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rulecheck

Detect and fix CLAUDE.md rule violations in a codebase.

Updated Apr 11, 2026
Search Tags:#automation#worktree#memory#code-quality#CLAUDE.md#pull-request#rulecheck
One-click install
npx skills add https://github.com/az9713/Archon-tutorial --skill rulecheck-az9713
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rulecheck
Source: https://github.com/az9713/Archon-tutorial/tree/main/.claude/skills/rulecheck
Command: npx skills add https://github.com/az9713/Archon-tutorial --skill rulecheck-az9713

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Detects and fixes CLAUDE.md rule violations in a codebase using a dedicated autonomous agent.

Core Features & Use Cases

  • Autonomous detection and remediation of CLAUDE.md rule violations within the codebase.
  • Safe, isolated worktree edits with automatic validation and PR creation.
  • Persistent memory to improve future runs and maintain audit trails.

Quick Start

Invoke /rulecheck with an optional focus area to start autonomous scanning.

Frequently Asked Questions about rulecheck

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

FAQPage Schema
How do I automatically check my codebase for CLAUDE.md rule violations?▼

Scanning for CLAUDE.md violations is handled by an autonomous agent that detects infractions and applies fixes in a disposable worktree. This ensures your main branch stays safe while the agent validates changes and opens a pull request.

How do I fix codebase rule violations without risking my main branch?▼

Rule violations are fixed safely within a disposable git worktree. This isolated environment allows the agent to apply edits, validate the results, and open a pull request without ever touching your main branch directly.

Can I automatically open a pull request after fixing code quality issues?▼

Yes, a pull request is opened automatically after the agent detects and fixes rule violations. The PR is generated once changes are validated within the disposable worktree, ensuring only auditable and repeatable improvements are submitted for review.

How does persistent memory improve automated codebase remediation?▼

Persistent memory improves automated remediation by tracking progress across multiple runs and maintaining an audit trail. This allows the autonomous agent to recall previous fixes and produce more consistent, auditable rule enforcement over time.

What is the best way to enforce deterministic code quality rules across runs?▼

The best way to enforce code quality rules deterministically is using an autonomous agent with deterministic orchestration and safety hooks. This setup ensures repeatable improvements by validating all fixes in an isolated worktree before submission.

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