| name | quality-auditor |
| description | Code quality gatekeeper and auditor. Enforces strict quality gates, resolves the AI verification gap, and evaluates codebases across 12 critical dimensions with evidence-based scoring. Use when auditing code quality, reviewing AI-generated code, scoring codebases against industry standards, or enforcing pre-commit quality gates. Use for quality audit, code review, codebase evaluation, security assessment, technical debt analysis. |
| license | MIT |
| metadata | {"author":"oakoss","version":"1.1"} |
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
