design-critique
# Design Critique Skill This Claude Code skill delivers structured design feedback using UX frameworks including Jobs-to-be-Done, Gestalt principles, and Nielsen's usability heuristics. It should be used when critiquing UI screens, wireframes, Figma files, user flows, or design systems, requiring the user to provide the design artifact, user goal, platform context, and design stage to generate prioritized, actionable recommendations ranked by impact on user objectives.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills /tmp/design-critique && cp -r /tmp/design-critique/plugins/pm-design/skills/design-critique ~/.claude/skills/design-critiqueSKILL.md
# Design Critique Skill This skill provides structured, actionable design feedback using established UX frameworks. It balances positive observations with clear, prioritised improvement suggestions. ## Required Inputs Ask the user for these if not provided: - **What is being reviewed** (screen, flow, component, full product) - **Design description or attached image** (describe it if no image — the skill will still work) - **User goal** (what is the user trying to accomplish with this design?) - **Context** (web / mobile / desktop app / physical product) - **Stage** (early wireframe / mid-fidelity / high-fidelity / live product) - **Primary concern** (optional — e.g. "I'm worried the onboarding is too long" or "I think the CTA is unclear") ## Output Structure --- # Design Critique: [Design Name or Screen] **User goal:** [What the user needs to accomplish] **Context:** [Platform / Stage] **Critique focus:** [Primary concern if stated, otherwise "full review"] --- ## 1. What's Working [3–5 specific, honest observations about what the design does well. Don't manufacture praise — only include genuine strengths. Be specific: "The visual hierarchy clearly guides the eye from headline → supporting detail → CTA" is useful. "Looks clean" is not.] --- ## 2. Priority Issues Rank issues by impact on the user goal. Use: - 🔴 **High** — Blocks or significantly degrades the user's ability to complete their goal - 🟡 **Medium** — Causes friction or confusion but doesn't block completion - 🟢 **Low** — Polish or preference — nice to fix but not critical For each issue: ### [Priority] Issue [N]: [Short name] **What's happening:** [Describe the specific design problem — be precise about which element, screen, or interaction] **Why it matters:** [Connect to the user goal or a specific principle — don't just say "it's confusing." Say why it creates confusion and what the consequence is for the user.] **Framework reference:** [Name the principle being violated — e.g. Nielsen's Heuristic #6 (Recognition over Recall), Gestalt proximity, JTBD clarity, Fitts's Law, etc.] **Recommendation:** [Specific, actionable suggestion. Not "make the button bigger" but "Increase the primary CTA to at least 44x44px to meet touch target guidelines; consider moving it below the form rather than inline with the input fields to reduce accidental taps."] --- ## 3. Heuristic Assessment Quick assessment against Nielsen's 10 Usability Heuristics — score each as ✅ Pass / 🟡 Partial / ❌ Fail: | Heuristic | Status | Note | |---|---|---| | 1. Visibility of system status | | | | 2. Match between system and real world | | | | 3. User control and freedom | | | | 4. Consistency and standards | | | | 5. Error prevention | | | | 6. Recognition rather than recall | | | | 7. Flexibility and efficiency of use | | | | 8. Aesthetic and minimalist design | | | | 9. Help users recognise, diagnose, and recover from errors | | | | 10. Help and documentation | | | Only include heuristics relevant to what's visible in the design — don't penalise for things not in scope. --- ## 4. Gestalt Principles Check [Comment on any Gestalt principles that are either well-applied or violated:] - **Proximity:** [Are related elements grouped clearly?] - **Similarity:** [Do similar elements look similar?] - **Continuity:** [Does the eye flow naturally through the design?] - **Figure/Ground:** [Is the primary content clearly distinguished from background?] - **Closure:** [Are any implied shapes or containers confusing?] --- ## 5. JTBD Alignment [Assess how well the design serves the stated job-to-be-done:] - **Does the design make the user's primary job obvious?** [Yes / Partially / No — explain] - **Are there any elements that distract from the primary job?** [List any competing CTAs, distractions, or unclear hierarchy] - **What emotional job does this design serve?