threejs-image-pipeline

threejs-image-pipeline is a skill for Codex from linegel/threejs-complete-set-of-skill. It costs 56 tokens per session (1,773 once invoked), scanned A, original, ISC.

A Three.js WebGPU/TSL guide for organising the complete final-image pipeline, from the HDR scene render through depth, shared signals, effects, and display conversion. HDR stores a wider brightness range than an ordinary image.

In plain words
What is it for?
Use it to choose between multiple render targets, reconstruction, or narrow passes; manage temporal history; and balance image quality, GPU memory, and frame time.
Why use it?
It gives shared render data and final output one clear owner, helping avoid duplicate scene passes, unnecessary attachments, and conflicting post-processing decisions.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: $skill-name invocation.

Good fit Use it to choose between multiple render targets, reconstruction, or narrow passes; manage temporal history; and balance image quality, GPU memory, and frame time.

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Install with agentmods
npx agentmods add skills/linegel/threejs-complete-set-of-skill/threejs-image-pipeline
Install

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.

Any agent
npx skills add linegel/threejs-complete-set-of-skill --skill threejs-image-pipeline
Clone the repo
git clone --depth 1 https://github.com/linegel/threejs-complete-set-of-skill

Made for: Codex.

Wrote 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.

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README.md
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Your own site
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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.

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Your own site · 80×15
<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>
Per session 56 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,773 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 12d ago against content hash 6c7a644f9342, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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.

The scan reads SKILL.md. This mod also ships 1 executable file (examples/rebuild-traa-node.mjs), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/threejs-image-pipeline/SKILL.md · 179 lines

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-shading for GTAO and indirect-light composition;
  • $threejs-bloom for glare source selection and BloomNode controls;
  • $threejs-exposure-color-grading for metering, adaptation, tone mapping, and LUT domains;
  • $threejs-dynamic-surface-effects for feature-local screen history;
  • $threejs-visual-validation for 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:

Read the full file on GitHub · 179 lines

Files

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.

Changes

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.

  1. 12d ago First seen · 179 lines · 56 tokens per session scan A 6c7a644f9342

Subscribe to this mod's changes

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.