Agent skill

Mjlab Skillkit

by cmjang in cmjang/mjlab-skillkit

A skill your agent uses when asked to migrate IsaacLab projects to mjlab, compare IsaacLab and mjlab APIs, import meshes into mjlab tasks, or author new mjlab-native tasks/components directly from…

MITAuto-check passedAI & LLM Engineering

Install Mjlab Skillkit

skills CLI
$ npx skills add cmjang/mjlab-skillkit --skill mjlab-skillkit -a claude-code

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

GitHub CLI
$ gh skill install cmjang/mjlab-skillkit mjlab-skillkit --agent claude-code

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

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

Facts

Skill name
mjlab-skillkit
GitHub stars
104
Token cost
~3.1k tokens
SKILL.md length
1,334 words
Files
40 (incl. scripts, references)
Skills in repo
1
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when asked to migrate IsaacLab projects to mjlab, compare IsaacLab and mjlab APIs, import meshes into mjlab tasks, or author new mjlab-native tasks/components directly from…

  • Works in 6 steps: Confirm whether the task is migrate or… → Resolve references in this order: target… → Read references/mjlab-api-pack.md and… → …
  • Asked to migrate IsaacLab projects to mjlab
  • SKILL.md covers Skill Suite, Official Repositories, Documentation Priority and Reference Resolution Order, plus 10 more sections
  • Reaches github.com

What it does

Mjlab Skillkit is an agent skill from cmjang/mjlab-skillkit. Use when asked to migrate IsaacLab projects to mjlab, compare IsaacLab and mjlab APIs, import meshes into mjlab tasks, or author new mjlab-native tasks/components directly from local or bundled mjlab docs and examples. This skillkit includes an IsaacLab Migration Skill and an mjlab Native Skill. Prefer mjlab public APIs, preserve behavior in migration mode, and avoid compatibility layers.

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 46 other files, including scripts and reference files (for example `README.md`, `adapters/claude/README.md` and `adapters/gemini/README.md`).

It sits in AI & LLM Engineering. The repository describes itself as: AI coding skill kit for IsaacLab migration and mjlab-native development. The licence is MIT.

When your agent uses it

  • Asked to migrate IsaacLab projects to mjlab
  • Compare IsaacLab and mjlab APIs
  • Import meshes into mjlab tasks
  • Author new mjlab-native tasks/components directly from local

Example prompts

  • “/mjlab-skillkit”

Workflow steps

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

  1. Confirm whether the task is migrate or author.
  2. Resolve references in this order: target repo -> local mjlab/ checkout if present -> bundled skill references -> online docs only if still…
  3. Read references/mjlab-api-pack.md and lock target APIs.
  4. Read references/mjlab-api-index.md, then load only the relevant module references
  5. If the request involves importing STL / OBJ / other mesh assets, also read references/mjlab-mesh-import-guidelines.md.
  6. Reuse native mjlab patterns from existing tasks in the target repo first, then from a local mjlab/ checkout if present, then from bundled…

What it can do on your machine

Read from SKILL.md and the folder at commit 50da882. 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/, which the agent can run.

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

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com

    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

Mjlab Skillkit loads about 3.1k tokens when it runs, and up to ~35k if it reads all its reference files. Until then it costs about 102 tokens; SKILL.md has 1,334 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~102
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~35k

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 cmjang/mjlab-skillkit at commit 50da882, republished under its MIT licence (© cmjang). 1,334 words, ~3,119 tokens.

Download SKILL.mdSave it as .claude/skills/mjlab-skillkit/SKILL.md (or your agent's skills folder). This skill also uses 39 other files; get the full folder from GitHub.
name
mjlab-skillkit
description
Use when asked to migrate IsaacLab projects to mjlab, compare IsaacLab and mjlab APIs, import meshes into mjlab tasks, or author new mjlab-native tasks/components directly from local or bundled mjlab docs and examples. This skillkit includes an IsaacLab Migration Skill and an mjlab Native Skill. Prefer mjlab public APIs, preserve behavior in migration mode, and avoid compatibility layers.

mjlab-skillkit

Skill Suite

  • IsaacLab Migration Skill (migrate)
    • Use when there is an IsaacLab source project to port into mjlab-native code.
    • Priority: preserve task behavior while translating APIs, config structure, and registration.
  • mjlab Native Skill (author)
    • Use when writing new mjlab-native tasks, configs, managers, sensors, terrain, RL wiring, or mesh workflows directly.
    • Priority: follow mjlab public APIs, existing task patterns, and bundled references.

Previously branded as isaaclab-to-mjlab.

