Agent skill

Simulation Rendering

by VectorSpaceLab in VectorSpaceLab/AREX-Skill

A skill your agent uses for MuJoCo model loading, simulation state and time inspection, headless or onscreen rendering, camera/output configuration, and viewer/display diagnostics; route environment…

Apache-2.0Auto-check passed

Install Simulation Rendering

skills CLI
$ npx skills add VectorSpaceLab/AREX-Skill --skill simulation-rendering -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install VectorSpaceLab/AREX-Skill simulation-rendering --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/repositories/repo-skills/myosuite/sub-skills/simulation-rendering .claude/skills/simulation-rendering && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
simulation-rendering
GitHub stars
328
Token cost
~1.8k tokens
SKILL.md length
801 words
Files
4 (incl. scripts, references)
Skills in repo
159
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses for MuJoCo model loading, simulation state and time inspection, headless or onscreen rendering, camera/output configuration, and viewer/display diagnostics; route environment…

  • Works in 3 steps: Headless or CI: load… → A window is explicitly wanted:… → Raw XML diagnosis: run
  • MuJoCo model loading
  • SKILL.md covers Route first, Fast decision: which rendering…, Environment rendering contract and Deterministic headless rollout, plus 3 more sections
  • Runs Python scripts from its folder; calls python

What it does

Simulation Rendering is an agent skill from VectorSpaceLab/AREX-Skill. Use for MuJoCo model loading, simulation state and time inspection, headless or onscreen rendering, camera/output configuration, and viewer/display diagnostics; route environment catalog, XML editing/IK, MJX acceleration, and training elsewhere.

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/rendering-api.md`, `references/troubleshooting.md` and `scripts/check_mujoco_xml.py`).

It works with Python. The repository describes itself as: A Skill Library for Automated Machine Learning. The licence is Apache-2.0.

When your agent uses it

  • MuJoCo model loading
  • Simulation state and time inspection
  • Onscreen rendering
  • Camera/output configuration

Example prompts

  • “/simulation-rendering”

Requirements

  • Python 3

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. Headless or CI: load mujoco.MjModel/mujoco.MjData, step with
  2. A window is explicitly wanted: env.mj_render() delegates to
  3. Raw XML diagnosis: run

What it can do on your machine

Read from SKILL.md and the folder at commit ac3fe1a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Simulation Rendering loads about 1.8k tokens when it runs, and up to ~6.1k if it reads all its reference files. Until then it costs about 67 tokens; SKILL.md has 801 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~67
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from VectorSpaceLab/AREX-Skill at commit ac3fe1a, republished under its Apache-2.0 licence (© VectorSpaceLab). 801 words, ~1,832 tokens.

Download SKILL.mdSave it as .claude/skills/simulation-rendering/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
simulation-rendering
description
Use for MuJoCo model loading, simulation state and time inspection, headless or onscreen rendering, camera/output configuration, and viewer/display diagnostics; route environment catalog, XML editing/IK, MJX acceleration, and training elsewhere.
disable-model-invocation
true
metadata.disco-role
operating
license
Apache 2.0

Simulation and rendering

Use this sub-skill when the task concerns the MuJoCo model/data handles behind a MyoSuite environment, rendering pixels or depth, selecting cameras, preserving or restoring simulator state, or diagnosing a viewer/display failure. It covers the base MuJoCo backend in MyoSuite 2.x. It does not teach task selection, XML mutation or IK, MJX/JAX acceleration, or RL training.

Route first

  • Choose environment workflows for registry lookup, task configuration, reset/step semantics, or environment-specific observation and reward behavior.
  • Choose model editing and kinematics for XML changes, MjSpec editing, site/body edits as a modeling operation, or IK.
  • Choose MJX acceleration for JAX, MJX, CUDA, batching, or accelerator performance.
  • Choose training integration for policies, learners, checkpoints, or long-running experiments.

Fast decision: which rendering path?

  1. Headless or CI: load mujoco.MjModel/mujoco.MjData, step with mujoco.mj_step, and use MJRenderer.render_offscreen(...), or run the bundled safe checker: python scripts/check_mujoco_xml.py --help.
  2. A window is explicitly wanted: env.mj_render() delegates to env.mj_renderer.render_to_window(), which creates a native passive viewer on first use. This is display-dependent and is not verification ground truth.
  3. Raw XML diagnosis: run python scripts/check_mujoco_xml.py --xml MODEL.xml --render none first. It loads and steps without creating a viewer. Add --render offscreen only when pixel output is required.

