MCP Server Builder
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
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…
$ npx skills add VectorSpaceLab/AREX-Skill --skill simulation-rendering -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill simulation-rendering --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "simulation-rendering" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/myosuite/sub-skills/simulation-rendering into .claude/skills/simulation-rendering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "simulation-rendering", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/myosuite/sub-skills/simulation-renderingType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add VectorSpaceLab/AREX-Skill --skill simulation-rendering -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill simulation-rendering --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/repositories/repo-skills/myosuite/sub-skills/simulation-rendering .agents/skills/simulation-rendering && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "simulation-rendering" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/myosuite/sub-skills/simulation-rendering into .agents/skills/simulation-rendering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "simulation-rendering", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add VectorSpaceLab/AREX-Skill --skill simulation-rendering -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill simulation-rendering --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/repositories/repo-skills/myosuite/sub-skills/simulation-rendering .cursor/skills/simulation-rendering && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "simulation-rendering" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/myosuite/sub-skills/simulation-rendering into .cursor/skills/simulation-rendering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "simulation-rendering", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/VectorSpaceLab/AREX-Skill.git --path skills/repositories/repo-skills/myosuite/sub-skills/simulation-rendering--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add VectorSpaceLab/AREX-Skill --skill simulation-rendering -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill simulation-rendering --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/repositories/repo-skills/myosuite/sub-skills/simulation-rendering .gemini/skills/simulation-rendering && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "simulation-rendering" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/myosuite/sub-skills/simulation-rendering into .gemini/skills/simulation-rendering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "simulation-rendering", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install VectorSpaceLab/AREX-Skill simulation-renderingInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add VectorSpaceLab/AREX-Skill --skill simulation-rendering -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/repositories/repo-skills/myosuite/sub-skills/simulation-rendering .github/skills/simulation-rendering && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "simulation-rendering" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/myosuite/sub-skills/simulation-rendering into .github/skills/simulation-rendering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "simulation-rendering", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add VectorSpaceLab/AREX-Skill --skill simulation-rendering -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill simulation-rendering --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/repositories/repo-skills/myosuite/sub-skills/simulation-rendering .opencode/skills/simulation-rendering && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "simulation-rendering" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/myosuite/sub-skills/simulation-rendering into .opencode/skills/simulation-rendering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "simulation-rendering", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
simulation-renderingA 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.
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.
3 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit ac3fe1a. It shows what the files ask for, not the result of running them.
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.
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
pythonFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from VectorSpaceLab/AREX-Skill at commit ac3fe1a, republished under its Apache-2.0 licence (© VectorSpaceLab). 801 words, ~1,832 tokens.
.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.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.
MjSpec editing, site/body edits as a modeling operation, or
IK.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.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.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.
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.For a raw model, the safe contract is:
python scripts/check_mujoco_xml.py \
--xml MODEL.xml --render offscreen --frames 8 \
--width 320 --height 240 --output-dir ./mujoco-checkThe 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.
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.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 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.
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
SKILL.md and 3 other files (scripts, references) in skills/repositories/repo-skills/myosuite/sub-skills/simulation-rendering of VectorSpaceLab/AREX-Skill.
Open the folder on GitHubat commit ac3fe1a
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Simulation Rendering this skillVectorSpaceLab/AREX-Skill | 328 | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| MCP Server Builderanthropics/skills | 180k | 62 repos | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| PDF Processinganthropics/skills | 180k | 48 repos | ~2k | Automated safety check: Pass | Proprietary | |
| NotebookLM Research AssistantPleasePrompto/notebooklm-skill | 7.8k | 13 repos | ~2.4k | Automated safety check: Notes | MIT | |
| Manim Video Productionbrowser-use/video-use | 28k | 6 repos | ~3k | Automated safety check: Pass | MIT | |
| Code Review ChecklistshareAI-lab/learn-claude-code | 78k | 5 repos | ~1.1k | Automated safety check: Pass | MIT |
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
anthropics/skills
Handles everyday PDF jobs in Python and on the command line: extract text and tables, merge, split, rotate, watermark, fill forms, encrypt and OCR.
PleasePrompto/notebooklm-skill
Lets Claude Code ask questions of your Google NotebookLM notebooks through browser automation and return answers grounded in your uploaded sources.
browser-use/video-use
Produces math and technical explainer videos with Manim Community Edition: concept animations, equation derivations, algorithm walkthroughs and data stories.
shareAI-lab/learn-claude-code
Reviews code against a five-part checklist covering security, correctness, performance, maintainability and testing, and reports findings in a fixed format.
hugohe3/ppt-master
Generates editable PowerPoint decks, rebuilds slides from images, fills .pptx templates and polishes existing presentations through routed workflows.
VectorSpaceLab/AREX-Skill
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VectorSpaceLab/AREX-Skill
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VectorSpaceLab/AREX-Skill
Build and debug DB-GPT agents, tools, skills, teams, and AWEL workflows, including deterministic local DAG runs and HTTP-trigger topology without assuming an LLM, credential, or external service.
VectorSpaceLab/AREX-Skill
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VectorSpaceLab/AREX-Skill
A skill your agent uses for giskard.agents async chat workflows, tools, prompt templates, structured outputs, retries, rate limiting, embeddings, and optional LiteLLM backend.
VectorSpaceLab/AREX-Skill
A skill your agent uses for AlphaFold 3 input preparation, prediction command planning, output interpretation, and Python API inspection.
Works with
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.
Simulation Rendering fits situations like: muJoCo model loading; simulation state and time inspection; onscreen rendering; camera/output configuration.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.