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 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/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/playcanvas/create-playcanvas/calibrate-model"><img src="https://agentmods.dev/badge/skills/playcanvas/create-playcanvas/calibrate-model.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00032 | $0.00381 |
| Opus 5.5 | $0.00013 | $0.00152 |
| Sonnet 5.5 | $0.00006 | $0.00076 |
| Haiku 4.5 | $0.00003 | $0.00038 |
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 24d 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
What it actually says
Model calibration
Measure each unique GLB once with inspect-glb, only trusting bounds when boundsSource is
vertices, and keep one tuning record per asset:
const ASSET_TUNING = {
model: {
boundsSource: 'vertices', dims: [8, 7, 18], center: [0, 2.5, 0], groundOffset: 1,
intended: { dimension: 'length', size: 18 }, scale: 1, y: 1, yaw: 180
}
} as const;
- Pick the dimension that defines the model's real size, in metres or relative to an already calibrated model.
- Set
scale = intendedSize / measuredDimensionand, for floor-resting models,y = groundOffset * scale. Record a deliberate offset for models that should not rest on the floor. - Confirm facing once in the running app and record
yaw.
Nest three levels so a root position means the same for every asset: the semantic root, a yaw
wrapper, then the visual child with the scale and local position
(-center.x * scale, y, -center.z * scale). Apply calibration once, never per instance.
Place the root on a measured support point, such as a mount point or raycast, not a global AABB maximum. Skinned bounds are bind-pose estimates; confirm contact in animated poses.
Read only the reference matching the code: direct Engine, React, or Web Components.
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.
- 24d ago Changed · -30 lines · -12 tokens per session 95243b0f914a
- 1mo 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 (61 stars, last pushed today), licensed MIT. It adds 32 tokens to every session and 381 once invoked, about $0.0001 per session on Opus 5.5. 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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