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Heuristic

Skill educlopez/ui-craft/cli/assets/gemini/skills/heuristic

Design engineering system for AI coding agents — ship UI with craft-level quality. Install as an agent skill.

Install
npx -y skills add educlopez/ui-craft --skill heuristic

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

Copied from the file, not written here

Produce a scored heuristic critique of the UI using Nielsen's 10 + 6 design laws + optional persona walkthroughs. Outputs a machine-parseable scorecard plus a 0-100 UsabilityScore (the judged companion to the deterministic UICraftScore). Invoke when the user asks for heuristic on their UI, or mentions 'heuristic' alongside design / UI / frontend work.

SKILL.md

4.1 KB, 979 tokens by cl100k_base, as published. Nobody here has run it

<!-- HARNESS MIRROR — do not edit here. Canonical source: skills/ or commands/. After editing source, copy into cli/assets/<harness>/ and repo-root harness mirrors. -->

Context: this sub-skill is one lens of the broader ui-craft skill. If the ui-craft skill is also installed, read its SKILL.md first for Discovery + Anti-Slop + Craft Test, then apply the specific lens below.

Score the UI at $ARGUMENTS against Nielsen's 10 + 6 design laws. Load the ui-craft skill.

Step 1 — Load the methodology. Read references/heuristics.md for the full rubric, scoring definitions, design law details, and the required output format. Do NOT invent a new format or a new scale.

Step 2 — Walk Nielsen's 10 heuristics. Score each 1-5 per the rubric:

  • 1 blocks users · 2 severe friction · 3 works but confusing · 4 works, minor polish · 5 best-in-class

For every heuristic, write a concrete finding — quote text, count elements, name the broken flow. Vague findings are rejected.

Step 3 — Audit the 6 design laws. PASS / FAIL each with a specific detail:

  • Fitts's Law — touch target sizing, CTA placement
  • Hick's Law — choice density, nav + select sizing
  • Doherty Threshold — perceived latency, optimistic UI
  • Cleveland-McGill — chart encoding choice
  • Miller's Law — nav depth, form section counts
  • Tesler's Law — where complexity lives

Step 4 — Persona walkthrough (if --persona= present). If the args include --persona=<name>, load references/personas.md and run the matching walkthrough checklist. Supported: priya, jordan, adaeze, kwame, margo, all. Output the walkthrough as a | Checklist item | Pass/Fail | Finding | Impact | table. Without the flag, skip this step.

Step 5 — Rank findings by impact tag. Impact order: blocks-conversion > adds-friction > reduces-trust > minor-polish. Include at most 5 findings in the ranked list; cut anything at minor-polish unless there are no higher-impact findings.

Step 6 — Compute the UsabilityScore. Roll the scorecard into a 0-100 number + grade per the UsabilityScore formula in references/heuristics.md: heuristic_base = round(((mean(nielsen_scores) − 1) / 4) × 100), minus 5 × (failed design laws), clamped [0,100]. Same A/B/C/D/F bands as UICraftScore. Always label it (judged) — it is not deterministic and must never gate CI. If the args include --json, also emit the machine-readable block. If the user asks for the full picture, build the Extended quality report by fetching the deterministic UICraftScore (node scripts/eval.mjs <path> --json or the score_ui MCP tool) and placing both side by side — never average them.

Step 7 — Output. Use the exact scorecard format in references/heuristics.md:

  1. ## Heuristic Scorecard table
  2. ## Design Law Audit table
  3. ## Persona Walkthrough table (only if --persona= was passed)
  4. ## Top findings (ranked by impact) — numbered list, 3-5 items
  5. ## UsabilityScore block — the 0-100 score + grade + component breakdown

Knob awareness: knob-agnostic. Usability is not a knob — a 2 is a 2 whether CRAFT_LEVEL is 3 or 9. Do not soften scores based on CRAFT_LEVEL.

Output contract:

  • This command produces a critique artifact, not code. No edits unless the user explicitly asks in a follow-up.
  • The scorecard is machine-parseable markdown. A PM can paste it into any issue tracker and file tickets row-by-row. Frame it that way in any preamble.
  • No "First Impressions" paragraph, no hedging, no praise padding. Tables + ranked list only.

Do NOT edit code. This is a scored critique.

Next step: Fix the findings, then /finalize — the scorecard is the input to a gated ship (rung 3).

Gives 0 of the 12 instructions most design frontend skills give in 979 tokens

Counted across 1,169 of the 1,878 authors here whose files we hold, read 2026-08-07

  • use css variables for color consistencyin 72 of 1169, across 23 files
  • commit to one bold aesthetic direction before codingin 72 of 1169, across 27 files
  • match implementation complexity to the aesthetic visionin 70 of 1169, across 20 files
  • add atmospheric background effects and texturesin 57 of 1169, across 9 files
  • use unexpected spatial compositions and layoutsin 56 of 1169, across 8 files
  • implement real working codein 55 of 1169, across 7 files
  • vary themes and aesthetics across different designsin 48 of 1169, across 7 files
  • launch chromium in headless modein 47 of 1169, across 4 files
  • close the browser when donein 47 of 1169, across 4 files
  • run provided scripts with help flag firstin 47 of 1169, across 4 files
  • wait for network idle statein 47 of 1169, across 4 files
  • use descriptive selectors for elementsin 47 of 1169, across 4 files

Said here and by no other author read

  • Read the ui-craft skill first
  • Write one concrete finding per heuristic
  • Audit the UI against six design laws
  • Run persona walkthrough if requested
  • Compute the 0-100 judged UsabilityScore
  • Output the exact scorecard format

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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