quality-auditor

Quality Auditor

Overview

Evaluates tools, frameworks, systems, and codebases against the highest industry standards across 12 weighted dimensions. Produces evidence-based scores, identifies anti-patterns, and generates prioritized improvement roadmaps. Applies extra scrutiny to AI-generated code through the verification gap protocol, ensuring velocity does not compromise integrity.

When to use: Auditing code quality, reviewing AI-generated code, scoring codebases against industry benchmarks, enforcing pre-commit quality gates, comparing tools or frameworks, assessing technical debt.

When NOT to use: Quick code reviews without scoring, style-only linting (use a linter), feature implementation, routine PR reviews that do not require a full audit.

Quick Reference

Dimension	Weight	What to Evaluate
Code Quality	10%	Structure, patterns, SOLID, duplication, complexity, error handling
Architecture	10%	Design, modularity, scalability, coupling/cohesion, API design
Documentation	10%	Completeness, clarity, accuracy, examples, troubleshooting
Usability	10%	Learning curve, installation ease, error messages, ergonomics
Performance	8%	Speed, resource usage, caching, bundle size, Core Web Vitals
Security	10%	OWASP Top 10, input validation, auth, secrets, dependencies
Testing	8%	Coverage (unit/integration/e2e), quality, automation, organization
Maintainability	8%	Technical debt, readability, refactorability, versioning
Developer Experience	10%	Setup ease, debugging, tooling, hot reload, IDE integration
Accessibility	8%	WCAG compliance, keyboard nav, screen readers, cognitive load
CI/CD	5%	Automation, pipelines, deployment, rollback, monitoring
Innovation	3%	Novel approaches, forward-thinking design, unique value

Audit Phases

Phase	Name	Purpose
0	Resource Completeness	Verify registry/filesystem parity; audit fails if this fails
1	Discovery	Read docs, examine code, test system, review supporting materials
2	Evaluation	Score each dimension with evidence, strengths, and weaknesses
3	Synthesis	Executive summary, detailed scores, recommendations, risk matrix

Scoring Scale

Score	Rating	Meaning
10	Exceptional	Industry-leading, sets new standards
8-9	Excellent	Exceeds expectations significantly
6-7	Good	Meets expectations with improvements needed
5	Acceptable	Below average, significant improvements
3-4	Poor	Major gaps and fundamental problems
1-2	Critical	Barely functional or non-functional

Common Mistakes

Mistake	Correct Pattern
Giving inflated scores without evidence	Every score must cite specific files, metrics, or code examples as evidence
Skipping Phase 0 resource completeness check	Always verify registry completeness first; missing resources cap the overall score at 6/10
Evaluating only code quality, ignoring dimensions	Score all 12 dimensions with appropriate weights; architecture, security, and DX matter equally
Accepting superficial "LGTM" reviews	Perform deep semantic audits checking contract integrity, security sanitization, and performance hygiene
Trusting AI-generated code without verification	Apply the verification gap protocol: critic agents, verifiable goals, human oversight for critical paths
Proceeding after audit failure without re-audit	Stop, analyze the deviation, remediate, then restart the checklist from step 1
Using 10/10 scores without exceptional evidence	Reserve 10/10 for truly industry-leading work; most quality tools score 6-7
Surface-level static analysis only	Combine linting with architectural fit checks, risk-based PR categorization, and context-aware validation

Delegation

• Discover codebase structure and gather audit evidence: Use Explore agent to survey file organization, dependencies, test coverage, and documentation
• Execute targeted quality checks across dimensions: Use Task agent to run linters, security scanners, performance profilers, and accessibility audits
• Design quality improvement roadmap: Use Plan agent to prioritize quick wins, short-term, and long-term recommendations from audit findings

For stylistic cleanup of AI-generated prose and code (emdash overuse, slop vocabulary, over-commenting, verbose naming), use the de-slopify skill.

If the usability-tester skill is available, delegate usability dimension evaluation and user flow validation to it. Otherwise, recommend: pnpm dlx skills add oakoss/agent-skills -s usability-tester -a claude-code -y

References

• Audit Rubric -- pass/warn/fail thresholds, weighted scoring methodology, automated vs manual checklists, score caps, report format
• Dimension Rubrics -- detailed scoring criteria, evidence requirements, and rubric tables for all 12 dimensions
• Audit Report Template -- structured report format, executive summary, recommendations, risk assessment
• Anti-Patterns Guide -- code, architecture, security, testing, and process anti-patterns to identify during audits
• Verification Gap Protocol -- AI code verification methodology, critic agents, rejection protocol, risk-based review strategies