Crag: Open-source tool generates unified AI agent rules from project configs

What Crag does

Crag is an open-source tool that solves configuration drift across multiple AI coding agent rule files. It analyzes your project's existing configurations and generates a unified governance file, then compiles that into all the specific rule files needed by different AI agents.

The problem it addresses

Developers using multiple AI coding agents face configuration management challenges. According to the source, one developer maintained 12 rule files across 4 projects (48 files total), including:

• CLAUDE.md for Claude Code
• AGENTS.md for Codex
• .cursor/rules/ for Cursor
• copilot-instructions.md for Copilot
• CI workflows to enforce rules

These files drifted over time, causing issues where agents wrote code that CI rejected because lint rules didn't match. The problem went unnoticed because "nobody reads all 12 files."

How Crag works

The tool has two main commands:

• crag analyze reads your project configurations including CI workflows, package.json, tsconfig, and test configs
• crag compile --target all generates all rule files from the unified governance file

The analysis generates a governance.md file (~80 lines) that includes:

• Gates
• Architecture guidelines
• Testing profile
• Code style rules
• Anti-patterns to avoid
• Framework conventions

The output is described as "auto-generated, reads like a senior engineer wrote it."

Technical details

Crag has several notable technical characteristics:

• No LLM required
• No network dependencies
• Zero dependencies
• Deterministic output
• SHA-verified across platforms

To try it: npx @whitehatd/crag demo (remove space between @ and whitehatd)

Source and availability

The tool is open-source and available on GitHub at https://github.com/WhitehatD/crag.