agentleFS
memi-designmemi-design/design-skills/skills/creative-rendering-audit/SKILL.md

creative-rendering-audit

Skill7 starsChanged 4 months agoMITView on GitHub

Audit shaders, GPU-driven effects, dithering, particles, and creative rendering for visual intent, correctness, performance, accessibility, fallbacks, evidence quality, and source licensing.

The file

Show its rules and text

We split this skill into the rules it gives an agent

13 rules. About 0 are common, 12 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈12

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Read shader and host-language source with file and line anchors.

    line 15
  2. Distinctive:

    Use references/platform-evidence.md for surface-specific evidence and failure modes.

    line 21
  3. Distinctive:

    No accessible or unsupported-renderer fallback: maximum 69.

    line 41
  4. Distinctive:

    No rendered runtime evidence: maximum 79.

    line 42
  5. Distinctive:

    No named-device performance evidence for an animated effect: maximum 89.

    line 43
  6. Distinctive:

    Start with a findings table ordered by severity:

    line 49
  7. Very specific:

    Use critical, high, medium, or low.

    line 54
  8. Distinctive:

    Conditional pass: when the bounded effect works but platform, accessibility, or named-device evidence is incomplete.

    line 67
  9. Distinctive:

    Pass: only when all required dimensions have verified evidence and no critical or high finding remains.

    line 68
  10. Distinctive:

    Do not treat a deterministic fixture as live integration proof.

    line 72
  11. Distinctive:

    Do not infer visual quality from a successful build.

    line 73
  12. Distinctive:

    Do not hide performance variance behind an average alone.

    line 74
  13. Distinctive:

    Do not recommend copying restricted reference implementations.

    line 75

≈ “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

Creative Rendering Audit

Audit the supplied implementation and rendered behavior. Keep the audit read-only unless the user separately authorizes fixes.

Workflow

  1. Establish the intended visual behavior, target platforms, supported hardware, quality tiers, fallback, and user-facing acceptance criteria.
  2. Map the rendering path from host inputs to shader stages, textures, render targets, compositing, and final presentation.
  3. Gather evidence:
    • Read shader and host-language source with file and line anchors.
    • Capture compilation, validation, and device-loss messages.
    • Exercise deterministic frames with a fixed seed and clock.
    • Compare the full effect, reduced-motion path, unsupported-renderer path, and static fallback.
    • Profile realistic content at the stated resolution on named hardware.
  4. Review every dimension in references/audit-rubric.md.
  5. Use references/platform-evidence.md for surface-specific evidence and failure modes.
  6. Reproduce or directly observe every high-severity finding. Mark code-only inferences as hypotheses.
  7. Report missing evidence as unassessed. Do not award a pass for a dimension that could not be tested.

Scoring

Score only verified evidence.

DimensionPoints
Visual intent and interaction fit15
Coordinate, sampling, color, and alpha correctness20
Dither and temporal stability15
Performance and resource bounds20
Accessibility, fallback, and failure behavior20
Provenance, licensing, and reproducible evidence10

Apply these caps:

  • Missing or incompatible source or asset license: maximum 49.
  • No accessible or unsupported-renderer fallback: maximum 69.
  • No rendered runtime evidence: maximum 79.
  • No named-device performance evidence for an animated effect: maximum 89.

Include a confidence value derived from assessed points divided by 100. Unknown dimensions score zero and lower confidence.

Required output

Start with a findings table ordered by severity:

SeverityEvidenceFindingUser impactRequired correction

Use critical, high, medium, or low. Cite file:line and attach runtime evidence when available.

Then provide:

  • score, confidence, assessed dimensions, and applied caps;
  • platform, device, resolution, seed, clock, and quality tier;
  • checks that passed with evidence;
  • unassessed behavior and the exact evidence needed;
  • a verdict of Block, Conditional pass, or Pass.

Verdict rules

  • Block for unsafe or incompatible licensing, inaccessible essential content, persistent rendering failure, unbounded sampling, or a reproducible severe performance regression.
  • Conditional pass when the bounded effect works but platform, accessibility, or named-device evidence is incomplete.
  • Pass only when all required dimensions have verified evidence and no critical or high finding remains.

Boundaries

  • Do not treat a deterministic fixture as live integration proof.
  • Do not infer visual quality from a successful build.
  • Do not hide performance variance behind an average alone; include worst observed behavior or a percentile where tooling permits.
  • Do not recommend copying restricted reference implementations.

Maintenance

Review platform evidence and reference links at least every 180 days. Refresh the registry and provenance verification dates only after inspection, then run the repository validator.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.