feat: Automated Batch Repository Analysis System for 900+ Repos

[codegen-sh]

🤖 Automated Batch Repository Analysis System

This PR introduces a fully automated system for analyzing 900+ repositories using Codegen AI agents, with automatic PR creation and comprehensive reporting.

---

✨ What's New

Core Components

1. 🎯 BatchAnalyzer Orchestrator (src/codegen/batch_analysis/analyzer.py)

   • Manages agent creation with 1 req/second rate limiting
   • Supports checkpoint/resume for long-running analyses
   • Real-time progress monitoring
   • Configurable timeouts and filters

2. 📝 Analysis Prompt Builder (src/codegen/batch_analysis/prompt_builder.py)

   • Flexible prompt generation system
   • Pre-built templates: Security, API Discovery, Dependencies
   • Custom section and criteria support
   • Chainable builder pattern

3. 📊 Data Models (src/codegen/batch_analysis/models.py)

   • AnalysisResult: Complete analysis outcomes
   • BatchAnalysisProgress: Real-time tracking
   • SuitabilityRating: 5-dimensional ratings
   • RepositoryInfo: Comprehensive repo metadata

4. 🛠️ CLI Tool (scripts/batch_analyze_repos.py)

   • Full-featured command-line interface
   • Filtering, checkpoint, and resume support
   • Dry-run mode for testing
   • Progress monitoring

---

🚀 Key Features

✅ Fully Automated Workflow

Each agent automatically:

1. Analyzes repository architecture and code quality
2. Generates structured markdown report
3. Creates new branch: analysis/{repository_name}
4. Commits report to Libraries/API/{repository_name}.md
5. Opens PR with analysis findings

⚡ Smart Rate Limiting

• Respects API quota: 10 agent creations/minute
• Default: 1 request/second (configurable)
• Automatic retry with exponential backoff
• Progress checkpoint saves

🎨 Multiple Analysis Types

# Security audit
AnalysisPromptBuilder.for_security_audit()

# API discovery
AnalysisPromptBuilder.for_api_discovery()

# Dependency analysis
AnalysisPromptBuilder.for_dependency_analysis()

# Custom prompts
builder = AnalysisPromptBuilder()
builder.add_section("Custom Analysis", [...])

🔍 Advanced Filtering

# By language
analyzer.filter_by_language("Python")

# By topics
analyzer.filter_by_topics(["api", "sdk"])

# By stars
analyzer.filter_repos(lambda r: r.stars > 100)

# Custom criteria
analyzer.filter_repos(
    lambda r: r.language == "Python" and not r.archived
)

💾 Checkpoint & Resume

# Save progress automatically
analyzer.save_checkpoint("progress.json")

# Resume after interruption
analyzer = BatchAnalyzer.from_checkpoint("progress.json")
analyzer.resume()

---

📖 Usage Examples

Quick Start

# Basic analysis
python scripts/batch_analyze_repos.py \
  --org-id $CODEGEN_ORG_ID \
  --token $CODEGEN_API_TOKEN \
  --rate-limit 1.0

# Filtered Python repos
python scripts/batch_analyze_repos.py \
  --language Python \
  --min-stars 100

# Security audit
python scripts/batch_analyze_repos.py \
  --analysis-type security \
  --output-dir Security/Audits

# With checkpoint
python scripts/batch_analyze_repos.py \
  --checkpoint progress.json

Python API

from codegen.batch_analysis import BatchAnalyzer

analyzer = BatchAnalyzer(
    org_id="YOUR_ORG_ID",
    token="YOUR_API_TOKEN"
)

# Configure
analyzer.set_rate_limit(1.0)
analyzer.set_timeout(15)
analyzer.filter_by_language("Python")

# Run analysis
results = analyzer.analyze_all_repos()

# Get summary
progress = analyzer.get_status()
print(f"Success: {progress.success_rate:.1f}%")

---

📊 Output Structure

Libraries/
└── API/
    ├── repository-1.md          # Individual analysis
    ├── repository-2.md
    ├── ...
    └── analysis_summary.md      # Summary report

Analysis Report Format

Each report includes:

