claudeaudit

Audit repositories for AI agent readiness across 14 categories with maturity scores.

Updated Apr 1, 2026
One-click install
npx skills add https://github.com/heimann/claudeaudit-site --skill claudeaudit
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: claudeaudit
Source: https://github.com/heimann/claudeaudit-site/tree/main
Command: npx skills add https://github.com/heimann/claudeaudit-site --skill claudeaudit

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Audits code repositories to determine how ready they are for autonomous AI agent work, producing structured maturity scores and concrete next steps.

Core Features & Use Cases

  • Automatically assesses repository signals across config readiness and ergonomic readiness, scoring per category and recommending concrete next steps.
  • Generates a structured audit with a maturity level and actionable next steps to improve agent-readiness.

Quick Start

Run claudeaudit on a repo to produce a structured audit report.

Frequently Asked Questions about claudeaudit

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I audit my repository for AI agent readiness?

To audit repository agent readiness, run the tool against your codebase to generate a structured report with per-category scores across 14 areas and a maturity level. It scans for agent signals like CLAUDE.md files to determine how prepared your repo is for autonomous AI work.

What is AI agent readiness and why does my codebase need it?

AI agent readiness measures how well your codebase supports autonomous AI work through config and ergonomic signals. It solves the problem of unknown repository maturity by scoring 14 categories and providing concrete next steps to improve agent integration.

Can I audit a repository for agent signals without a CLAUDE.md file?

Yes, you can audit codebases of any language. The tool scans for CLAUDE.md or other agent signals to assess config and ergonomic readiness, generating a structured maturity score and actionable next steps even if some signals are missing.

What's the best way to assess codebase maturity for autonomous AI agents?

Assessing codebase maturity for autonomous AI agents is best done by scanning repository signals across 14 categories. This approach produces a structured audit with per-category scores and a maturity level, plus concrete next steps to improve agent-readiness.

What categories does an agent readiness audit evaluate?

An agent readiness audit evaluates 14 categories covering config readiness and ergonomic readiness. It scores each category individually to produce a structured report with an overall maturity level and specific recommendations to improve your codebase.