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 agentmods add skills/w1ne/kernelcad-web/blockout-modelnpx skills add w1ne/kernelCAD-web --skill blockout-modelgit clone --depth 1 https://github.com/w1ne/kernelCAD-webWhat 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 | $0.00046 | $0.01188 |
| Opus 5 | $0.00023 | $0.00594 |
| Sonnet 5 | $0.00009 | $0.00238 |
| Haiku 4.5 | $0.00005 | $0.00119 |
Grade A, and why
blockout-model 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 3d 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
blockout-model
Purpose
The blockout pass produces a coarse, parametric approximation of the object's primary masses. It is not a finished model — it is a testable hypothesis: "does this bounding envelope match the reference silhouette in all canonical views?" Only after the blockout is plausible does detail work begin.
Inputs
- The
// Real Object Brieffromprepare-prompt/SKILL.md. - The reference photo path.
What to do
Step 1 — Declare key params
Map each dimension in the brief's Scale section to a param(). Use the brief's
estimated value as the default:
const frameW = param('frameWidth', 130, { min: 100, max: 160, description: 'outer frame width mm' });
const frameH = param('frameHeight', 48, { min: 30, max: 70 });
const bodyD = param('bodyDepth', 5, { min: 2, max: 12 });
const bridgeW = param('bridgeWidth', 18, { min: 10, max: 30 });
All key dimensions must be params, not magic numbers. The blockout must be adjustable after the first canonical-view check without touching geometry code.
Step 2 — Place the reference image overlay
Pick the view plane that shows the most information. For most flat products, the front view is the XZ plane:
referenceImage('./reference.jpg', {
plane: 'xz',
anchor: 'origin',
scale: 'fit-bbox',
opacity: 0.35,
});
The reference image does not affect the score (it is hidden during scoring). It is for visual alignment during authoring.
Step 3 — Build primary mass boxes
One box() or extrudeRoundedRect() per major component. No holes, no
features, no material yet. Translate each into position. For symmetric objects,
build one half only and call .mirror() after the blockout check:
// primary lens+frame slab
const frameBody = extrudeRoundedRect(frameW, frameH, bodyD, 4)
.translate(-frameW / 2, 0, -frameH / 2);
// arm stubs — one side, to be mirrored
const armStub = box(50, armW, armT)
.translate(frameW / 2, 0, -armT / 2);
const full = frameBody.union(armStub.mirror('YZ'));
return full;
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.
- 3d ago First seen · 135 lines · 46 tokens per session scan A 2895052b25f0
blockout-model is a skill published in the GitHub repository w1ne/kernelCAD-web (17 stars, last pushed 5d ago), licensed MIT. It adds 46 tokens to every session and 1,188 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 2026-08-30.
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