mesh-ops

mesh-ops is a skill for Claude Code from dcc-mcp/dcc-mcp-core. It costs 85 tokens per session (1,411 once invoked), scanned A, original, MIT.

A collection of reusable polygon-modeling recipes for digital content creation tools such as Maya or Blender. Each recipe describes validated inputs and ordered modeling operations without depending on one application.

In plain words
What is it for?
It is for creating primitives, editing hard surfaces, cleaning topology, generating UVs, making radial arrays, and assigning materials.
Why use it?
It gives different 3D applications a shared way to describe and validate modeling work before sending operations to an adapter.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Good fit It is for creating primitives, editing hard surfaces, cleaning topology, generating UVs, making radial arrays, and assigning materials.

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Install with agentmods
npx agentmods add skills/dcc-mcp/dcc-mcp-core/mesh-ops
Install

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.

Any agent
npx skills add dcc-mcp/dcc-mcp-core --skill mesh-ops
Clone the repo
git clone --depth 1 https://github.com/dcc-mcp/dcc-mcp-core

Made for: Claude Code.

Wrote 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.

agentmods badge for mesh-ops

README.md
[![agentmods](https://agentmods.dev/badge/skills/dcc-mcp/dcc-mcp-core/mesh-ops/github.svg)](https://agentmods.dev/skills/dcc-mcp/dcc-mcp-core/mesh-ops)
Your own site
<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.

agentmods 80×15 button for mesh-ops

Your own site · 80×15
<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>
Per session 85 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,411 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 7d ago against content hash 704a14dcc6cf, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

skills/mesh-ops/SKILL.md · 135 lines

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

  1. recipes__list with skill: mesh-ops to see the available recipes.
  2. recipes__get a recipe to read its inputs_schema, steps, and output_contract.
  3. recipes__validate candidate inputs against the recipe schema before dispatching.
  4. recipes__apply to obtain the execution plan, then materialize and dispatch the returned steps (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[].tool uses the canonical mesh_ops__<verb> name, never an adapter-specific slug.
  • inputs_schema is a Draft 2020-12 schema validated by Core without a runtime jsonschema dependency.

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. Use tool_routing.scripting_fallback[<adapter>] and project the script result onto the output_contract yourself.

Read the full file on GitHub · 135 lines

Files

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.

Changes

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.

  1. 7d ago Changed · +1 lines 704a14dcc6cf
  2. 18d ago Changed · +5 lines 90924342c9c4
  3. 27d ago First seen · 129 lines · 85 tokens per session scan A f8f064e609bf

Subscribe to this mod's changes

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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