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-image-pipelinegit 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-image-pipeline)<a href="https://agentmods.dev/skills/linegel/threejs-complete-set-of-skill/threejs-image-pipeline"><img src="https://agentmods.dev/badge/skills/linegel/threejs-complete-set-of-skill/threejs-image-pipeline/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-image-pipeline"><img src="https://agentmods.dev/badge/skills/linegel/threejs-complete-set-of-skill/threejs-image-pipeline.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 | $0.00056 | $0.01773 |
| Opus 5 | $0.00028 | $0.00886 |
| Sonnet 5 | $0.00011 | $0.00355 |
| Haiku 4.5 | $0.00006 | $0.00177 |
Grade A, and why
threejs-image-pipeline 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 12d 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 — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image Pipeline
Build one causal image graph: one HDR scene pass with depth, selected shared signals, and one final output conversion. Add a scene traversal or attachment only when its measured alternative is worse.
Use the atomic owner for each admitted effect:
$threejs-ambient-contact-shadingfor GTAO and indirect-light composition;$threejs-bloomfor glare source selection andBloomNodecontrols;$threejs-exposure-color-gradingfor metering, adaptation, tone mapping, and LUT domains;$threejs-dynamic-surface-effectsfor feature-local screen history;$threejs-visual-validationfor capture, timing, and lifecycle evidence.
1. Fix the baseline
Declare physical canvas pixels, target browser/GPU, frame budget, primary
visual contract, and a readable no-post view. Initialize one
WebGPURenderer, confirm renderer.backend.isWebGPUBackend, create one
RenderPipeline, and make one pass(scene, camera) own scene-linear HDR plus
its depth texture. Set trackTimestamp before renderer.init() when GPU timing
is requested.
This step is complete when the baseline renders without optional post, the HDR and depth producers are named, and exactly one component owns presentation.
2. Inventory signals
For every candidate signal—HDR color, depth, normal, emissive, velocity, diffuse/base color, IDs, histories, exposure, and UI—record:
writer | readers | mathematical/color domain | physical format and extent
first write -> last read | history/reset owner | disable path
Treat depth as the pass depth texture rather than an MRT color output. Request only signals with a real reader.
This step is complete when every graph edge has one writer, all consumers agree on domain and extent, and every optional signal has a working disable path.
3. Admit attachments
First reject any reconstruction, attachment, or narrow-rerender candidate that cannot meet the signal's declared domain, precision/error bound, spatial coverage, temporal stability, or discard semantics. Compare the remaining correct candidates on the target graph:
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.
- 12d ago First seen · 179 lines · 56 tokens per session scan A 6c7a644f9342
threejs-image-pipeline is a skill published in the GitHub repository linegel/threejs-complete-set-of-skill (6 stars, last pushed 1mo ago), licensed ISC. It adds 56 tokens to every session and 1,773 once invoked, about $0.0003 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.
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