skills/design-evaluation-audit/SKILL.md
Systematically evaluates existing designs against cognitive science principles using repeatable checklists, scoring rubrics, and severity-classified fix recommendations. Use when conducting design reviews or critiques, evaluating designs for cognitive alignment, performing quality assurance before launch, diagnosing usability issues, or choosing between design alternatives with objective criteria.
npx skillsauth add lyndonkl/claude design-evaluation-auditInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use instead of this skill for:
cognitive-designcognitive-design Path 1cognitive-fallacies-guardTime: 30-90 minutes depending on scope
Copy this checklist and track your progress:
Design Evaluation Progress:
- [ ] Step 1: Systematic Assessment
- [ ] Step 2: Visualization Quality Audit (if applicable)
- [ ] Step 3: Severity Classification & Prioritization
- [ ] Step 4: Fix Recommendations
Apply the Cognitive Design Checklist across all 8 dimensions: Visibility, Visual Hierarchy, Chunking, Simplicity, Memory Support, Feedback, Consistency, Scanning Patterns. Check every item. Record pass/fail for each dimension with specific evidence.
Resource: Cognitive Design Checklist
If the design includes data visualizations, apply the 4-Criteria Visualization Audit. Score each criterion 1-5: Clarity, Efficiency, Integrity, Aesthetics. Calculate average and identify weakest dimension.
Resource: Visualization Audit Framework
Classify every finding by severity:
Priority rule: Fix foundation-first — perception before coherence, integrity before aesthetics, critical before high.
For each finding, document:
Verify fixes don't harm other dimensions.
Choose this when: Evaluating any interface, layout, content page, form, or general design.
What you'll get: Pass/fail across 8 cognitive dimensions, test methods, common failures, severity-classified findings.
Time: 20-40 minutes
→ Go to Cognitive Design Checklist
Choose this when: Evaluating data visualizations — charts, graphs, dashboards, infographics.
What you'll get: 1-5 scores on Clarity, Efficiency, Integrity, Aesthetics with pass/fail threshold.
Time: 15-30 minutes per visualization
→ Go to Visualization Audit Framework
Choose this when: Comprehensive review covering both interface elements and data visualizations.
Process: Run Cognitive Checklist first, then Visualization Audit on each data component, merge findings, produce unified fix list.
Time: 45-90 minutes
→ Start with Cognitive Checklist, then Visualization Audit
1. Attention — "Is it obvious what to look at first?"
2. Memory — "Is the user required to remember anything that could be shown?"
3. Clarity — "Can someone unfamiliar understand in 5 seconds?"
All YES = likely cognitively sound. Any NO = run full checklist on the failing area.
Out of scope: Creating designs, teaching theory, providing domain guidance, replacing user testing, or covering full accessibility compliance.
In scope: Systematic evaluation against cognitive principles, severity-classified findings, prioritized fix recommendations, and visualization quality scoring.
testing
Cluster a conference's event records into a small set of coarse themes with finer sub-clusters, an explicit outlier bucket, and soft (multi-membership) affinities — using the hybrid embed-then-label pipeline (embed abstracts, reduce, density-cluster, then LLM-label the clusters) when embedding libraries are available, and an LLM-reasoned hierarchical fallback when they are not. Embeddings do the grouping; the LLM only names the groups. Conference-agnostic. Use when turning structured event records into a navigable theme map for preference elicitation and scheduling, when you need 6-8 reasonable themes rather than 20 muddy ones, or when overlapping talks must belong to more than one theme. Trigger keywords - theme clustering, cluster talks, embed then label, soft membership, outlier talks, conference themes, topic map.
development
Build a personal conference schedule as a constraint-optimization problem — hard constraints (no time overlap, room-to-room travel time, capacity/registration, the attendee's own must-attends and blackouts) plus a user-owned weighted objective trading interest against breadth, pacing (maximize contiguous free time), and serendipity. Surfaces unbreakable conflicts (two high-value overlapping talks the model cannot rank) as decisions for the human rather than silently picking, and reports what each choice traded away. Conference-agnostic. Use to turn a preference profile plus a theme map into a day-by-day plan, to resolve overlapping sessions, or to balance a packed vs paced schedule. Trigger keywords - schedule optimization, conference schedule, constraint optimization, overlapping talks, contiguous free time, conflict surfacing, packed vs paced.
development
Parse a heterogeneous conference program (markdown, HTML, PDF-derived text, or JSON) into normalized event records with per-field confidence scores and independent classification axes (topic, depth, format, prerequisites, recorded, capacity). Detects the program's format before extracting, treats every inferred field as uncertain (present vs inferred vs missing), and flags thin or missing abstracts so downstream enrichment can target them. Conference-agnostic. Use when ingesting a conference or event schedule into a structured store, normalizing a talk/session list, or extracting per-session metadata with calibrated confidence. Trigger keywords - program ingestion, parse schedule, session extraction, event records, conference program, talk metadata, per-field confidence.
development
Build a personalized preference profile from a small number of well-chosen, cluster-grounded questions instead of a long survey. Represents the person's interests as an uncertainty region over the theme map, picks the single highest-information-gain choice-based question (contrasting real talks from different clusters), balances exploiting known interests against exploring uncertain ones, deliberately injects outlier probes to fight selection bias, and stops as soon as the schedule would be stable. Also elicits the user-owned objective weights and hard constraints. Interactive — runs where it can actually ask the person. Conference-agnostic. Use to turn a theme map into a preference profile, to decide what to ask a conference attendee, or to elicit scheduling priorities. Trigger keywords - preference elicitation, ask few questions, information gain, choice-based questions, selection bias probe, objective weights, attendee preferences.