pr-generator-agent 1.4.0

AI-powered Pull Request Description Generator

AIPR - Agentic Pull Request Description Generator

Automatically analyze git diffs and vulnerabilities to generate comprehensive, well-structured pull request descriptions. By intelligently detecting changes, performing security scans, and leveraging state-of-the-art AI models, AIPR helps teams save time while maintaining high-quality, consistent pull request descriptions.

AI-Driven Development

This project follows an AI-driven development workflow:

• 🤖 Built with AI - Developed using Claude Code with comprehensive AI guidance
• 📋 AI Task Assignment - Structured workflow for AI agents documented in CONTRIBUTING.md
• 📚 AI-Friendly Documentation - Comprehensive guides for AI agents in CLAUDE.md and Architecture Decision Records
• 🏗️ Architecture-First Design - ADRs define behavior and guide implementation patterns

# Install with pipx (recommended)
pipx install pr-generator-agent

# Or with pip
pip install pr-generator-agent

# Set the environment variable for the API key
export ANTHROPIC_API_KEY="your-api-key"

# Generate a PR description
aipr

# Generate a conventional commit message
aipr commit

# Generate PR description from commit range
aipr pr --from abc123 --to def456

# Generate commit message from commit range
aipr commit --from v1.0.0

# Custom usage - Analyze changes against main branch
# Include: Vulnerability scanning
# Use: Azure OpenAI o1-mini model
# Prompt: meta template
# Output: Verbose
aipr pr -t main --vulns -p meta -m azure/o1-mini -v

# Inline with merge request creation
gh pr create -b "$(aipr pr -s)" -t "feat: New Feature"

# Inline with commit creation
git commit -m "$(aipr commit)"

Key Features

• 🔍 Smart Detection: Automatically analyzes working tree changes, compares branches, or analyzes commit ranges
• 📝 Conventional Commits: Generate conventional commit messages from staged changes or commit ranges
• 📊 Commit Range Analysis: Generate descriptions and commit messages from any commit range (SHA to SHA)
• 🛡️ Security First: Optional vulnerability scanning between branches using Trivy
• 🤖 AI-Powered: Multiple AI providers (Azure OpenAI, OpenAI, Anthropic, Gemini) for optimal results
• 🔄 CI/CD Ready: Seamless integration with GitLab and GitHub workflows

Example Output

Change Summary:

1. **Added User Authentication**
   - Implemented JWT middleware
   - Added login/register endpoints
   - Updated bcrypt to v5.1.1

2. **Security Updates**
   - Fixed 2 medium severity vulnerabilities
   - Updated deprecated crypto functions

Security Analysis:
✓ No new vulnerabilities introduced

Commit Message Generation

$ git add src/auth.py tests/test_auth.py
$ aipr commit
feat(auth): add OAuth2 authentication support

$ git add requirements.txt
$ aipr commit
build(deps): update dependencies to latest versions

$ git add README.md docs/guide.md
$ aipr commit
docs: update installation and usage documentation

Requirements

• Python 3.11 or higher (3.11, 3.12 officially supported)
• Git
• LLM API Key (Anthropic, OpenAI, or Azure OpenAI)
• Trivy (used for --vulns scanning)

Environment Variables

Anthropic (Default)

• ANTHROPIC_API_KEY: Anthropic API key

Azure OpenAI

• AZURE_API_KEY: Azure OpenAI API key
• AZURE_API_BASE: Azure endpoint URL
• AZURE_API_VERSION: API version (default: "2024-02-15-preview")

OpenAI

• OPENAI_API_KEY: OpenAI API key

Google Gemini

• GEMINI_API_KEY: Google Gemini API key

Usage

AIPR provides two main commands:

PR Command (Pull Request Descriptions)

aipr pr [options]  # or just 'aipr' for backward compatibility

Options:

• -t, --target: Compare changes with specific branch (default: auto-detects main/master)
• -p, --prompt: Select prompt template (e.g., 'meta')
• --vulns: Include vulnerability scanning
• --working-tree: Use working tree changes
• --from: Starting commit for range analysis (SHA, branch, tag, etc.)
• --to: Ending commit for range analysis (defaults to HEAD, requires --from)

Global Options:

• -v, --verbose: Show API interaction details
• -d, --debug: Preview prompts without API calls
• -s, --silent: Output only the description
• -m, --model: Specify AI model to use

The tool intelligently detects changes by:

1. Using staged/unstaged changes if present
2. Comparing against target branch if working tree is clean

Commit Command (Conventional Commit Messages)

aipr commit [options]

Options:

• --conventional: Generate conventional commit message (default)
• --format: Message format (currently only 'conventional')
• --context: Additional context for the commit message
• --from: Starting commit for range analysis (SHA, branch, tag, etc.)
• --to: Ending commit for range analysis (defaults to HEAD, requires --from)

Examples:

# Basic commit message generation from staged changes
git add .
aipr commit

# Generate commit message from commit range
aipr commit --from abc123 --to def456

# Generate commit message from specific commit to HEAD
aipr commit --from abc123

# With additional context
aipr commit --context "upstream sync"

# Use in automation
git commit -m "$(aipr commit)"

Supported AI Models

Choose from multiple AI providers:

Provider	Model	Notes
Anthropic	claude-4	default (maps to claude-sonnet-4-20250514)
	claude-4.0	alias for claude-4
	claude-3.5-sonnet	maps to claude-3-5-sonnet-20241022
	claude-3.5-haiku	maps to claude-3-5-haiku-20241022
	claude-3-haiku	maps to claude-3-haiku-20240307
	claude	alias for claude-4
Azure OpenAI	azure/o1-mini
	azure/gpt-4o
	azure/gpt-4o-mini
	azure/gpt-4	alias for azure/gpt-4o
	azure	alias for azure/gpt-4o-mini
OpenAI	gpt-4o	maps to gpt-4
	gpt-4-turbo
	gpt-3.5-turbo
	gpt4	alias for gpt-4
	openai	alias for gpt-4o
Google Gemini	gemini-1.5-pro
	gemini-1.5-flash
	gemini-2.5-pro-experimental	maps to gemini-2.5-pro-exp-03-25, default for Gemini
	gemini	alias for gemini-2.5-pro-experimental

Custom Prompts

AIPR supports custom prompt templates that allow you to tailor merge request descriptions to your team's specific needs. Custom prompts enable you to:

• Define consistent formatting across your team
• Include organization-specific requirements
• Add custom sections and validation rules
• Provide examples that match your team's standards

For detailed information on creating and using custom prompts, see our Custom Prompts Tutorial.

Contributing

We welcome contributions! See our Contributing Guide for details on:

• Setting up your development environment
• Our development workflow
• Code style guidelines
• Pull request process
• Running tests

License

This project is licensed under the MIT License - see the LICENSE file for details.