What makes this audit workflow different from standard linting?

Unlike static linting, this skill uses parallel AI agents to understand architectural context, enforce complex design principles like Single Responsibility, and automatically generate remediation PRs.

How does it prevent duplicate issues?

The workflow includes a dedicated validation phase (Phase 3.5) that scans recently merged PRs and calculates confidence scores to ensure discovered issues haven't already been addressed.

Does it support large codebases?

Yes, it is specifically designed for scale, utilizing codebase division strategies and parallel orchestration to handle large repositories without hitting single-context limitations.

Can I customize the code quality standards?

Yes, you can configure thresholds for module line-of-code (LOC) limits, severity levels, and parallel processing limits to match your team's specific requirements.

Quality Audit Workflow

Name: Quality Audit Workflow
Author: rysweet

byrysweet

•

Security & Testing

Orchestrates end-to-end codebase reviews using parallel agents to identify technical debt, create GitHub issues, and generate automated pull requests.

About

The Quality Audit Workflow is a comprehensive 7-phase agentic system designed to maintain high code standards through automated analysis and remediation. It deploys parallel agents to audit codebase divisions, evaluates them against strict architectural philosophies like Single Responsibility, and automates the entire lifecycle from issue discovery to PR generation. With built-in validation to prevent duplicate work and a Project Manager phase for prioritization, it serves as a powerful tool for large-scale refactoring, technical debt reduction, and codebase health management.

Key Features

Intelligent validation against recent PRs to minimize false positives
AI-driven PR generation in dedicated worktrees for rapid remediation
16 GitHub stars
Automated GitHub issue creation with severity and location metadata
PM-prioritized reporting with dependency mapping and action plans
Parallel agent orchestration for multi-module codebase analysis

Use Cases

Enforcing architectural standards and module size limits across large repositories
Conducting deep technical debt audits on legacy codebases
Scaling code review processes through automated agent-led analysis

About

Key Features

Intelligent validation against recent PRs to minimize false positives
AI-driven PR generation in dedicated worktrees for rapid remediation
16 GitHub stars
Automated GitHub issue creation with severity and location metadata
PM-prioritized reporting with dependency mapping and action plans
Parallel agent orchestration for multi-module codebase analysis

Use Cases

Enforcing architectural standards and module size limits across large repositories
Conducting deep technical debt audits on legacy codebases
Scaling code review processes through automated agent-led analysis