Never use myosuite.utils.examine_sim as a headless check: its documented workflow calls mujoco.viewer.launch(...) and is intentionally onscreen. Its safe native candidate is python -m myosuite.utils.examine_sim --help only.

Environment rendering contract

python
from myosuite.utils import gym

env = gym.make("myoElbowPose1D6MRandom-v0")
env.reset(seed=1234)
frame = env.mj_renderer.render_offscreen(
    width=320, height=240, camera_id=-1, rgb=True
)
env.close()
  • env.mj_model is the compiled mujoco.MjModel; env.mj_data is its live mujoco.MjData. The base environment constructs env.mj_renderer as an MJRenderer for those handles.
  • camera_id=-1 means MuJoCo's free camera. A named camera string or numeric camera id selects a model camera. Use a model camera name only after checking that it exists; a missing name is a model/rendering error, not a display fix.
  • render_offscreen returns an RGB numpy array for the default call. The detailed return matrix for RGB/depth/segmentation is in rendering-api.md.
  • env.mj_render() is deliberately different: it opens/synchronizes a window and returns no frame. Do not call it in a server, CI job, or headless synthetic case.
  • env.viewer_setup(distance=..., azimuth=..., elevation=..., lookat=..., render_actuator=..., render_tendon=...) stores free-camera and visualization settings for the next window or offscreen scene. Camera/output details and the distance adjustment are documented in rendering-api.md.

Deterministic headless rollout

For a raw model, the safe contract is:

bash
python scripts/check_mujoco_xml.py \
  --xml MODEL.xml --render offscreen --frames 8 \
  --width 320 --height 240 --output-dir ./mujoco-check

The command never calls mujoco.viewer or launch_passive. It prints model sizes, steps the requested number of frames, and writes one binary PPM image per rendered frame under the supplied output directory (for example, mujoco-check/frame-0000.ppm). --render none is the default and creates no output directory. The script accepts optional comma-separated --qpos and --ctrl; it validates their lengths before applying them. See the script's --help output for the complete input/output contract.

For a policy/environment rollout, use MujocoEnv.examine_policy_new only when its policy API is already available. Its supported render values are "onscreen", "offscreen", and "none"; offscreen mode accumulates RGB frames and writes an MP4 per episode through imageio. Set an explicit output_dir, filename, frame_size, and finite horizon. The safe raw-model script is preferred for a small, dependency-light rendering check.

Show full SKILL.md (305 more words)Show less

State and time workflow

  • env.dt is env.mj_model.opt.timestep * env.frame_skip.
  • env.time reads the observed simulation data time (env.obsd_mj_data.time), so compare it with env.mj_data.time only when the environment uses the same observed and ground-truth model.
  • env.get_env_state() returns copied time, qpos, qvel, optional act, optional mocap position/quaternion, optional site position/quaternion, and body position/quaternion arrays.
  • Save the returned dictionary before a branch, then call env.set_env_state(state) to restore the base state. The method updates both ground-truth and observed data where present and performs a MuJoCo step to refresh derived state; treat restoration as a simulator-state operation, not as a byte-for-byte snapshot of every internal buffer.
  • env.reset(...) establishes the task's initial state. After changing raw qpos/qvel, use mujoco.mj_forward(model, data) before inspecting derived positions or rendering. Do not edit model arrays merely to move a body unless the task is explicitly a model-editing task.

Visual observations

Visual observations are opt-in. Call env.get_obs(update_exteroception=True) or env.get_visuals(...) when the environment has configured visual_keys. Supported key forms are rgb:CAMERA:HxW:1d, rgb:CAMERA:HxW:2d, and optional encoder forms such as r3m18, r3m34, r3m50, rrl, or vc1 when their optional encoder dependencies are installed. A matching d: key requests depth alongside the RGB key. get_obs() does not refresh exteroception by default, and env_info["visual_dict"] may therefore be empty or stale unless visuals were explicitly updated at the current simulation time.

