Assess git repositories against evidence-based attributes for AI-assisted development readiness.

📚 Research-Based Assessment: AgentReady's attributes are derived from comprehensive research analyzing 50+ authoritative sources including Anthropic, Microsoft, Google, ArXiv, and IEEE/ACM. Each attribute is backed by peer-reviewed research and industry best practices. View full research report →

AgentReady evaluates your repository across multiple dimensions of code quality, documentation, testing, and infrastructure to determine how well-suited it is for AI-assisted development workflows. The tool generates comprehensive reports with:

• Overall Score & Certification: Platinum/Gold/Silver/Bronze based on comprehensive attribute assessment
• Interactive HTML Reports: Filter, sort, and explore findings with embedded guidance
• Version-Control-Friendly Markdown: Track progress over time with git-diffable reports
• Actionable Remediation: Specific tools, commands, and examples to improve each attribute
• Schema Versioning: Backwards-compatible report format with validation and migration tools

# Login to GitHub Container Registry (required for private image)
podman login ghcr.io

# Pull container
podman pull ghcr.io/ambient-code/agentready:latest

# Create output directory
mkdir -p ~/agentready-reports

# Assess AgentReady itself
git clone https://github.com/ambient-code/agentready /tmp/agentready
podman run --rm \
  -v /tmp/agentready:/repo:ro,z \
  -v ~/agentready-reports:/reports:z \
  ghcr.io/ambient-code/agentready:latest \
  assess /repo --output-dir /reports

# Assess your repository
# For large repos, add -i flag to confirm the size warning
podman run --rm \
  -v /path/to/your/repo:/repo:ro,z \
  -v ~/agentready-reports:/reports:z \
  ghcr.io/ambient-code/agentready:latest \
  assess /repo --output-dir /reports

# Open reports
open ~/agentready-reports/report-latest.html

Rootless Podman (Fedora, RHEL, CentOS): The --userns=keep-id flag maps your host UID into the container, preventing Git "dubious ownership" errors. If you still encounter permission issues, see Podman Rootless Mode for the full solution.

See full container documentation →

# Install
pip install agentready

# Assess AgentReady itself
git clone https://github.com/ambient-code/agentready /tmp/agentready
agentready assess /tmp/agentready

# Create virtual environment
python3 -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -e ".[dev]"

If you use uv, you can run AgentReady directly from GitHub without cloning or installing:

uvx --from git+https://github.com/ambient-code/agentready agentready -- assess .

To install it as a reusable global tool:

uv tool install --from git+https://github.com/ambient-code/agentready agentready

After installing globally:

agentready assess .

For one-time analysis without infrastructure changes:

# Assess current repository
agentready assess .

# Assess another repository
agentready assess /path/to/your/repo

# Specify custom configuration
agentready assess /path/to/repo --config my-config.yaml

# Custom output directory
agentready assess /path/to/repo --output-dir ./reports

Assessing repository: myproject
Repository: /Users/username/myproject
Languages detected: Python (42 files), JavaScript (18 files)

Evaluating attributes...
[████████████████████████░░░░░░░░] 23/25 (2 skipped)

Overall Score: 72.5/100 (Silver)
Attributes Assessed: 23/25
Duration: 2m 7s

Reports generated:
  HTML: .agentready/report-latest.html
  Markdown: .agentready/report-latest.md

Evaluated across 13 categories:

1. Context Window Optimization: CLAUDE.md files, concise docs, file size limits
2. Documentation Standards: README structure, inline docs, ADRs
3. Code Quality: Cyclomatic complexity, file length, type annotations, code smells
4. Repository Structure: Standard layouts, separation of concerns
5. Testing & CI/CD: Coverage, test naming, pre-commit hooks
6. Dependency Management: Lock files, freshness, security
7. Git & Version Control: Conventional commits, gitignore, templates
8. Build & Development: One-command setup, dev docs, containers
9. Error Handling: Clear messages, structured logging
10. API Documentation: OpenAPI/Swagger specs
11. Modularity: DRY principle, naming conventions
12. CI/CD Integration: Pipeline visibility, branch protection
13. Security: Scanning automation, secrets management

