Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add linegel/threejs-complete-set-of-skill --skill threejs-visual-validationgit clone --depth 1 https://github.com/linegel/threejs-complete-set-of-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/linegel/threejs-complete-set-of-skill/threejs-visual-validation)<a href="https://agentmods.dev/skills/linegel/threejs-complete-set-of-skill/threejs-visual-validation"><img src="https://agentmods.dev/badge/skills/linegel/threejs-complete-set-of-skill/threejs-visual-validation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/linegel/threejs-complete-set-of-skill/threejs-visual-validation"><img src="https://agentmods.dev/badge/skills/linegel/threejs-complete-set-of-skill/threejs-visual-validation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00043 | $0.01179 |
| Opus 5 | $0.00022 | $0.00589 |
| Sonnet 5 | $0.00009 | $0.00236 |
| Haiku 4.5 | $0.00004 | $0.00118 |
Grade A, and why
threejs-visual-validation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Visual Validation
Validate claims, not polished frames. Keep each verdict scoped to one declared claim so evidence for appearance cannot substitute for mechanism, timing, or lifecycle proof. The owning subject skill defines mechanism truth and failure signatures; this skill defines how to falsify them.
1. Predeclare every claim
For each claim, record:
- class: visual, mechanism, temporal, performance, GPU attribution, resource, or lifecycle;
- invariant and truth source;
- observable and the diagnostic that isolates it;
- native-domain metric, units, alignment, mask, and aggregation;
- acceptance gate fixed before candidate inspection;
- blocking failure and required evidence.
Label every claim-driving number Authored, Derived, Measured, or Gated
and record its unit and source. Keep unknown values unknown.
This step is complete when every claim has a direct falsifier and no claim is supported only by the final image.
2. Freeze the run
Freeze the exact Three.js revision, renderer and initialized backend, target, browser/GPU, camera matrices, seed, time or deterministic step, viewport, DPR, quality state, assets, and color/output graph. Construct the renderer with timestamp tracking before initialization whenever a declared claim needs GPU timing:
const renderer = new WebGPURenderer( {
trackTimestamp: gpuTimingRequirement === 'required'
} );
await renderer.init();
if ( renderer.backend.isWebGPUBackend !== true ) {
throw new Error( 'Canonical WebGPU validation is unavailable on this target.' );
}
Record the blocker when canonical WebGPU is unavailable. Reach
$threejs-compatibility-fallbacks only when the user explicitly asks for that
branch.
This step is complete when the run can be repeated from the recorded state and initialized backend truth is captured rather than inferred.
3. Capture the producing mechanism
Capture the real pipeline under the frozen state:
- for visual or mechanism claims, the final, no-post, and contribution views required to isolate the claimed cause;
- only the depth, normal, velocity, field, history, mask, resource, or pass diagnostics needed by declared claims;
- for performance, GPU-attribution, resource, or lifecycle claims without a visual/mechanism claim, only the producing trace and diagnostics required by that claim;
- the pass/dispatch ownership graph, including histories and reset edges.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 137 lines · 43 tokens per session scan A 79e7519853f9
threejs-visual-validation is a skill published in the GitHub repository linegel/threejs-complete-set-of-skill (6 stars, last pushed 1mo ago), licensed ISC. It adds 43 tokens to every session and 1,179 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
qa
QA test your code changes by reading your git diff, choosing the right validation path for frontend/browser and backend changes, and reporting pass/fail with evidence.
testing
Verify a Skyvern deployment is working correctly by smoke-testing the backend API, frontend rendering, browser session provisioning, and workflow execution. Use when the user says 'is Skyvern working', 'test my deployment', 'verify the installation', 'smoke test', or needs to check that a self-hosted or local Skyvern…
testing-e2e
End-to-end testing patterns with Playwright — page objects, AI agent testing, visual regression, accessibility testing with axe-core, and CI integration. Use when writing E2E tests, setting up Playwright, implementing visual regression, or testing accessibility.
pi-validation-flow
Use when validating Pi monorepo changes after editing TypeScript, Svelte, package manifests, built-in registry, tests, repo tooling, skills, or docs.
ui-data-testid
Add stable data-testid attributes by default for new or refactored UI components. Use when implementing React/TSX views, shadcn/antd-style components, dropdown/menu configs, or interactive UI flows that need reliable selectors for unit/E2E tests.
kdd-accessibility-scan
Guia a cualquier agente (no depende de ningun modelo puntual) para producir un scan de accesibilidad (WCAG) de un producto en el formato de contrato de Capa 3 de KDD -- findings.json -- que despues gatea scripts/validateaccessibilityfindings.py. Usala cuando se pida auditar accesibilidad de una app/pagina, correr…