Official Repositories

  • Migration pattern (official): https://github.com/mujocolab/anymal_c_velocity
  • mjlab repository (read-only reference): https://github.com/mujocolab/mjlab
  • IsaacLab repository (source behavior reference): https://github.com/isaac-sim/IsaacLab

Documentation Priority

  • Use local docs/ in the target workspace as source of truth first (for example mjlab/docs and IsaacLab/docs).
  • Read references/docs-interface-diff.md before implementation and follow the listed API differences.
  • Read references/mjlab-api-pack.md for the quick API shortlist.
  • Read references/mjlab-api-index.md to choose the exact mjlab API domain files you need.
  • When writing new mjlab-native code, also read references/mjlab-authoring-workflow.md.
  • For common coding requests, read references/mjlab-authoring-recipes.md and follow the matching minimal recipe instead of exploring broadly.
  • Then load only the matching references/mjlab-api-*.md and references/mjlab-mdp-builtins.md files instead of reading the whole API surface at once.
  • Do not bulk-ingest the whole mjlab/docs tree or crawl large raw upstream doc sets into context; read only the exact page/signature/example you still need after using the bundled references.
  • Fall back to online docs only when local docs are missing or incomplete.

Reference Resolution Order

  • Treat raw paths such as mjlab/docs/source/... and mjlab/src/mjlab/... as preferred lookup targets only when a local mjlab/ checkout exists.
  • If there is no local mjlab/ checkout, use this skill's bundled references first:
    • references/mjlab-api-*.md
    • references/mjlab-mdp-builtins.md
    • references/mjlab-authoring-workflow.md
    • references/mjlab-authoring-recipes.md
  • If the user provides another local mjlab repo path, use that path as the upstream reference root instead of assuming ./mjlab.
  • Use online docs / GitHub only as a last resort for exact upstream signatures or examples missing from both the local checkout and bundled references.
  • Do not block author mode just because the current workspace has no mjlab/ directory.

Working Modes

  • migrate
    • Use when there is an IsaacLab source task/project to port.
    • Goal: preserve behavior while converting to mjlab-native code.
  • author
    • Use when writing new mjlab-native code directly.
    • Goal: build tasks/components/configs/scripts that already follow mjlab docs and examples.

If the user does not specify a mode, infer it from the request:

  • mentions IsaacLab/source parity/porting -> migrate
  • mentions write/build/create mjlab task/config/component -> author

Layout Mode (migration only)

  • preserve-layout:
    • Keep original repository structure and module paths.
    • Only migrate API/config semantics to mjlab.
  • mjlab-layout:
    • Reorganize into anymal_c_velocity style task package layout (for example src/<task_pkg>/...).
    • Use mjlab.tasks entry points and register_mjlab_task(...) for registration.
  • If user does not specify, ask first:
    • Do you want to keep the original project layout, or convert directly to mjlab layout?

Shared Workflow

  1. Confirm whether the task is migrate or author.
  2. Resolve references in this order: target repo -> local mjlab/ checkout if present -> bundled skill references -> online docs only if still blocked.
  3. Read references/mjlab-api-pack.md and lock target APIs.
  4. Read references/mjlab-api-index.md, then load only the relevant module references:
    • references/mjlab-api-envs.md
    • references/mjlab-api-scene.md
    • references/mjlab-api-sim.md
    • references/mjlab-api-entity.md
    • references/mjlab-api-actuator.md
    • references/mjlab-api-sensor.md
    • references/mjlab-api-managers.md
    • references/mjlab-mdp-builtins.md
    • references/mjlab-api-terrains.md
    • references/mjlab-api-rl.md
    • references/mjlab-api-viewer.md
    • references/mjlab-api-tasks.md
  5. If the request involves importing STL / OBJ / other mesh assets, also read references/mjlab-mesh-import-guidelines.md.
  6. Reuse native mjlab patterns from existing tasks in the target repo first, then from a local mjlab/ checkout if present, then from bundled references.

Migration Workflow

  1. Confirm migration scope, source path, and target path before editing.
  2. Confirm layout mode: preserve-layout or mjlab-layout.
  3. Read references/migration-rules.md and enforce all hard constraints.
  4. Read references/docs-interface-diff.md for API differences from local docs.
  5. Read references/official-migrating-from-isaaclab.md for boundary notes.
  6. Read references/migration-gotchas.md for the compressed high-value pitfalls list.
  7. If the task is multi-variant / command-heavy / play-eval sensitive, first read references/complex-task-migration-playbook.md.
  8. If the task is motion tracking / whole-body tracking / reference-motion tracking, then read references/tracking-case-study.md as a concrete example, not as the only migration pattern.
  9. If the target task family has adjacent tests or registry files, treat them as invariants before editing (for example task __init__.py registration files and same-family tests).
  10. For complex tasks, first write a variant matrix for base / play / eval / no-state-estimation / low-freq / robot-specific style variants before migrating code.
  11. For mjlab-layout, align project packaging/registration with anymal_c_velocity.
  12. Read references/mapping.md while replacing imports/fields/term APIs.
  13. Read references/patterns.md while implementing EnvCfg/SceneCfg/manager structures.
  14. If importing STL / OBJ / mesh assets, read references/mjlab-mesh-import-guidelines.md and explicitly separate visual mesh from collision representation.
  15. Run references/checklist.md validation before completion.