** [Speed / Confidence / Control / Delight / Other] — and does the visual design match that emotional goal? --- ## 6. Top 3 Recommended Next Steps Prioritised list of the 3 most impactful changes. Each should be actionable in the next design iteration: 1. [Most impactful change — specific] 2. [Second priority] 3. [Third priority] --- ## Quality Checks - [ ] "What's working" includes only genuine, specific observations - [ ] Every issue has a framework reference (not just subjective opinion) - [ ] Recommendations are specific and actionable - [ ] Priority levels (High/Medium/Low) reflect actual impact on user goal - [ ] Heuristic assessment only covers visible elements ## Anti-Patterns - [ ] Do not lead with visual preference (e.g. "I don't like the colour") — every issue must reference a UX principle or user impact - [ ] Do not invent problems in the "What's Working" section — manufactured praise undermines the entire critique - [ ] Do not provide the same priority level (High/Medium/Low) to every issue — prioritisation requires genuine judgment about user impact - [ ] Do not skip the JTBD section for product screens — connecting feedback to the user's job-to-be-done is what separates UX critique from aesthetic opinion - [ ] Do not give recommendations that require a full redesign when the user is in high-fidelity — scope recommendations to the design stage ## Example Trigger Phrases - "Critique this design: [description or image]" - "Give me feedback on this UI/UX" - "Review this Figma screen for usability issues" - "What's wrong with this user flow?" - "Do a heuristic evaluation of [screen/product]"
Conduct a structured ethical review of an AI or ML feature, model, or product. Use when preparing to deploy an AI system, assessing algorithmic risk, auditing a model for bias, or producing a responsible AI impact assessment. Produces a structured ethics review covering fairness, transparency, privacy, safety, accountability, and societal impact with a risk tier score, pre-deployment checklist, and prioritised mitigations.
Structure AI and ML product decisions with the rigour of any product decision. Use when building AI-powered features, evaluating LLM integrations, designing AI products, or assessing AI readiness. Produces a complete AI product canvas covering problem definition, model approach, data requirements, evaluation framework, UX design, responsible AI checklist, and launch monitoring plan.
Transform feature briefs into structured design briefs that give designers the context they need before opening Figma. Use when asked to write a design brief, create a design handoff, brief a designer on a new feature, or translate a PRD into design requirements. Produces a brief with user goal, emotional context, success criteria, constraints, edge cases, and out-of-scope boundaries.
Design statistically rigorous A/B tests and interpret experiment results. Use when asked to design an experiment, run an A/B test, calculate sample size, interpret test results, or assess whether an experiment was successful. Produces a complete experiment design with hypothesis, sample size, run time, success criteria, and risk flags — or a results interpretation with ship/iterate/kill recommendation.
Synthesises user signals from multiple research sources into a unified, weighted insight brief. Use when you have data from interviews, support tickets, NPS verbatims, app reviews, or sales calls and need to reconcile contradictions, surface the underlying need behind requests, or answer 'what are users really telling us'. Produces ranked insights with confidence ratings, source weighting rationale, divergent signal analysis by user segment, and a research gap identification section.
Structure a product data analysis, metric deep-dive, funnel analysis, or cohort study. Use when asked to analyse product metrics, investigate a drop in conversion, explain a data change to stakeholders, or find the root cause of a metric movement. Produces a structured analysis with question, root cause, confidence level, and recommended action.
Interpret product metrics against goals and surface actionable signals. Use when asked to analyse product health, review key metrics, investigate a performance issue, produce a health report, or assess product-market fit signals. Produces a structured health report with RAG status, trend analysis, root cause hypotheses, and prioritised actions.
Structure a retention analysis, churn investigation, or engagement deep-dive for any product team. Use when asked to analyse user retention, investigate churn, measure DAU/MAU, or build a retention improvement plan. Produces a retention snapshot with root cause hypotheses, aha-moment correlation, and prioritised interventions.