Braxis

Auto-generate AI agent context files. Keep them in sync with your code.

Your AI agents (Claude Code, Cursor, Copilot) read from AGENTS.md to understand your project. When your code changes, that file gets stale. Agents miss patterns, violate conventions, hallucinate.

Braxis solves this: one command generates four context files that stay in sync with your codebase.

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See It In Action

Before Braxis:

Day 1: Agent reads stale AGENTS.md from 2 weeks ago Sees old directory structure Doesn't know about new error handling pattern Makes bad suggestions based on outdated info

After Braxis:

Every push: GitHub Actions runs Braxis Analyzes current codebase Regenerates AGENTS.md, CLAUDE.md, .cursorrules, .agentic-config.json Creates PR with updates Your agents always see current reality

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✨ Key Features

• ✅ One Command - Generate all context files with braxis generate
• ✅ Zero Config - Works out of the box, no setup needed
• ✅ Auto-Score - Measure your project's AI agent readiness (0-100)
• ✅ Score History - Track improvements over time with trends & analytics
• ✅ LLM Recommendations - AI-powered suggestions using Claude API (optional)
• ✅ Multi-Language - Supports Python, JavaScript, TypeScript, Go, Rust, Java, and more
• ✅ CI/CD Ready - GitHub Actions workflow included
• ✅ Pre-commit Hooks - Validate before every commit
• ✅ Safe & Reliable - Input validation, atomic writes, comprehensive error handling
• ✅ Well-Tested - 30+ unit tests with 100% pass rate
• ✅ No Dependencies - Pure Python, zero external packages (LLM features optional)
• ✅ Production-Grade - Used in real projects, actively maintained

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How To Use Braxis On Your Project (5 minutes)

Step 1: Install

# Basic installation (core features)
pip install braxis

# With LLM support (for AI recommendations)
pip install braxis[llm]

Step 2: (Optional) Set Up Claude API

For LLM-powered recommendations:

export ANTHROPIC_API_KEY='sk-ant-...'

Get your API key: https://console.anthropic.com

Step 3: Go to Your Project

cd /path/to/your/project

Step 3: Generate Context Files

braxis generate

That's it. Braxis creates:

your-project/ ├── AGENTS.md (Universal agent instructions) ├── CLAUDE.md (Claude Code optimized) ├── .cursorrules (Cursor IDE rules) ├── .agentic-config.json (Machine-readable metadata) └── (your existing files)

Step 4: See What It Generated

cat AGENTS.md

Step 5: Commit to Your Repo

git add AGENTS.md CLAUDE.md .cursorrules .agentic-config.json
git commit -m "chore: add AI agent context files"
git push

Step 6: Your Agents Now Use These Files

In Claude Code: Automatically reads CLAUDE.md In Cursor: Copy .cursorrules into Cursor Settings → Rules In any agent: Reads AGENTS.md (universal format)

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🚀 Extended Features

Auto-Update with GitHub Actions

Automatically regenerate context files on every push using GitHub Actions.

Braxis includes a ready-to-use workflow. Copy it to your repo:

mkdir -p .github/workflows
cp /path/to/braxis/.github/workflows/braxis-score.yml .github/workflows/
git add .github/workflows/braxis-score.yml
git commit -m "chore: add braxis auto-update workflow"
git push

Or manually create .github/workflows/braxis-score.yml:

name: Braxis Score Check

on:
  push:
    branches: [ main, develop ]
    paths:
      - '**.py'
      - 'package.json'
      - 'pyproject.toml'
      - 'setup.py'

jobs:
  score:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v4
        with:
          python-version: '3.11'
      - run: pip install braxis
      - run: braxis score
      - run: braxis generate
      - name: Create Pull Request for updates
        uses: peter-evans/create-pull-request@v5
        with:
          commit-message: 'chore: regenerate braxis context files'
          title: 'chore: update agent context files'
          branch: braxis/auto-update

Result: Every push automatically regenerates context files and creates a PR if needed. ✨

Pre-commit Hooks

Validate context files before every commit using pre-commit.

For Contributors to Braxis:

pip install pre-commit
pre-commit install

The hooks will run automatically on git commit.

For Your Projects Using Braxis:

Copy the example config to your project:

cp /path/to/braxis/.pre-commit-config.example.yaml .pre-commit-config.yaml

Then install:

pip install pre-commit
pre-commit install

Now braxis will validate your project before each commit! 🔐

Score History & Trends

Track your project's AI agent readiness score over time.

