A robotics simulation setup built around MuJoCo, a physics engine for testing robot movement and contact. It covers single-arm grasping, MJCF robot models, inverse kinematics, and repeatable camera rendering without ROS.

Use it to build and test simulated pick-and-place environments, calculate arm movement, render camera data for policies or datasets, and calibrate grasps.

It helps test manipulation tasks without running a full robotics stack or requiring a high-end graphics simulator. It also addresses rendering and grasp-calibration problems that can otherwise be difficult to diagnose.

mujoco — Skill for Claude Code by robium-ai/robium

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 robium-ai/robium --skill mujoco

Clone the repo

git clone --depth 1 https://github.com/robium-ai/robium

Made for: Claude Code.

Or install robium, the plugin that ships this one along with the rest of its 27 skills, 1 agent, 7 hooks.

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/robium-ai/robium/mujoco/github.svg)](https://agentmods.dev/skills/robium-ai/robium/mujoco)

Your own site

<a href="https://agentmods.dev/skills/robium-ai/robium/mujoco"><img src="https://agentmods.dev/badge/skills/robium-ai/robium/mujoco/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/robium-ai/robium/mujoco"><img src="https://agentmods.dev/badge/skills/robium-ai/robium/mujoco.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 648 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. Third-party audits

NVIDIA SkillSpector warn 7 Sept 2026

SkillSpector: 6 findings, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

high Prompt Injection · line 70
Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.

Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
high Prompt Injection · line 87
Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.

Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
high Prompt Injection · line 129
Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.

Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
high Prompt Injection · line 141
Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.

Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
high Prompt Injection · line 156
Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.

Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
high Prompt Injection · line 191
Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.

Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.

How audits are shown

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.

Model	Per session	Once invoked
Fable 5.1	$0.00026	$0.00648
Opus 5.5	$0.00010	$0.00259
Sonnet 5.5	$0.00005	$0.00130
Haiku 4.5	$0.00003	$0.00065

Measured 1mo ago against content hash c2d79b9d5c4f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

mujoco 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 1mo 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/mujoco/SKILL.md · 62 lines

How it starts

The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.

MuJoCo

A plausible render proves little by itself. Follow the physical chain from model through kinematics, actuation, contact, and observation.

Start from the model

For a first interactive tryout, new app, or first policy demo, read architect before creating a scene, controller, or viewer or downloading a model collection. Reuse its compatible reference-app selection if already made. Existing-model edits, physics debugging, and explanations stay here.
Read the MJCF and the pinned asset revision before adding control code. Check joint ranges, actuator limits, collision geometry, sites, masses, and the intended work surface.
Prefer a maintained model from MuJoCo Menagerie, but verify it against the real robot and task envelope.
Confirm gripper polarity, fingertip gap, and contact geometry empirically. Names and documentation can disagree with the model that actually runs.
Use the current MuJoCo documentation for MJCF and Python APIs rather than carrying signatures forward from memory.

Follow the physical chain

Kinematics: solve only for reachable targets and check the residual; damped least-squares can return a poor local solution without raising.
Actuation: compare commanded position or torque with joint state, actuator force, range limits, and saturation.
Contact: inspect which geoms belong to the gripper and object. Unnamed mesh geoms make name-only contact filters unsafe.
Grasp: calibrate the grasp point, approach path, wrist orientation, and lift together. The end-effector site is not automatically the physical pinch point.
Observation: make cameras and renderer lifecycle deterministic before using frames as training or regression data.
Controls: distinguish model state, actuator limits, and rounded UI ranges. Clamp reset values to the actual widget bounds before binding them; a physically valid state can still be rejected by a narrower control.

Read the full file on GitHub · 62 lines

Files

What ships with it

2 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.

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.

1mo ago Changed · +1 lines · +10 tokens per session c2d79b9d5c4f
1mo ago Changed · +4 lines ff4c6957190a
1mo ago Changed · +3 lines 7720668c8691
2mo ago Changed · -207 lines · -246 tokens per session 29b0e456d51e
2mo ago First seen · 261 lines · 262 tokens per session scan A f9b1fca694d1

Subscribe to this mod's changes

mujoco is a skill published in the GitHub repository robium-ai/robium (108 stars, last pushed 1mo ago), licensed MIT. It adds 26 tokens to every session and 648 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 2026-09-03.

Claude Code Hardware and IoT Scientific agent-skills ai-agents gazebo isaac-sim lerobot

mujoco next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
mujocorobium-ai/robium	Skill	108	1mo ago	MIT	A	26	Claude Code
urdf-to-mjcfPathOn-AI/PathOnRoboticsOSS	Skill	153	2d ago	none	A	37	Claude Code
ruview-applicationsruvnet/RuView	Skill	103k	today	MIT	A	79	Claude Code
lab-hardware-cadK-Dense-AI/scientific-agent-skills	Skill	56k	6d ago	MIT	A	108	Claude Code
calibrate-roomruvnet/RuView	Skill	103k	today	MIT	A	41	Claude Code
cdf-simulationNVIDIA/skills	Skill	3.9k	2d ago	Apache-2.0	A	117	Claude Code, Codex
i4h-catheter-navigationNVIDIA/skills	Skill	3.9k	2d ago	Apache-2.0	A	55	Claude Code, Codex