Backend and safety boundary

Base MuJoCo model loading, stepping, state inspection, and the MJRenderer offscreen path are CPU/base-package capabilities, subject to a usable graphics backend for pixel rendering. Onscreen viewing additionally requires a display and native viewer support. MJX/JAX/CUDA is optional and is not established by an offscreen CPU check; route it to the MJX sub-skill and report it separately.

Before reporting success, check the actual artifact: model load/step succeeded, rendered arrays have the requested dimensions, and every requested output file exists and is non-empty. For failure symptoms and recovery branches, use troubleshooting.md.

© VectorSpaceLab, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in skills/repositories/repo-skills/myosuite/sub-skills/simulation-rendering of VectorSpaceLab/AREX-Skill.

  • SKILL.md
  • references/rendering-api.md
  • references/troubleshooting.md
  • scripts/check_mujoco_xml.py

Open the folder on GitHubat commit ac3fe1a

Compare with similar skills

Simulation Rendering next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Simulation Rendering this skillVectorSpaceLab/AREX-Skill328—~1.8kAutomated safety check: PassApache-2.0
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PDF Processinganthropics/skills180k48 repos~2kAutomated safety check: PassProprietary
NotebookLM Research AssistantPleasePrompto/notebooklm-skill7.8k13 repos~2.4kAutomated safety check: NotesMIT
Manim Video Productionbrowser-use/video-use28k6 repos~3kAutomated safety check: PassMIT
Code Review ChecklistshareAI-lab/learn-claude-code78k5 repos~1.1kAutomated safety check: PassMIT

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Works with

Questions about Simulation Rendering

What does Simulation Rendering do?

A skill your agent uses for MuJoCo model loading, simulation state and time inspection, headless or onscreen rendering, camera/output configuration, and viewer/display diagnostics; route environment…. Simulation Rendering is an agent skill from VectorSpaceLab/AREX-Skill. Use for MuJoCo model loading, simulation state and time inspection, headless or onscreen rendering, camera/output configuration, and viewer/display diagnostics; route environment catalog, XML editing/IK, MJX acceleration, and training elsewhere.

When should I use Simulation Rendering?

Simulation Rendering fits situations like: muJoCo model loading; simulation state and time inspection; onscreen rendering; camera/output configuration.

How do I install Simulation Rendering in Claude Code?

Run `npx skills add VectorSpaceLab/AREX-Skill --skill simulation-rendering -a claude-code`. Or copy the skill folder (skills/repositories/repo-skills/myosuite/sub-skills/simulation-rendering in VectorSpaceLab/AREX-Skill) into .claude/skills/simulation-rendering in your project. Claude Code loads it when a task matches its description.

How do I install Simulation Rendering in Codex?

Run `npx skills add VectorSpaceLab/AREX-Skill --skill simulation-rendering -a codex`. Or copy the skill folder (skills/repositories/repo-skills/myosuite/sub-skills/simulation-rendering in VectorSpaceLab/AREX-Skill) into .agents/skills/simulation-rendering in your project. Codex loads it when a task matches its description.

Can I use Simulation Rendering in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add VectorSpaceLab/AREX-Skill --skill simulation-rendering -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/simulation-rendering, .gemini/skills/simulation-rendering, .github/skills/simulation-rendering and .opencode/skills/simulation-rendering in your project.

What does Simulation Rendering need to run?

Going by SKILL.md and its folder, Simulation Rendering needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Simulation Rendering access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Simulation Rendering safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Simulation Rendering use?

Simulation Rendering is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Simulation Rendering use?

About 1.8k tokens (SKILL.md is roughly 7.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.3k tokens, read only when the agent opens those files.

What are the alternatives to Simulation Rendering?

Skills that share tags, products or a category with Simulation Rendering: MCP Server Builder (anthropics/skills, 180k stars), PDF Processing (anthropics/skills, 180k stars), NotebookLM Research Assistant (PleasePrompto/notebooklm-skill, 7.8k stars) and Manim Video Production (browser-use/video-use, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Simulation Rendering?

VectorSpaceLab (a GitHub organization) maintains it in VectorSpaceLab/AREX-Skill, which has 328 GitHub stars. The repository holds 159 skills in this directory. The repository was last updated on September 3, 2026.

Source: VectorSpaceLab/AREX-Skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.