github-repo-analysis

# GitHub Repository Analysis

This skill guides analysis of GitHub repositories to extract meaningful insights about development activity, contributor patterns, and release cycles.

## Core Analysis Capabilities

### Commit Frequency Analysis
- Extract commit history via GitHub API or git commands
- Calculate commit frequency by time period (daily, weekly, monthly)
- Identify patterns in development activity
- Detect periods of high/low activity
- Generate time-series visualizations of commit trends

### Outstanding Contributors Analysis
- Identify top contributors by commit count
- Calculate contribution distribution (percentage per contributor)
- Analyze commit patterns by contributor
- Track first-time vs. recurring contributors
- Generate contributor leaderboards

### Release Timeline Analysis
- Extract release/tag history
- Calculate time between releases
- Identify release patterns and cycles
- Track version numbering schemes
- Map releases to major commit periods

## Implementation Approaches

### Approach 1: GitHub API (Recommended for Public Repos)

Use GitHub's REST or GraphQL API for efficient data retrieval:

```python
import requests
from datetime import datetime

def analyze_commits(owner, repo, token=None):
    headers = {'Authorization': f'token {token}'} if token else {}
    url = f'https://api.github.com/repos/{owner}/{repo}/commits'
    
    all_commits = []
    page = 1
    
    while True:
        response = requests.get(url, headers=headers, params={'page': page, 'per_page': 100})
        commits = response.json()
        if not commits:
            break
        all_commits.extend(commits)
        page += 1
    
    return all_commits

def analyze_contributors(commits):
    contributor_stats = {}
    for commit in commits:
        author = commit['commit']['author']['name']
        contributor_stats[author] = contributor_stats.get(author, 0) + 1
    
    return sorted(contributor_stats.items(), key=lambda x: x[1], reverse=True)

def analyze_releases(owner, repo, token=None):
    headers = {'Authorization': f'token {token}'} if token else {}
    url = f'https://api.github.com/repos/{owner}/{repo}/releases'
    response = requests.get(url, headers=headers)
    return response.json()
```

**Benefits:**
- No repository cloning needed
- Efficient pagination
- Access to additional metadata
- Rate limiting: 60 requests/hour without auth, 5000 with token

### Approach 2: Local Git Repository

Use git commands for detailed analysis when repository is already cloned:

```bash
# Get commit history with timestamps
git log --pretty=format:"%h|%an|%ae|%ad|%s" --date=iso > commits.txt

# Count commits by author
git shortlog -sn --all

# Get all tags/releases
git tag -l --sort=-version:refname

# Commit frequency by week
git log --pretty=format:"%ad" --date=short | awk '{print $1}' | uniq -c

# Commits per month
git log --pretty=format:"%ad" --date=format:"%Y-%m" | sort | uniq -c
```

### Approach 3: Hybrid Approach

Combine both methods for comprehensive analysis:
1. Use API for releases and high-level stats
2. Clone repository for detailed commit analysis
3. Use git commands for advanced filtering

## Analysis Workflow

1. **Repository Identification**
   - Parse GitHub URL or accept owner/repo parameters
   - Validate repository exists and is accessible

2. **Data Collection**
   - Fetch commit history (API or git log)
   - Retrieve release/tag information
   - Collect contributor metadata

3. **Data Processing**
   - Parse timestamps and author information
   - Group commits by time periods
   - Calculate statistics and metrics

4. **Insight Generation**
   - Identify top contributors with percentages
   - Calculate commit frequency trends
   - Map release timeline with intervals
   - Detect anomalies or interesting patterns

5. **Visualization & Reporting**
   - Create charts/graphs for trends
   - Generate summary statistics
   - Present findings in structured format

## Key Metrics to Calculate

### Commit Metrics
- Total commits
- Commits per day/week/month
- Average commits per active period
- Longest streak of daily commits
- Periods of inactivity

### Contributor Metrics
- Total unique contributors
- Top N contributors (typically top 5-10)
- Contribution percentage per contributor
- One-time vs. recurring contributors
- New contributors over time

### Release Metrics
- Total releases
- Time between releases (min, max, average)
- Release frequency trend
- Semantic versioning patterns
- Pre-release vs. stable releases

## Output Formats

### Summary Report
```
Repository: owner/repo
Analysis Period: YYYY-MM-DD to YYYY-MM-DD

Commit Activity:
- Total Commits: N
- Active Contributors: N
- Average Commits/Week: N
- Most Active Period: YYYY-MM

Top Contributors:
1. Name (N commits, X%)
2. Name (N commits, X%)
...

Recent Releases:
- v1.2.3 (YYYY-MM-DD) - N days since previous
- v1.2.2 (YYYY-MM-DD) - N days since previous
...
```

### Detailed Analytics
- Time-series data in CSV/JSON format
- Visualization-ready datasets
- Contributor breakdown by time period
- Release calendar with annotations

## Common Patterns & Tips

**Handle rate limiting:** Always check API rate limit headers and implement exponential backoff

**Large repositories:** For repos with 10k+ commits, consider:
- Analyzing recent history only (e.g., last 12 months)
- Sampling commits rather than processing all
- Using shallow clones for git-based analysis

**Privacy considerations:** GitHub API exposes public data only; private repos require authentication

**Timezone handling:** Normalize all timestamps to UTC for consistent analysis

**Bot commits:** Filter out automated commits (dependabot, renovate) for human contributor analysis

**Email normalization:** Same contributor may use different email addresses; consider consolidation

## Error Handling

- Repository not found: Verify owner/repo spelling
- Rate limit exceeded: Implement retry logic or use authentication
- Empty history: Check if repository has been initialized
- API changes: GitHub API versioning may affect endpoints