Evaluate design effectiveness across visual hierarchy, information architecture, emotional resonance, and user experience quality.
Works with
Assesses interfaces against AI-generated design anti-patterns (color palettes, gradients, glassmorphism, generic layouts) to identify and eliminate \"AI slop\" tells
Evaluates 10 core dimensions: visual hierarchy, information architecture, emotional resonance, discoverability, composition, typography, color strategy, state handling, microcopy, and affordance
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versioncritiqueExecute the skills CLI command in your project's root directory to begin installation:
Fetches critique 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 critique. Access via /critique 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. Additionally gather: what the interface is trying to accomplish.
Conduct a holistic design critique, evaluating whether the interface actually works — not just technically, but as a designed experience. Think like a design director giving feedback.
Evaluate the interface across these dimensions:
This is the most important check. Does this look like every other AI-generated interface from 2024-2025?
Review the design against ALL the DON'T guidelines in the frontend-design skill — they are the fingerprints of AI-generated work. Check for the AI color palette, gradient text, dark mode with glowing accents, glassmorphism, hero metric layouts, identical card grids, generic fonts, and all other tells.
The test: If you showed this to someone and said "AI made this," would they believe you immediately? If yes, that's the problem.
Consult cognitive-load for the working memory rule and 8-item checklist
Structure your feedback as a design director would:
Consult heuristics-scoring
Score each of Nielsen's 10 heuristics 0–4. Present as a table:
| # | Heuristic | Score | Key Issue |
|---|---|---|---|
| 1 | Visibility of System Status | ? | [specific finding or "—" if solid] |
| 2 | Match System / Real World | ? | |
| 3 | User Control and Freedom | ? | |
| 4 | Consistency and Standards | ? | |
| 5 | Error Prevention | ? | |
| 6 | Recognition Rather Than Recall | ? | |
| 7 | Flexibility and Efficiency | ? | |
| 8 | Aesthetic and Minimalist Design | ? | |
| 9 | Error Recovery | ? | |
| 10 | Help and Documentation | ? | |
| Total | ??/40 | [Rating band] |
Be honest with scores. A 4 means genuinely excellent. Most real interfaces score 20–32.
Start here. Pass/fail: Does this look AI-generated? List specific tells from the skill's Anti-Patterns section. Be brutally honest.
A brief gut reaction — what works, what doesn't, and the single biggest opportunity.
Highlight 2–3 things done well. Be specific about why they work.
The 3–5 most impactful design problems, ordered by importance.
For each issue, tag with P0–P3 severity (consult heuristics-scoring for severity definitions):
Consult personas
Auto-select 2–3 personas most relevant to this interface type (use the selection table in the reference). If .github/copilot-instructions.md contains a ## Design Context section from teach-impeccable, also generate 1–2 project-specific personas from the audience/brand info.
For each selected persona, walk through the primary user action and list specific red flags found:
Alex (Power User): No keyboard shortcuts detected. Form requires 8 clicks for primary action. Forced modal onboarding. ⚠️ High abandonment risk.
Jordan (First-Timer): Icon-only nav in sidebar. Technical jargon in error messages ("404 Not Found"). No visible help. ⚠️ Will abandon at step 2.
Be specific — name the exact elements and interactions that fail each persona. Don't write generic persona descriptions; write what broke for them.
Quick notes on smaller issues worth addressing.
Remember:
After presenting findings, use targeted questions based on what was actually found. ask the user directly to clarify what you cannot infer. These answers will shape the action plan.
Ask questions along these lines (adapt to the specific findings — do NOT ask generic questions):
Priority direction: Based on the issues found, ask which category matters most to the user right now. For example: "I found problems with visual hierarchy, color usage, and information overload. Which area should we tackle first?" Offer the top 2–3 issue categories as options.
Design intent: If the critique found a tonal mismatch, ask whether it was intentional. For example: "The interface feels clinical and corporate. Is that the intended tone, or should it feel warmer/bolder/more playful?" Offer 2–3 tonal directions as options based on what would fix the issues found.
Scope: Ask how much the user wants to take on. For example: "I found N issues. Want to address everything, or focus on the top 3?" Offer scope options like "Top 3 only", "All issues", "Critical issues only".
Constraints (optional — only ask if relevant): If the findings touch many areas, ask if anything is off-limits. For example: "Should any sections stay as-is?" This prevents the plan from touching things the user considers done.
Rules for questions:
After receiving the user's answers, present a prioritized action summary reflecting the user's priorities and scope from Phase 3.
List recommended commands in priority order, based on the user's answers:
/command-name — Brief description of what to fix (specific context from critique findings)/command-name — Brief description (specific context)
...Rules for recommendations:
/polish as the final step if any fixes were recommendedAfter 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
/critiqueafter fixes to see your score improve.
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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
I recommend critique for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added critique from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Registry listing for critique matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: critique is the kind of skill you can hand to a new teammate without a long onboarding doc.
Keeps context tight: critique is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend critique for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
critique is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in critique — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Useful defaults in critique — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
critique fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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