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rschuftrschuft/ai-frontend-skill-packs/skills/design-diagnose/SKILL.md

design-diagnose

Skill4 starsChanged 5 days agoMITView on GitHub

Diagnose visual composition and art-direction problems in UI screenshots or code, including hierarchy, color, typography, and layout. Use when a page looks visually inconsistent or unpolished.

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 0 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈0

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Use visual evidence first, not taste labels.

    line 10
  2. Very specific:

    Read references/cross_principle_conflicts.json for at least one relevant tradeoff.

    line 17

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Diagnose visual design issues

Scope

Use visual evidence first, not taste labels. This skill explains why a composition does not convey the intended message. If an observed issue is primarily interaction friction route to ux-diagnose; if it is an accessibility barrier route to wcag-diagnose too.

Procedure

  1. Record the surface and audience, user task, design intent, brand constraints, viewport/theme, and observations. Do not infer click behavior, responsive failures or screen-reader behavior from one image.
  2. Load references/issue_taxonomy.json: select one primary of ten practical repair categories and zero to two secondary ones. Use references/principles.json to associate at most three of the original seven composition principles. references/elements.json helps describe the material causing the issue (color, texture, space, etc.).
  3. Distinguish symptoms from mechanism: for example, "CTA disappears against photo" -> hierarchy/contrast or image treatment, not automatically a color-theory problem. Compare alternatives using the appropriate article summary in references/articles.json and Gestalt concepts where relevant.
  4. Read references/cross_principle_conflicts.json for at least one relevant tradeoff. Filter references/remediation_recipes.json to matching category/observation. Load only the matching design-* specialist's references/playbook.md when implementing.
  5. Propose the smallest coherent repair that preserves the underlying user task. Test at relevant widths/themes, through grayscale, with real copy and content length. Never claim a design "fails" a subjective principle as if it were a technical standard.

Evidence grades and prioritization

  • Confirmed: screenshot or browser/code observation directly supports the issue; observational limits explicitly stated.
  • Hypothesis: design mechanism inferred; discriminate with a screen comparison, brief check, measured contrast or user test.
  • Prioritize a blocked task or inaccessible content over a weak aesthetic; after those, prioritize task-relevant salience, reproducible confusion and cross-screen visual drift over personal preference.

Output

finding ID | observed symptom | evidence/uncertainty | primary category | relevant design principle(s) | user/brand impact | smallest repair | competing tradeoff | acceptance test. For machine-readable records use references/finding.schema.json.

Boundaries

Digital Polo articles provide design heuristics, not normative accessibility standards. User goals, truthful interface state, consent and applicable WCAG criteria outrank aesthetic optimization. If appropriate, activate ux-* and wcag-* alongside this skill. Do not bulk-load the whole article catalog into output.

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