Stop correcting AI hallucinations caused by stale context files...

The Problem Nobody Talks About
You're using Claude Code, Cursor, or Copilot. They're incredible. But here's what happens in practice:

Day 1: You create AGENTS.md with your project conventions, directory structure, error handling patterns.

Day 2-14: You refactor. Add new error handling. Update conventions. Restructure directories. Your code evolves.

Day 15: Your AI agent reads the 2-week-old AGENTS.md and has no idea about any of these changes.

Result:

❌ Agent misses new patterns
❌ Agent violates conventions it doesn't know about
❌ Agent makes suggestions that break your current architecture
❌ You waste time correcting hallucinations
The fundamental problem: Your AI agents are working with stale documentation.

The Aha Moment
I realized the solution wasn't just "auto-generate context files." Plenty of tools do that (sort of). The real insight was: If AI agents are your primary interface, you need to measure your codebase readiness for them.
**
So I built three things:**

1. Automatic Context File Generation
braxis generate
Generates four formats simultaneously:

AGENTS.md - Universal format any AI can read
CLAUDE.md - Optimized for Claude Code
.cursorrules - Cursor IDE format
.agentic-config.json - Machine-readable metadata
One command. Takes 10 seconds. Your agents always have current info.

2. AI Readiness Scoring (0-100)
braxis score
Analyzes 8 dimensions of your codebase:

Architecture 65/100 [████░░░░░░░░░░░░░░░]
Testing 45/100 [███░░░░░░░░░░░░░░░░]
Dependencies 72/100 [██████░░░░░░░░░░░░░]
Conventions 58/100 [█████░░░░░░░░░░░░░░]
Entry Points 52/100 [████░░░░░░░░░░░░░░░]
Security 68/100 [██████░░░░░░░░░░░░░]
Build 71/100 [██████░░░░░░░░░░░░░]
Documentation 48/100 [███░░░░░░░░░░░░░░░░]
─────────────────────────────
Overall Score: 71/100 (AI-Native)
This tells you exactly how AI-friendly your codebase is and where to improve.

3. Track Progress Over Time
braxis history --trends
Output:

Score History for myproject
════════════════════════════════════════

1. 2026-10-01 - 65/100 (AI-Native)
2. 2026-10-15 - 71/100 (AI-Native)
3. 2026-10-30 - 79/100 (AI-Native-Plus) Trend: 📈 +14 points ════════════════════════════════════════ Every run automatically persists your score. Over time, you see which refactorings actually moved the needle. Teams use this to track quarterly progress.

Bonus: Claude-Powered Recommendations
export ANTHROPIC_API_KEY='sk-ant-...'
braxis recommendations
Claude Opus 5.5 analyzes your specific project and generates 5-7 concrete suggestions:

1. Add Input Validation Framework Why: 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
2. Implement Retry Logic Standardization Why: Your retry patterns are inconsistent Locations:
   • services/api.py:45 (exponential backoff)
   • services/db.py:128 (fixed retry) Current State: No standard pattern How to implement:
   • Create a shared retry decorator
   • Apply consistently across codebase
   • Example: @retry(max_attempts=3, backoff=exponential) [... 5 more specific, actionable recommendations ...] Each suggestion is grounded in your actual code, not generic tips.

How It Works (Technically)

1. Zero Dependencies (Core)
2. pip install braxis
3. # Pure Python, zero external packages
4. Optional LLM Support
5. pip install braxis[llm]
6. export ANTHROPIC_API_KEY='sk-ant-...'
7. braxis recommendations
8. # Now you have Claude-powered analysis
9. Atomic File Writes
10. Uses temporary files + atomic rename to prevent corruption under concurrent CI/CD runs. Your context files are never partially written.

MD5-Based Project Hashing
Project-specific hashing stores scores in ~/.braxis/history/scores_{hash}.json. Supports tracking multiple codebases simultaneously without collisions.

Production-Grade Quality

• 30+ unit tests (100% passing)
• Input validation + path normalization
• Comprehensive error handling
• 15+ language detection
• GitHub Actions workflow included

Real-World Workflow
Day 1: Initial Setup
`# Install
pip install braxis

Generate context files

braxis generate

Check your readiness score

braxis score`

Commit everything

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

**
Every Commit After: Automatic Updates**
Add .github/workflows/braxis-score.yml:

name: Braxis Auto-Update

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

jobs:
update:
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 generate
- name: Create PR if changed
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. Your agents always see current reality.

Why This Matters (For Your Career)
If you're using AI agents heavily:

• Your agents are only as good as their context
• Stale context = hallucinations = wasted time
• Braxis fixes this automatically
• Your team works 20-30% faster with accurate agent context

If you're measuring code quality:

• Most metrics (coverage, complexity, lines of code) miss AI-readiness
• The readiness score tells you how well AI agents can work in your project
• Track it quarterly to see real improvement
• Show leadership concrete progress

If you're optimizing your codebase:

• Most refactoring advice is generic ("add tests," "improve documentation")
• Braxis gives you specific, prioritized recommendations for your project
• Spend time on high-impact changes only

The Numbers

• 📊 622-line README with comprehensive docs
• 🧪 30+ unit tests (100% pass rate)
• 🌍 15+ languages detected (Python, JS, TS, Go, Rust, Java, C#, PHP, Ruby, Kotlin, Scala, Swift, Elixir, Clojure, Shell)
• 📈 5 readiness tiers (Not Ready → Agent-Optimized)
• 🎯 8 scoring categories analyzed
• ⚡ Zero production dependencies
• 🔄 GitHub Actions workflow included
• 📅 MIT licensed - Free to use, commercial-friendly

Get Started (5 Minutes)

1. Install

pip install braxis

2. Run it

cd /path/to/your/project
braxis generate

3. See your score

braxis score

4. Commit the files

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

5. Set up auto-updates (optional)

Copy .github/workflows/braxis-score.yml to your repo

That's it. Your agents now have fresh, accurate context.

Want AI Recommendations?

Install with LLM support

pip install braxis[llm]

Set your API key

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

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

Get Claude's analysis

braxis recommendations
Claude Opus 5.5 will analyze your project and suggest the 5-7 highest-impact improvements with concrete implementation steps.

What Sets It Apart
Most tools generate one static file once. Braxis:

Feature ** **Braxis ** Other Tools**
Auto-generates context 4 formats 1 format (usually)
Scores readiness 0-100 with trends ❌ No
Tracks history Yes, automatically. ❌ No
AI recommendations Claude-powered ❌ Limited
Multi-language 15+ languages Limited
CI/CD integration GitHub Actions ready Manual
Zero dependencies Core only Often 5+
Production-tested 30+ unit tests Varies

Open Source & MIT Licensed
Everything is on GitHub. No corporate lock-in. No proprietary models. Just pure Python doing what it does best.

GitHub: https://github.com/jaykrishna316/braxis PyPI: https://pypi.org/project/braxis/ README with examples: https://github.com/jaykrishna316/braxis/blob/main/README.md

Questions?
What's your biggest frustration with AI agents in your workflow? Drop a comment below—I'd love to hear what breaks or surprises you when you run braxis score on your own project.

Give it a star on GitHub if this solves a problem for you. ⭐

Tags

Python #OpenSource #AI #AIDeveloperTools #DevTools #CursorAI #ClaudeCode #Automation #CI/CD #DeveloperProductivity

Keep your agents aligned. Keep your code context current. One command. Always in sync. 🚀