverage: [%]

## Issues Found

### 🔴 Critical
- [Issue with security/correctness impact]

### 🟡 Warnings
- [Issue that should be addressed]

### 🔵 Suggestions
- [Nice-to-have improvements]

## Security Check

- [ ] No hardcoded credentials
- [ ] No SQL injection risks
- [ ] No XSS vulnerabilities
- [ ] Input validation present
- [ ] Authentication/authorization correct

## Performance

- [ ] No obvious performance issues
- [ ] Database queries optimized
- [ ] No N+1 query problems
- [ ] Appropriate caching

## Recommendations

1. [Priority recommendation]
2. [Additional improvement]
 [Nice-to-have enhancement]



 [ ] ✅  - Ready to merge
 [ ] 🟡  - Minor issues, can merge
 [ ] 🔴  - Must address issues before merge

Contributor Leaderboard

Metrics

Track these metrics per contributor:

1. Commit Count (weight: 1x)
2. Lines Changed (weight: 0.5x)
   • Added lines + modified lines
3. Commit Quality (weight: 2x)
   • Average message quality score
4. PR Reviews (weight: 1.5x)
   • Number of PR reviews contributed
5. Response Time (weight: 1x)
   • Average time to respond to PR comments

Scoring Formula

Total Score = (commits × 1) +
              (lines_changed / 100 × 0.5) +
              (avg_quality × 2) +
              (pr_reviews × 1.5) +
              (10 - avg_response_hours × 1)

Leaderboard Format

# GitHub Contributor Leaderboard
## Period: [Week/Month]

| Rank | Contributor | Score | Commits | Lines | Quality | Reviews |
|------|-------------|-------|---------|-------|---------|---------|
| 1    | John Doe    | 45.2  | 12      | 1,234 | 8.5     | 5       |
| 2    | Jane Smith  | 38.7  | 10      | 987   | 7.8     | 4       |

Code Quality Metrics

Complexity Analysis

# Count function complexity (rough estimate)
# Functions with >4 nested levels or >50 lines
grep -n "function\|def " file.js | while read line; do
    # Analyze complexity
done

Code Churn

Files with high churn (changed frequently):

git log --format=format: --name-only | \
    sort | uniq -c | sort -rn | head -20

High churn may indicate:

• Unstable code
• Unclear requirements
• Technical debt
• Active development area

Test Coverage

# Run test coverage (example)
npm test -- --coverage
python -m pytest --cov=src tests/

Good coverage targets:

• Critical paths: 90%+
• Business logic: 80%+
• Overall: 70%+

Data Processing

Input Format (commits.json)

[
  {
    "sha": "abc123",
    "author": "John Doe",
    "email": "[e-mail]",
    "date": "2026-01-04T10:30:00Z",
    "message": "Add user authentication feature",
    "files_changed": ["src/auth.js", "src/users.js"],
    "additions": 125,
    "deletions": 45,
    "files_count": 2
  }
]

Output Format (analysis.json)

{
  "summary": {
    "total_commits": 25,
    "total_contributors": 5,
    "total_files_changed": 67,
    "total_lines": 2345
  },
  "contributors": [
    {
      "name": "John Doe",
      "commits": 12,
      "lines_changed": 1234,
      "avg_quality": 8.5,
      "score": 45.2
    }
  ],
  "hot_files": [
    {"file": "src/auth.js", "changes": 8
  
   
       
       
  

Scripts

analyze_commits.py

Analyzes commit data and generates metrics.

Usage:

python scripts/analyze_commits.py input.json --output analysis.json

calculate_leaderboard.py

Calculates contributor rankings.

Usage:

python scripts/calculate_leaderboard.py commits.json \
    --period week \
    --output leaderboard.json

generate_report.py

Generates HTML report from analysis.

Usage:

python scripts/generate_report.py analysis.json \
    --template github-summary \
    --output report.html

Integration with Agents

Code Agent

# Get commits from GitHub
commits = github.get_commits(repo='owner/repo', days=7)

# Analyze with skill
python scripts/analyze_commits.py commits.json

Reporting Agent

# Generate leaderboard
python scripts/calculate_leaderboard.py commits.json

# Create HTML report
python scripts/generate_report.py analysis.json --template github-summary

Reference Files

• metrics.md - Detailed scoring algorithms
• patterns.md - Code quality patterns to detect
• templates.md - Additional report templates