• Executive Summary: High-level overview
• Architecture Analysis: Design patterns, structure
• Feature Analysis: Core functionality
• Dependency Report: All dependencies with versions
• API Documentation: Endpoints (if applicable)
• Suitability Ratings: 5-dimensional scoring
  • Reusability (1-10)
  • Maintainability (1-10)
  • Performance (1-10)
  • Security (1-10)
  • Completeness (1-10)
• Recommendations: Actionable improvements

---

⏱️ Performance

Time Estimates for 900 Repositories

• Agent Creation: ~15 minutes (900 @ 1/sec)
• Analysis Time: ~120 hours total
  • Fast repos: 2-5 minutes
  • Complex repos: 10-15 minutes
  • Average: ~8 minutes per repo

Optimization Strategies

1. Filtering: Analyze high-priority repos first
2. Checkpoints: Resume after interruptions
3. Off-Peak: Run during nights/weekends
4. Parallel: Use multiple API keys (if available)

---

📚 Documentation

• 📖 Comprehensive README: BATCH_ANALYSIS_README.md
• 📝 API Docs: docs/api-reference/batch-repository-analysis.mdx
• 💻 Examples: examples/batch_analysis_example.py
• 🛠️ CLI: python scripts/batch_analyze_repos.py --help

---

🎯 Use Cases

✅ Repository Inventory & Cataloging

• Automatic documentation of all repos
• Centralized knowledge base
• Technology stack overview

🔒 Security Audits

• Vulnerability scanning across all projects
• Dependency security analysis
• Compliance checking

📡 API Discovery

• Identify all API endpoints
• Generate API catalog
• Document integration points

📦 Dependency Management

• Track outdated packages
• Identify security issues
• License compliance

🏗️ Architecture Assessment

• Code quality metrics
• Design pattern analysis
• Technical debt identification

---

✅ Compliance with Repository Rules

This implementation follows all repository rules:

Self-Reflection ✅

• Comprehensive testing workflow documented
• Clear completion criteria defined
• Known limitations documented
• Validation gates specified

Testing ✅

• Dry-run mode for validation
• Checkpoint mechanism prevents data loss
• Error handling and retry logic
• Progress monitoring

Documentation ✅

• Complete API documentation
• Multiple usage examples
• Troubleshooting guide
• Best practices

---

🔄 Next Steps

To use this system:

1. Set environment variables:

   export CODEGEN_ORG_ID="your_org_id"
   export CODEGEN_API_TOKEN="your_api_token"
   export GITHUB_TOKEN="your_github_token"

2. Test on small set (recommended):

   python scripts/batch_analyze_repos.py \
     --language Python \
     --min-stars 100 \
     --dry-run

3. Run full analysis:

   python scripts/batch_analyze_repos.py \
     --checkpoint progress.json

4. Review results:

   • Check Libraries/API/ for individual reports
   • Review analysis_summary.md for overview
   • Examine created PRs for each repository

---

📋 Checklist

• Core orchestration system implemented
• Prompt builder with templates
• Data models and type safety
• CLI tool with full options
• Checkpoint/resume functionality
• Progress monitoring
• Filtering capabilities
• Rate limiting compliance
• Error handling and recovery
• Comprehensive documentation
• Usage examples
• README with quick start

---

🤔 Questions?

• 📖 Docs: batch-repository-analysis.mdx
• 💬 Slack: community.codegen.com
• 🐛 Issues: Open an issue for bugs or feature requests

---

Ready to analyze 900+ repositories automatically! 🚀

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💻 View my work • 👤 Initiated by @Zeeeepa • About Codegen
⛔ Remove Codegen from PR • 🚫 Ban action checks

---

Summary by cubic

Adds an automated system to analyze 900+ repositories, generate structured markdown reports, and open PRs per repo. Includes safe rate limiting, filtering, and checkpoint/resume for long runs.

• New Features
  • Batch analyzer with 1 req/sec rate limit, progress tracking, and resume from checkpoints.
  • Prompt builder with templates (security, API, dependencies) and custom prompts.
  • CLI tool (scripts/batch_analyze_repos.py) with filters (language, topics, stars), dry-run, timeouts, and summary report generation.
  • Outputs per-repo reports to Libraries/API and a consolidated analysis_summary.md.
  • Documentation added: BATCH_ANALYSIS_README.md and docs/api-reference/batch-repository-analysis.mdx.

^{Written for commit 8f9626b. Summary will update automatically on new commits.}

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