How do I install the Mujoco Simulation skill?

Run npx skillmds@latest add baibai2013/mujoco-simulation in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Mujoco Simulation safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Mujoco Simulation?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Mujoco Simulation free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Mujoco Simulation?

baibai2013 (@baibai2013) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Mujoco Simulation by baibai2013 — Agent Skill

Name: Mujoco Simulation
Author: baibai2013

Mujoco Simulation

MuJoCo-oriented robot-dog dynamics validation, MJCF scenario contracts, contact, terrain, actuator, stability, torque, slip, and disturbance checks for virtual prototypes. Use this skill when the user asks for MuJoCo, MJCF, high-fidelity legged-robot simulation, contact/friction validation, slope/step/drop/push scenarios, or serious gait simulation beyond PyBullet smoke tests.

baibai2013 d999b0f 27 files · 34.3 KB Updated Sep 17, 2026

File contents

mujoco-simulation

This skill defines and evaluates MuJoCo-style dynamics scenarios for robot-dog virtual prototypes. The MVP uses deterministic scenario metadata so tests and digital-twin gates can run without a local MuJoCo install. It establishes the file contract that later real mujoco runners will fill with solver output.

When To Use

Use this skill for:

MJCF/MuJoCo scenario planning and validation.
Stand, flat-walk, slope, step-obstacle, drop, and push-disturbance checks.
Contact/friction, foot slip, body roll/pitch, torque margin, and fall checks.
Generating mujoco_result.json for robot-dog-digital-twin gates.

Workflow

Read <project>/mujoco_scenarios.yaml.
Evaluate each scenario against stability, posture, contact, torque, slip, and energy limits.
Write per-scenario *.sim_result.json plus a project-level mujoco_result.json.
Mark blockers when any required scenario falls, exceeds posture/contact limits, or violates torque/slip thresholds.

Commands

python skills/mujoco-simulation/scripts/run_scenarios.py skills/mujoco-simulation/examples/quadruped_mvp
python skills/mujoco-simulation/scripts/summarize_results.py skills/mujoco-simulation/examples/quadruped_mvp

Rules

Keep the MVP deterministic and conservative.
Do not claim metadata-mode results are real MuJoCo solver output.
Use PyBullet simulation for lightweight CI smoke; use this skill for MuJoCo/MJCF contracts and higher-fidelity scenario gates.
Do not import sibling subskill code. Read files only.

References

references/input-contract.md for mujoco_scenarios.yaml fields.
references/scenarios.md for MVP scenario definitions.
references/output-contract.md for report fields.
references/backend-plan.md for moving from metadata backend to real MuJoCo.
references/examples.md for sample project layout.

---name: mujoco-simulationdescription: MuJoCo-oriented robot-dog dynamics validation, MJCF scenario contracts, contact, terrain, actuator, stability, torque, slip, and disturbance checks for virtual prototypes. Use this skill when the user asks for MuJoCo, MJCF, high-fidelity legged-robot simulation, contact/friction validation, slope/step/drop/push scenarios, or serious gait simulation beyond PyBullet smoke tests.---# mujoco-simulationThis skill defines and evaluates MuJoCo-style dynamics scenarios for robot-dogvirtual prototypes. The MVP uses deterministic scenario metadata so tests anddigital-twin gates can run without a local MuJoCo install. It establishes thefile contract that later real `mujoco` runners will fill with solver output.## When To UseUse this skill for:- MJCF/MuJoCo scenario planning and validation.- Stand, flat-walk, slope, step-obstacle, drop, and push-disturbance checks.- Contact/friction, foot slip, body roll/pitch, torque margin, and fall checks.- Generating `mujoco_result.json` for `robot-dog-digital-twin` gates.## Workflow1. Read `<project>/mujoco_scenarios.yaml`.2. Evaluate each scenario against stability, posture, contact, torque, slip, and energy limits.3. Write per-scenario `*.sim_result.json` plus a project-level `mujoco_result.json`.4. Mark blockers when any required scenario falls, exceeds posture/contact limits, or violates torque/slip thresholds.## Commands```bashpython skills/mujoco-simulation/scripts/run_scenarios.py skills/mujoco-simulation/examples/quadruped_mvppython skills/mujoco-simulation/scripts/summarize_results.py skills/mujoco-simulation/examples/quadruped_mvp```## Rules- Keep the MVP deterministic and conservative.- Do not claim metadata-mode results are real MuJoCo solver output.- Use PyBullet `simulation` for lightweight CI smoke; use this skill for MuJoCo/MJCF contracts and higher-fidelity scenario gates.- Do not import sibling subskill code. Read files only.## References- `references/input-contract.md` for `mujoco_scenarios.yaml` fields.- `references/scenarios.md` for MVP scenario definitions.- `references/output-contract.md` for report fields.- `references/backend-plan.md` for moving from metadata backend to real MuJoCo.- `references/examples.md` for sample project layout.

Frequently asked questions