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 scenario-labs/skills --skill scenario-meshygit clone --depth 1 https://github.com/scenario-labs/skillsWrote 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/scenario-labs/skills/scenario-meshy)<a href="https://agentmods.dev/skills/scenario-labs/skills/scenario-meshy"><img src="https://agentmods.dev/badge/skills/scenario-labs/skills/scenario-meshy/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/scenario-labs/skills/scenario-meshy"><img src="https://agentmods.dev/badge/skills/scenario-labs/skills/scenario-meshy.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 | $0.00131 | $0.01660 |
| Opus 5 | $0.00066 | $0.00830 |
| Sonnet 5 | $0.00026 | $0.00332 |
| Haiku 4.5 | $0.00013 | $0.00166 |
Grade A, and why
scenario-meshy 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 11d 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scenario Meshy 3D
Overview
Meshy on Scenario is a toolchain rather than one model: image-to-3D generators (Meshy 7 Image to 3D, Meshy 7 Multi Image to 3D, Meshy T2 Smart Topology), Meshy 6 Text-to-3D, and GLB-in, GLB-out utilities (Retexture, Remesh, UV Unwrap, Rigging, Animation). Work runs as a pipeline: generate a mesh, refine it, then rig or animate, each stage its own model_run whose model parameter takes the 3D asset id the previous stage returned. Discover members with search and treat model_schema_get as the contract: members disagree on defaults as basic as enablePbr (true on Image to 3D, false on Multi Image and Retexture at authoring time).
Connection and the core loop: see the scenario skill in this repo; model-agnostic 3D work (viewer, capture, engine import): the scenario-3d skill. If a sibling skill named here is missing from your available skills, ask the user to install it (npx skills add scenario-labs/skills --skill <name>); unattended, proceed from tool schemas and flag the gap.
Quick reference
Members and their traps (names from the live schema, caps at authoring time):
| Member | Core input | Watch for |
|---|---|---|
| 7 Image to 3D | image (1-4) |
ultraMode takes a single image only; raw mesh by default |
| 7 Multi Image to 3D | image (1-4, first = front view) |
remeshes in-run; savePreRemeshedModel keeps the raw GLB |
| T2 Smart Topology | image (1-4) |
animation-ready topology; targetPolycount caps at 15,000 |
| 6 Text-to-3D | prompt (600 chars) |
shouldRemesh off by default |
| Retexture | model + one style input |
geometry untouched; keep inputs near 30K polys; enableOriginalUv |
| Remesh | model |
targetPolycount 100 to 300,000, resizeHeight, originAt |
| UV Unwrap | model |
rejects meshes above 44,000 faces; Remesh down first |
| Rigging | model, heightMeters |
humanoid skeleton and skin weights |
| Animation | model, actionId |
auto-rigs, then applies the clip |
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.
- 11d ago First seen · 59 lines · 131 tokens per session scan A 3a0301b84b01
scenario-meshy is a skill published in the GitHub repository scenario-labs/skills (11 stars, last pushed today), licensed MIT. It adds 131 tokens to every session and 1,660 once invoked, about $0.0007 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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