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AI Agents for Codebase Auditing

50+ specialized AI agents that analyze repos and help you find:

  • security vulnerabilities
  • architecture smells
  • testing gaps
  • documentation drift
  • maintainability risks

These agents are organized into 10 analysis phases and are designed to review code, not modify it.

They generate structured findings so you can spot problems faster and decide what to fix next.

What this is NOT

This project is not another agent framework.

Instead of building a generic AI assistant, this repo focuses on very narrow agents that each perform a specific analysis on a project.

Think of them as automated code reviewers.

Phases & Agents

(Ensure code runs and behaves correctly at a basic level)

(Check for unnecessary work and structural cost)

(Ensure scalable, maintainable structure before integration)

(Verify boundaries between systems and data accuracy)

(Check provenance, safety, and license health)

(Validate that builds are reproducible and correct)

(Guardrails for safe, legal, and observable systems)

(Guarantee portability across OS, runtimes, browsers)

(Verify truth, clarity, and user experience)

(Summarize all results for decision-making)

Usage

Each agent is designed to be run independently or as part of a comprehensive audit. They follow a read-only approach - they analyze code and artifacts without making changes, providing detailed reports that developers can use to prioritize improvements.

How It Works

Each agent follows a consistent methodology:

  • Goal: Clear objective of what the agent analyzes
  • Method: Systematic approach to examining code and artifacts
  • What to Look For: Specific patterns and issues to identify
  • Expected Output Format: Structured, readable findings
  • Output Rules: Guidelines for reporting results
  • Severity & Confidence: Framework for prioritizing findings

Key Principles

  • No Code Modification: Agents only read and analyze, never modify
  • Structured Output: Consistent, readable format for all findings
  • Severity & Confidence: Clear prioritization framework
  • Comprehensive Coverage: From syntax to security to user experience
  • Systematic Approach: Each agent has clear methodology and scope

Target Audience

  • Development Teams: Looking to systematically improve code quality
  • DevOps Engineers: Validating build, deployment, and infrastructure practices
  • Security Teams: Identifying vulnerabilities and compliance gaps
  • QA Teams: Enhancing test coverage and documentation accuracy
  • Architects: Ensuring maintainable, scalable system design

Contributing

This repository is designed as a comprehensive toolkit for software quality analysis. Each agent is self-contained with clear documentation and can be extended or customized for specific project needs.

⭐ If you find this project useful, consider starring the repository.


This repository provides a systematic approach to software quality assurance, helping teams identify and address issues across the entire development lifecycle.

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50+ AI agents that audit codebases for security, architecture, testing and documentation issues

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