Systematic quality audit across accessibility, performance, theming, and responsive design with prioritized findings.
Works with
Scans five dimensions: accessibility (contrast, ARIA, keyboard nav, semantic HTML, alt text, forms), performance (layout thrashing, expensive animations, bundle size, render efficiency), theming (hard-coded colors, dark mode, token consistency), responsive design (fixed widths, touch targets, overflow, text scaling), and AI anti-patterns
Documents issues by severity (Cri
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionauditExecute the skills CLI command in your project's root directory to begin installation:
Fetches audit from pbakaus/impeccable and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate audit. Access via /audit in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Invoke /frontend-design — it contains design principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding — if no design context exists yet, you MUST run /teach-impeccable first.
Run systematic technical quality checks and generate a comprehensive report. Don't fix issues — document them for other commands to address.
This is a code-level audit, not a design critique. Check what's measurable and verifiable in the implementation.
Run comprehensive checks across 5 dimensions. Score each dimension 0-4 using the criteria below.
Check for:
Score 0-4: 0=Inaccessible (fails WCAG A), 1=Major gaps (few ARIA labels, no keyboard nav), 2=Partial (some a11y effort, significant gaps), 3=Good (WCAG AA mostly met, minor gaps), 4=Excellent (WCAG AA fully met, approaches AAA)
Check for:
Score 0-4: 0=Severe issues (layout thrash, unoptimized everything), 1=Major problems (no lazy loading, expensive animations), 2=Partial (some optimization, gaps remain), 3=Good (mostly optimized, minor improvements possible), 4=Excellent (fast, lean, well-optimized)
Check for:
Score 0-4: 0=No theming (hard-coded everything), 1=Minimal tokens (mostly hard-coded), 2=Partial (tokens exist but inconsistently used), 3=Good (tokens used, minor hard-coded values), 4=Excellent (full token system, dark mode works perfectly)
Check for:
Score 0-4: 0=Desktop-only (breaks on mobile), 1=Major issues (some breakpoints, many failures), 2=Partial (works on mobile, rough edges), 3=Good (responsive, minor touch target or overflow issues), 4=Excellent (fluid, all viewports, proper touch targets)
Check against ALL the DON'T guidelines in the frontend-design skill. Look for AI slop tells (AI color palette, gradient text, glassmorphism, hero metrics, card grids, generic fonts) and general design anti-patterns (gray on color, nested cards, bounce easing, redundant copy).
Score 0-4: 0=AI slop gallery (5+ tells), 1=Heavy AI aesthetic (3-4 tells), 2=Some tells (1-2 noticeable), 3=Mostly clean (subtle issues only), 4=No AI tells (distinctive, intentional design)
| # | Dimension | Score | Key Finding |
|---|---|---|---|
| 1 | Accessibility | ? | [most critical a11y issue or "--"] |
| 2 | Performance | ? | |
| 3 | Responsive Design | ? | |
| 4 | Theming | ? | |
| 5 | Anti-Patterns | ? | |
| Total | ??/20 | [Rating band] |
Rating bands: 18-20 Excellent (minor polish), 14-17 Good (address weak dimensions), 10-13 Acceptable (significant work needed), 6-9 Poor (major overhaul), 0-5 Critical (fundamental issues)
Start here. Pass/fail: Does this look AI-generated? List specific tells. Be brutally honest.
Tag every issue with P0-P3 severity:
For each issue, document:
Identify recurring problems that indicate systemic gaps rather than one-off mistakes:
Note what's working well — good practices to maintain and replicate.
List recommended commands in priority order (P0 first, then P1, then P2):
/command-name — Brief description (specific context from audit findings)/command-name — Brief description (specific context)Rules: Only recommend commands from: /animate, /quieter, /optimize, /adapt, /clarify, /distill, /delight, /onboard, /normalize, /audit, /harden, /polish, /extract, /bolder, /arrange, /typeset, /critique, /colorize, /overdrive. Map findings to the most appropriate command. End with /polish as the final step if any fixes were recommended.
After presenting the summary, tell the user:
You can ask me to run these one at a time, all at once, or in any order you prefer.
Re-run
/auditafter fixes to see your score improve.
IMPORTANT: Be thorough but actionable. Too many P3 issues creates noise. Focus on what actually matters.
NEVER:
Remember: You're a technical quality auditor. Document systematically, prioritize ruthlessly, cite specific code locations, and provide clear paths to improvement.
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
shadcn/improve
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Registry listing for audit matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in audit — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Useful defaults in audit — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
audit fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: audit is focused, and the summary matches what you get after install.
audit is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend audit for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
audit reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: audit is the kind of skill you can hand to a new teammate without a long onboarding doc.
audit has been reliable in day-to-day use. Documentation quality is above average for community skills.
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