Authoring Workflow

  1. Read references/mjlab-authoring-workflow.md.
  2. Read references/mjlab-authoring-recipes.md.
  3. Identify the artifact type:
    • new task package
    • new EnvCfg or scene
    • new manager terms / MDP helpers
    • new sensors / terrain / RL config / registration
  4. Choose the closest example before writing:
    • target-repo example first
    • then local mjlab/ example if available
    • then bundled skill references if no local mjlab/ checkout exists
    • velocity-style -> mjlab/src/mjlab/tasks/velocity/
    • tracking-style -> mjlab/src/mjlab/tasks/tracking/
  5. Build the config in mjlab-native order:
    • scene / sensors
    • observations
    • actions
    • commands
    • events
    • rewards
    • terminations
    • curriculum / metrics
    • simulation / viewer / episode settings
  6. Prefer existing mjlab.envs.mdp helpers before adding custom terms.
  7. Prefer the smallest edit surface that satisfies the request:
    • add a term before creating a new subsystem
    • modify robot-specific cfg before cloning a base factory
    • reuse an existing command/reward/event helper before inventing a new class
  8. If creating a new task, add RL config plus register_mjlab_task(...).
  9. Validate syntax and config wiring before completion.
Show full SKILL.md (473 more words)Show less

Authoring Request Routing

  • “Add a reward / penalty” -> references/mjlab-authoring-recipes.md + references/mjlab-mdp-builtins.md
  • “Add an observation / actor-critic input” -> references/mjlab-authoring-recipes.md + references/mjlab-api-managers.md
  • “Add contact / raycast / camera” -> references/mjlab-authoring-recipes.md + references/mjlab-api-sensor.md
  • “Add command / curriculum / reset / DR” -> references/mjlab-authoring-recipes.md + references/mjlab-mdp-builtins.md
  • “Create a new task / new robot env cfg / register a task” -> references/mjlab-authoring-recipes.md + references/mjlab-api-tasks.md + references/mjlab-api-rl.md
  • “Import STL / OBJ / mesh assets” -> references/mjlab-mesh-import-guidelines.md + references/mjlab-api-entity.md
  • “Unsure which file to edit” -> first read the “file placement” section in references/mjlab-authoring-recipes.md

Shared Constraints

  • Final implementation must be mjlab-native.
  • Do not add arbitrary abstractions (extra inheritance/wrappers/major restructuring).
  • Do not modify mujocolab/mjlab source code.
  • Manager configuration must be dict-based (dict[str, XxxTermCfg]), not manager @configclass.
  • Explicitly ban bridge helpers:
    • manager_terms_to_dict
    • AttrDict
    • observation_terms_from_class
  • No compatibility layer / adapter shim / transition wrappers.

Migration Constraints

  • Preserve behavior equivalence for rewards, observations, actions, commands, reset/events, terminations, and curriculum.
  • Keep function boundaries, call order, and config semantics aligned with source unless mjlab API differences require minimal internal changes.
  • Prioritize functional/semantic equivalence over literal code-shape equivalence; if mjlab API constraints prevent a one-to-one implementation, use the smallest mjlab-native adaptation that preserves behavior.
  • Do not drop source logic steps, config items, or execution order.
  • Remove Isaac/IsaacLab API residue (imports, symbols, stale comments, legacy fields).
  • Keep source-specific semantic names (for example hack_generator) unless forced field mapping is required.
  • Do not keep IsaacLab/Omniverse extension scaffolding by default (ui_extension_example.py, config/extension.toml, omni.* extension files).
  • Do not add new raise or assert if the source task has none, unless a minimal check is explicitly required by mjlab/target API semantics for correctness.
  • Do not add fallback logic if the source task has none (no broad try/except, no hasattr-style fallback branches, no silent degradations), unless a minimal guard is explicitly required by mjlab/target API semantics for correctness.
  • Keep original comments/TODOs. If wording must change, only do minimal mjlab terminology updates while preserving meaning.