Attributes are weighted by importance:

• Tier 1 (Essential): 55% of total score - Test execution, types, CLAUDE.md, CI gates, verification, README, layout, lock files, dependency security
• Tier 2 (Critical): 27% of total score - Enforcement, commits, build setup, patterns
• Tier 3 (Important): 14% of total score - Design intent, complexity, logging, API docs
• Tier 4 (Advanced): 4% of total score - Code smells, templates, containers, progressive disclosure

Missing essential attributes (especially test execution at 10% weight) has 10x the impact of missing advanced features.

• Filter by status (Pass/Fail/Skipped)
• Sort by score, tier, or category
• Search attributes by name
• Collapsible sections with detailed evidence
• Color-coded score indicators
• Certification ladder visualization
• Works offline (no CDN dependencies)

Create .agentready-config.yaml to customize weights:

weights:
  agent_instructions: 0.15   # Increase importance (default: 0.07)
  test_execution: 0.15      # Increase importance (default: 0.10)
  conventional_commits: 0.01  # Decrease importance (default: 0.03)
  # Other attributes use defaults, rescaled to sum to 1.0

excluded_attributes:
  - container_setup         # Skip this attribute

output_dir: ./custom-reports

# Assessment commands
agentready assess PATH                   # Assess repository at PATH
agentready assess PATH --verbose         # Show detailed progress
agentready assess PATH --config FILE     # Use custom configuration
agentready assess PATH --output-dir DIR  # Custom report location

# Configuration commands
agentready --validate-config FILE        # Validate configuration
agentready generate-config               # Create example config

# Research report management
agentready research-version              # Show bundled research version
agentready research validate FILE        # Validate research report
agentready research init                 # Generate new research report
agentready research add-attribute FILE   # Add attribute to report
agentready research bump-version FILE    # Update version
agentready research format FILE          # Format research report

# Utility commands
agentready --version                     # Show tool version
agentready --help                        # Show help message

AgentReady follows a library-first design:

• Models: Data entities (Repository, Assessment, Finding, Attribute)
• Assessors: Independent evaluators for each attribute category
• Services: Scanner (orchestration), Scorer (calculation), LanguageDetector
• Reporters: HTML and Markdown report generators
• CLI: Thin wrapper orchestrating assessment workflow

# Run all tests with coverage
pytest

# Run specific test suite
pytest tests/unit/
pytest tests/integration/
pytest tests/contract/

# Run with verbose output
pytest -v -s

# Format code
black src/ tests/

# Sort imports
isort src/ tests/

# Lint code
flake8 src/ tests/ --ignore=E501

# Run all checks
black . && isort . && flake8 .

src/agentready/
├── cli/              # Click-based CLI entry point
├── assessors/        # Attribute evaluators (13 categories)
├── models/           # Data entities
├── services/         # Core logic (Scanner, Scorer)
├── reporters/        # HTML and Markdown generators
├── templates/        # Jinja2 HTML template
└── data/             # Bundled research report and defaults

tests/
├── unit/             # Unit tests for individual components
├── integration/      # End-to-end workflow tests
├── contract/         # Schema validation tests
└── fixtures/         # Test repositories

All attributes are derived from evidence-based research with 50+ citations from:

• Anthropic (Claude Code documentation, engineering blog)
• Microsoft (Code metrics, Azure DevOps best practices)
• Google (SRE handbook, style guides)
• ArXiv (Software engineering research papers)
• IEEE/ACM (Academic publications on code quality)

See src/agentready/data/RESEARCH_REPORT.md for complete research report.

MIT License - see LICENSE file for details.

Contributions welcome! Please ensure:

• All tests pass (pytest)
• Code is formatted (black, isort)
• Linting passes (flake8)
• Test coverage >80%

• Documentation: See /docs directory
• Issues: Report at GitHub Issues
• Questions: Open a discussion on GitHub

Quick Start: pip install -e ".[dev]" && agentready assess . - Ready in <5 minutes!