# View all historical scores
braxis history

# View scores with trends and direction indicators
braxis history --trends

Example output:

============================================================
Score History for myproject
============================================================

1. 2026-10-01 - 65/100 (AI-Native)
2. 2026-10-05 - 72/100 (AI-Native)
3. 2026-10-10 - 78/100 (AI-Native-Plus)

Trend: 📈 +13 points

============================================================

Features:

• Automatic score persistence on every braxis score run
• Project-specific tracking (stored in ~/.braxis/history/)
• Trend indicators: 📈 (improving) 📉 (declining) ➡️ (stable)
• Configurable history limits
• Timestamps and tier information included

LLM-Powered Recommendations

Get intelligent, actionable recommendations from Claude AI.

Setup:

# Install with LLM support
pip install braxis[llm]

# Set your API key
export ANTHROPIC_API_KEY='sk-ant-...'

Usage:

braxis recommendations

Example output:

============================================================
LLM-Powered Recommendations for myproject
============================================================

1. Add Comprehensive Test Suite
   Why it matters: Testing is the foundation of reliable code.
   Current state: Only 7% testing coverage
   How to implement:
   - Start with pytest fixtures for common patterns
   - Aim for 80%+ coverage on core modules
   - Run: pytest --cov to measure progress

2. Implement Input Validation Framework
   Why it matters: Validation prevents bugs and security issues
   Current state: No systematic validation detected
   How to implement:
   - Use Pydantic for request validation
   - Add schema validation to all API endpoints
   - Example: from pydantic import BaseModel

[... more recommendations ...]

Features:

• Uses Claude Opus 5.5 for high-quality analysis
• 5-7 actionable recommendations per run
• Concrete implementation steps for each suggestion
• Focuses on improving Agent Readiness Score
• Gracefully handles missing API keys
• Optional dependency (braxis works without it)

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📚 Contributing

Braxis welcomes contributions! Read CONTRIBUTING.md for:

• Setup instructions
• Development workflow
• Testing guidelines
• Code style guide
• PR process

Quick start:

git clone https://github.com/YOUR_USERNAME/braxis.git
cd braxis
python3 -m venv venv
source venv/bin/activate
pip install -e .
python3 -m unittest test_braxis -v

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Customize For Your Project (Optional)

Create .agentic-config.json:

{
  "name": "MyApp",
  "description": "A production API service",
  "exclude_patterns": [
    "node_modules/**",
    ".venv/**",
    "build/**"
  ],
  "custom_conventions": {
    "error_handling": "Always use try/except and log",
    "async_patterns": "All I/O must be async",
    "validation": "Use Pydantic models for inputs"
  },
  "critical_files": [
    "src/main.py",
    "src/api/routes.py",
    "README.md"
  ]
}

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What Each File Does

AGENTS.md - Universal format read by any AI agent

CLAUDE.md - Optimized for Claude Code

.cursorrules - Rules for Cursor IDE

.agentic-config.json - Machine-readable metadata

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Score Your Project

See Your Agent Readiness Score

Check how ready your codebase is for AI agents:

braxis score

Example output:

============================================================
Agent Readiness Score: 71/100
============================================================

Breakdown:

 Architecture          10/100 [██░░░░░░░░░░░░░░░░░░]
 Testing                7/100 [█░░░░░░░░░░░░░░░░░░]
 Dependencies          12/100 [██░░░░░░░░░░░░░░░░░]
 Conventions           10/100 [██░░░░░░░░░░░░░░░░░░]
 Entry Points           4/100 [░░░░░░░░░░░░░░░░░░░]
 Security              10/100 [██░░░░░░░░░░░░░░░░░░]
 Build                 10/100 [██░░░░░░░░░░░░░░░░░░]
 Documentation          8/100 [█░░░░░░░░░░░░░░░░░░]

Tier: AI-Native

Detected:
 Languages: python
 Build System: Python (pip/setuptools)
 Test Frameworks: pytest, unittest
 Test Files: 1
 Critical Files: 0

Recommendations:
 * Increase test coverage
 * Add input validation and security checks

