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 dcc-mcp/dcc-mcp-core --skill mesh-opsgit clone --depth 1 https://github.com/dcc-mcp/dcc-mcp-coreWrote 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/dcc-mcp/dcc-mcp-core/mesh-ops)<a href="https://agentmods.dev/skills/dcc-mcp/dcc-mcp-core/mesh-ops"><img src="https://agentmods.dev/badge/skills/dcc-mcp/dcc-mcp-core/mesh-ops/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/dcc-mcp/dcc-mcp-core/mesh-ops"><img src="https://agentmods.dev/badge/skills/dcc-mcp/dcc-mcp-core/mesh-ops.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 | $0.00085 | $0.01411 |
| Opus 5.5 | $0.00034 | $0.00564 |
| Sonnet 5.5 | $0.00017 | $0.00282 |
| Haiku 4.5 | $0.00009 | $0.00141 |
Grade A, and why
mesh-ops 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 7d 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.
mesh-ops
Cross-adapter polygon modeling recipes for the shared cross-DCC modeling
vocabulary. Every recipe is a DCC-agnostic plan: a validated inputs_schema,
an ordered list of steps that dispatch the canonical mesh_ops__<verb> tool,
a Draft 2020-12 output_contract, and a toolset_profiles: [mesh-ops]
binding.
How to use
recipes__listwithskill: mesh-opsto see the available recipes.recipes__geta recipe to read itsinputs_schema,steps, andoutput_contract.recipes__validatecandidate inputs against the recipe schema before dispatching.recipes__applyto obtain the execution plan, then materialize and dispatch the returnedsteps(see "Dispatch contract" below).
Cross-adapter contract
dcc: any— a recipe is not tied to one host; the same plan applies in every DCC adapter that implements the shared modeling verb.toolset_profiles: [mesh-ops]— the recipe belongs to the mesh-ops toolset carried by each adapter's mesh-ops skill.steps[].tooluses the canonicalmesh_ops__<verb>name, never an adapter-specific slug.inputs_schemais a Draft 2020-12 schema validated by Core without a runtimejsonschemadependency.
Dispatch contract
recipes__apply returns the plan verbatim. Core does three things only:
validate inputs against inputs_schema, attach the output_contract, and
return the steps. It does not substitute ${param} placeholders and does
not apply inputs_schema defaults. The caller must materialize each step:
step.arguments[k] == "${name}" -> inputs[name] if present
else inputs_schema.properties[name].default
Every placeholder therefore names an input property that is either listed in
required or carries a default, so a materialized step never still contains
a ${...} string. The test suite asserts this for every recipe.
Resolve mesh_ops__<verb> through tool_routing in RECIPES.yaml:
tool_routing.tools[<verb>][<adapter>]— the concrete tool that adapter publishes (blender_mesh_ops__bevel_edges,maya_mesh_ops__mirror_mesh,houdini_materials__assign_material, …).null— the adapter publishes no equivalent. Usetool_routing.scripting_fallback[<adapter>]and project the script result onto theoutput_contractyourself.
What ships with it
1 file 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.
- 7d ago Changed · +1 lines 704a14dcc6cf
- 18d ago Changed · +5 lines 90924342c9c4
- 27d ago First seen · 129 lines · 85 tokens per session scan A f8f064e609bf
mesh-ops is a skill published in the GitHub repository dcc-mcp/dcc-mcp-core (68 stars, last pushed today), licensed MIT. It adds 85 tokens to every session and 1,411 once invoked, about $0.0003 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 2026-09-19.
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