Global Rank · of 601 Skills
quality-auditor AI Agent Skill
View Source: oakoss/agent-skills
SafeInstallation
npx skills add oakoss/agent-skills --skill quality-auditor 38
Installs
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
Exploreagent to survey file organization, dependencies, test coverage, and documentation - Execute targeted quality checks across dimensions: Use
Taskagent to run linters, security scanners, performance profilers, and accessibility audits - Design quality improvement roadmap: Use
Planagent 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-slopifyskill.If the
usability-testerskill 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
Installs
Security Audit
View Source
oakoss/agent-skills
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How to use this skill
Install quality-auditor by running npx skills add oakoss/agent-skills --skill quality-auditor in your project directory. Run the install command above in your project directory. The skill file will be downloaded from GitHub and placed in your project.
No configuration needed. Your AI agent (Claude Code, Cursor, Windsurf, etc.) automatically detects installed skills and uses them as context when generating code.
The skill enhances your agent's understanding of quality-auditor, helping it follow established patterns, avoid common mistakes, and produce production-ready output.
What you get
Skills are plain-text instruction files — not executable code. They encode expert knowledge about frameworks, languages, or tools that your AI agent reads to improve its output. This means zero runtime overhead, no dependency conflicts, and full transparency: you can read and review every instruction before installing.
Compatibility
This skill works with any AI coding agent that supports the skills.sh format, including Claude Code (Anthropic), Cursor, Windsurf, Cline, Aider, and other tools that read project-level context files. Skills are framework-agnostic at the transport level — the content inside determines which language or framework it applies to.
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