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/playcanvas/create-playcanvas/calibrate-modelnpx skills add playcanvas/create-playcanvas --skill calibrate-modelgit clone --depth 1 https://github.com/playcanvas/create-playcanvasWrote 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/playcanvas/create-playcanvas/calibrate-model)<a href="https://agentmods.dev/skills/playcanvas/create-playcanvas/calibrate-model"><img src="https://agentmods.dev/badge/skills/playcanvas/create-playcanvas/calibrate-model.svg" alt="Measured on agentmods" 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 | $0.00044 | $0.00746 |
| Opus 5 | $0.00022 | $0.00373 |
| Sonnet 5 | $0.00009 | $0.00149 |
| Haiku 4.5 | $0.00004 | $0.00075 |
Grade A, and why
calibrate-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 6d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- calibrate-model — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model calibration
Use the inspect-glb skill to measure every unique GLB before mass placement. Rely on dims and
groundOffset for contact-accurate placement only when it reports boundsSource: vertices. Store
one tuning record per asset:
const ASSET_TUNING = {
model: {
boundsSource: 'vertices',
aabb: { min: [-4, -1, -9], max: [4, 6, 9] },
dims: [8, 7, 18],
center: [0, 2.5, 0],
groundOffset: 1,
intended: { dimension: 'length', size: 18 },
scale: 0.9,
y: 0.9,
yaw: 180
}
} as const;
Calculate the record
- Pick and record the intended dimension and size: character height, building footprint, or vehicle length. Base it on world units or an already calibrated reference model.
- Calculate
scale = intendedSize / measuredDimension. - For a floor-resting model, calculate
y = groundOffset * scale. Record a deliberate offset for waterlines, embedded objects, or airborne models. - Confirm directional facing once in the running app, as the
apply-conventionsskill describes. PlayCanvas entities face -Z while glTF convention is +Z, but asset packs vary. - Retain
boundsSource,aabb,dims,center,groundOffset, and the intended size with{ scale, y, yaw }. Use the scaled footprint and centre for initial spacing; do not re-derive or add per-instance nudges.
Keep gameplay position and heading on an outer semantic root, and seat the model on one predictable reference point beneath it so a root position means the same thing for every asset: by default the footprint centre over the base. Apply the authored yaw on a wrapper, then the scale and the full offset on the render child inside it, so an off-centre pivot is compensated in the authored frame and never re-rotated by the yaw or by gameplay heading:
const yaw = new Entity('yaw');
yaw.setLocalEulerAngles(0, t.yaw, 0);
const visual = instantiate(asset);
visual.setLocalScale(t.scale, t.scale, t.scale);
visual.setLocalPosition(-t.center[0] * t.scale, t.y, -t.center[2] * t.scale);
yaw.addChild(visual);
root.addChild(yaw);
What ships with it
3 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.
- 6d ago First seen · 66 lines · 44 tokens per session scan A f2297806f5d8
calibrate-model is a skill published in the GitHub repository playcanvas/create-playcanvas (53 stars, last pushed yesterday), licensed MIT. It adds 44 tokens to every session and 746 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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