agentleFS
robium-airobium-ai/robium/skills/simulation/SKILL.md

simulation

Skill93 starsChanged 3 months agoMITView on GitHub

Choose between Gazebo, Isaac Sim, and MuJoCo and define what the simulation must prove.

3 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

8 rules. About 0 are common, 4 are distinctive, and 4 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 4

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Gazebo: prefer for ROS-centric mobile robots, standard sensor simulation, and projects without a confirmed compatible NVIDIA GPU.

    line 18
  2. Very specific:

    Isaac Sim: consider when photorealism, synthetic data at scale, or the NVIDIA Isaac stack is central.

    line 20
  3. Distinctive:

    MuJoCo: prefer for lightweight, contact-rich manipulation that does not need ROS integration or photoreal rendering.

    line 24
  4. Very specific:

    After selection, use gazebo, isaac-sim, or mujoco for tool mechanics.

    line 29
  5. Very specific:

    Use isaac-lab for training workflows on top of Isaac Sim.

    line 30
  6. Distinctive:

    If the simulator will back regression tests, control time step and randomness and judge behavior with tolerances rather than pixel-perfect …

    line 40
  7. Very specific:

    Read sensor fidelity when adding sensors, preparing sim-to-real work, or turning a scenario into a repeatable test.

    line 43
  8. Distinctive:

    Every consumer uses a consistent clock and frame convention.

    line 54

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Simulation

Choose a simulator for the project's hardest requirement, then define the evidence that would make its results trustworthy.

Pick by bottleneck

  • For a new robot app or first simulation demo, use architect to check compatible reference apps before selecting a fresh stack. Keep a selected example's simulator. Continue here when no example fits or simulator choice is the actual unresolved question; comparisons and existing simulation work do not need onboarding.
  • Gazebo: prefer for ROS-centric mobile robots, standard sensor simulation, and projects without a confirmed compatible NVIDIA GPU.
  • Isaac Sim: consider when photorealism, synthetic data at scale, or the NVIDIA Isaac stack is central. Confirm the current RTX GPU, driver, OS, and VRAM requirements before committing to it; use gazebo if that environment is not real and available.
  • MuJoCo: prefer for lightweight, contact-rich manipulation that does not need ROS integration or photoreal rendering.
  • A project may use different simulators for different concerns. Avoid forcing one tool to own both navigation validation and training-data generation.

After selection, use gazebo, isaac-sim, or mujoco for tool mechanics. Use isaac-lab for training workflows on top of Isaac Sim.

Define fidelity before building

  • Name what must transfer to hardware: interfaces, sensor behavior, dynamics, contacts, timing, or visual appearance.
  • Match real sensor rate, noise, frame names, range, field of view, resolution, and timestamp source where those affect downstream decisions.
  • Record known model gaps instead of treating simulation success as real-world evidence.
  • If the simulator will back regression tests, control time step and randomness and judge behavior with tolerances rather than pixel-perfect coincidence.

Read sensor fidelity when adding sensors, preparing sim-to-real work, or turning a scenario into a repeatable test. Use data when the unresolved question is what training data to generate rather than how to simulate it.

Done

  • The selected tool fits the available hardware and the project's main fidelity requirement.
  • The simulation contract names which interfaces and physical properties must match reality.
  • Every consumer uses a consistent clock and frame convention.
  • Repeatability is designed where the simulation will be used as a test.
  • Version and hardware requirements were checked in the current upstream Gazebo, Isaac Sim, or MuJoCo documentation.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

Reviews can't be read right now.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.