Authoring Constraints

  • Prefer public mjlab.* modules and local task examples before inventing new helper layers.
  • Prefer task factories such as make_xxx_env_cfg() when a reusable base pattern is helpful.
  • Reuse mjlab.envs.mdp helper functions/configs whenever they already express the needed behavior.
  • Keep new code consistent with existing mjlab task structure, config naming, and registration style.

Execution Notes

  • In migrate mode, prefer one-to-one migration, not refactor-oriented rewrite.
  • In author mode, prefer nearest mjlab-native example, not a framework-agnostic rewrite.
  • For API mismatch, use minimal mjlab-native adaptation instead of bridge code.
  • When the question is “which mjlab API should replace this IsaacLab piece?”, choose the target module from references/mjlab-api-index.md first, then read only the matching reference file.
  • For mjlab-layout, prefer anymal_c_velocity packaging pattern: standalone task package + mjlab.tasks entry point + register_mjlab_task.
  • For preserve-layout, keep directory structure and only migrate API/config/registration wiring.
  • Managers should be generated via dict factory functions and initialized with field(default_factory=make_xxx) when dataclass config style is used.
  • If inherited config chains are not supported in target style, flatten them via explicit dict merge/override.
  • Follow target project registration conventions (prefer project-level registrar; use gym.register only when target project explicitly requires it).

© cmjang, MIT. 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 39 other files (scripts, references) in the repository root of cmjang/mjlab-skillkit.

  • SKILL.md
  • .gitignore
  • LICENSE
  • README.md
  • adapters/claude/README.md
  • adapters/cursor/mjlab-skillkit.mdc
  • adapters/gemini/README.md
  • adapters/opencode/README.md
  • agents/openai.yaml
  • references/checklist.md
  • references/complex-task-migration-playbook.md
  • references/docs-interface-diff.md
  • references/mapping.md
  • references/migration-gotchas.md
  • … and 26 more

Open the folder on GitHubat commit 50da882

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1passwordtrpc-group/trpc-agent-go1.9k14 repos~656Automated safety check: PassApache-2.0

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Questions about Mjlab Skillkit

What does Mjlab Skillkit do?

A skill your agent uses when asked to migrate IsaacLab projects to mjlab, compare IsaacLab and mjlab APIs, import meshes into mjlab tasks, or author new mjlab-native tasks/components directly from…. Mjlab Skillkit is an agent skill from cmjang/mjlab-skillkit. Use when asked to migrate IsaacLab projects to mjlab, compare IsaacLab and mjlab APIs, import meshes into mjlab tasks, or author new mjlab-native tasks/components directly from local or bundled mjlab docs and examples.

When should I use Mjlab Skillkit?

Mjlab Skillkit fits situations like: asked to migrate IsaacLab projects to mjlab; compare IsaacLab and mjlab APIs; import meshes into mjlab tasks; author new mjlab-native tasks/components directly from local.

How do I install Mjlab Skillkit in Claude Code?

Run `npx skills add cmjang/mjlab-skillkit --skill mjlab-skillkit -a claude-code`. Or copy the skill folder (the cmjang/mjlab-skillkit repository) into .claude/skills/mjlab-skillkit in your project. Claude Code loads it when a task matches its description.

How do I install Mjlab Skillkit in Codex?

Run `npx skills add cmjang/mjlab-skillkit --skill mjlab-skillkit -a codex`. Or copy the skill folder (the cmjang/mjlab-skillkit repository) into .agents/skills/mjlab-skillkit in your project. Codex loads it when a task matches its description.

Can I use Mjlab Skillkit 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 cmjang/mjlab-skillkit --skill mjlab-skillkit -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mjlab-skillkit, .gemini/skills/mjlab-skillkit, .github/skills/mjlab-skillkit and .opencode/skills/mjlab-skillkit in your project.

What does Mjlab Skillkit need to run?

SKILL.md names no scripts, command-line tools or credentials: Mjlab Skillkit is instructions for the agent only.

Does Mjlab Skillkit access the network?

SKILL.md names 1 domain. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Mjlab Skillkit 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 Mjlab Skillkit use?

Mjlab Skillkit is published under the MIT licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Mjlab Skillkit use?

About 3.1k tokens (SKILL.md is roughly 12k 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 32k tokens, read only when the agent opens those files.

What are the alternatives to Mjlab Skillkit?

Skills that share tags, products or a category with Mjlab Skillkit: Agent Builder (shareAI-lab/learn-claude-code, 78k stars), Add Uint Support (pytorch/pytorch, 104k stars), LLM Benchmarking with lm-evaluation-harness (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Segment Anything Model Guide (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mjlab Skillkit?

cmjang (a GitHub user) maintains it in cmjang/mjlab-skillkit, which has 104 GitHub stars. The repository was last updated on March 7, 2026.

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