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audit

Orchestrate AI agents to audit code quality with tiered review and static analysis.

11|1|Updated Aug 6, 2025
Search Tags:#code quality#static analysis#code review#performance#security#audit#agent orchestration
One-click install
npx skills add https://github.com/thkt/dotclaude --skill audit-thkt
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: audit
Source: https://github.com/thkt/dotclaude/tree/main/.ja/skills/audit
Command: npx skills add https://github.com/thkt/dotclaude --skill audit-thkt

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates comprehensive code quality assessments by orchestrating specialized review agents, ensuring code meets high standards for security, performance, and readability.

Core Features & Use Cases

  • Automated Code Auditing: Leverages multiple AI agents to perform in-depth code reviews.
  • Tiered Review Process: Scales the review process based on the number of files, from direct leader review to a full pipeline with sub-reviewers, challengers, and verifiers.
  • Pre-flight Static Analysis: Automatically detects and runs linters and check tools before agent execution.
  • Use Case: Before merging a large feature branch, run /audit to get a thorough security and quality check across all modified files, identifying potential issues and suggesting improvements.

Quick Start

Run the audit skill on the current staged files to perform a code quality assessment.

Frequently Asked Questions about audit

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

FAQPage Schema
How do I automate a comprehensive code quality audit for a large feature branch?▼

Automated code quality auditing leverages specialized AI agents to perform in-depth reviews across your codebase. It scales review complexity based on file count, utilizing sub-reviewers, challengers, and verifiers to ensure high security and performance standards.

What is the best way to run static analysis before an AI code review?▼

Pre-flight static analysis works by automatically detecting and executing project-specific linting and check tools before agent execution. This ensures potential issues are identified early, preparing the codebase for the subsequent multi-stage AI review pipeline.

Does this code review process scale for projects with many modified files?▼

Yes, the code review process scales dynamically based on file count. It transitions from a direct leader review for smaller changes to a full multi-stage pipeline involving sub-reviewers, challengers, verifiers, and integrators for larger projects.

How do expert agents identify security and performance issues during a code audit?▼

Expert agents identify security and performance issues by conducting a tiered review process across modified files. Specialized AI agents analyze the codebase to ensure it meets high standards for security, performance, and readability.

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