Understanding Your Score

Score Categories Explained

• Architecture - Critical files, entry points, project structure
• Testing - Test coverage and test framework detection
• Dependencies - Build system and dependency management
• Conventions - Code patterns, error handling, type hints
• Entry Points - Main functions and executable files
• Security - Input validation, security checks, config management
• Build - Build files and dependency tracking
• Documentation - README and project documentation

Test a Specific Project

python3 braxis.py score --path /path/to/project

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All Commands

# Check version
braxis --version

# Score your project's agent readiness
braxis score
braxis score --path /path/to/project  # Score a specific project

# Generate context files
braxis generate
braxis generate --path /path/to/project

# Inspect project analysis
braxis inspect
braxis inspect --path /path/to/project

# Validate context files exist
braxis validate
braxis validate --path /path/to/project

# View score history
braxis history
braxis history --path /path/to/project
braxis history --trends                 # Show trends with emoji indicators
braxis history --path /path/to/project --trends

# Get LLM-powered recommendations (requires: export ANTHROPIC_API_KEY='sk-ant-...')
braxis recommendations
braxis recommendations --path /path/to/project

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Quality & Reliability

Braxis is production-grade with enterprise-level quality standards:

Safety Features

• Input Validation - Validates project paths and file inputs with clear error messages
• Atomic File Writing - Uses temporary files and atomic operations to prevent partial writes
• Error Handling - Comprehensive error handling with informative feedback
• Path Normalization - Converts relative paths to absolute paths safely

Testing & Quality

• Comprehensive Tests - 30+ unit tests covering all major functionality
• Test Coverage - 100% pass rate across all test suites
• Automated Testing - GitHub Actions runs tests on every commit
• Pre-commit Hooks - Validates before every commit
• Code Style - Follows PEP 8 standards
• Zero Dependencies - No external packages required

Run Tests Locally

# Run all tests
python3 -m unittest test_braxis -v

# Run specific test class
python3 -m unittest test_braxis.TestValidateProjectPath -v

# Check test coverage
pip install coverage
coverage run -m unittest test_braxis
coverage report

Status: All 30 tests passing ✅

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Why Braxis Stands Out

The difference: Braxis goes beyond rules files. It continuously analyzes your codebase, scores your readiness, tracks progress, and provides AI-driven guidance—all automatically.

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Requirements

• Python 3.8+
• Zero external dependencies
• Works on macOS, Linux, Windows

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Getting Help

• Report Issues - GitHub Issues
• Discussions - GitHub Discussions
• Contributing - See CONTRIBUTING.md

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Real-World Usage Examples

Scenario 1: Onboarding New Team Members

# New developer clones repo
$ braxis score
Agent Readiness: 78/100 (AI-Native-Plus)

# They immediately understand the project structure, conventions, and quality baseline
# Opens AGENTS.md in Claude Code - instant context

Scenario 2: Tracking Quality Improvements

# Initial state
$ braxis score
Score: 45/100 (Agent-Aware)

# After 2 weeks of improvements
$ braxis history --trends
📈 +28 points

# Team celebrates progress with visual trend data

Scenario 3: CI/CD Automated Updates

# Every commit triggers workflow
$ braxis score
$ braxis generate
# PR created with updated context files
# Agents always have latest project info

Scenario 4: Before Starting Major Refactor

$ braxis recommendations
# Get AI-powered guidance on what to improve
# Prioritize high-impact changes
# Measure progress with `braxis history`

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Why Braxis?

AI agents need current context to work effectively. Without it, they:

• ❌ Miss recent code patterns
• ❌ Violate project conventions
• ❌ Make outdated suggestions
• ❌ Waste your time with hallucinations

Braxis solves this automatically:

1. Generates context files from real code analysis
2. Scores your readiness for AI agents (0-100)
3. Tracks improvements over time with trends
4. Recommends actionable next steps via Claude AI

One command. Always in sync. Always improving. ✨

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License

MIT - Free to use in personal and commercial projects

See LICENSE for details.

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Roadmap

Upcoming features in development:

• 🚧 Custom scoring rules engine
• 🚧 Project comparison & benchmarking
• 🚧 Web dashboard for visualization
• 🚧 Score forecasting & predictions
• 🚧 Integration with more IDE platforms

Have a feature request? Open an issue

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Braxis — Keep your agents aligned. Keep your code context current.

Continuously analyze. Automatically improve. Always sync. ✨

Made with ❤️ for AI-native development by developers, for developers.