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Rosclaw Simforge

Organization
ros-claw
rosclaw-simforge

Safely install, validate, diagnose, and optimize ROSClaw simulation workflows across MuJoCo/MJWarp, ROS 2 rosbridge, turtlesim, Gazebo, Isaac Sim, Isaac Lab multi-GPU training, MCP, and the signed ROSClaw Hub. Use for evidence-backed physical-AI smoke tests, 4-GPU validation, simulator integration, Hub upload/download tests, or ROSClaw verification reports without a real robot.

Overview

Publisherros-claw
Repositoryrosclaw
Skill namerosclaw-simforge
Stars
230
Forks
32
Bundled files
6
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ros-claw on GitHub. Read the source before you install it.

Installation

Install the Rosclaw Simforge AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ros-claw/rosclaw/tree/main/.agents/skills/rosclaw-simforge
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ros-claw/rosclaw.git /tmp/rosclaw
mkdir -p .claude/skills
cp -r /tmp/rosclaw/.agents/skills/rosclaw-simforge .claude/skills/rosclaw-simforge
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Rosclaw Simforge in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Rosclaw Simforge on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Rosclaw Simforge is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

ROSClaw SimForge

Validate the full software loop from CLI and safety gates through simulators, MCP, evidence receipts, and asset distribution. Treat real hardware as out of scope unless the user separately authorizes it.

Safety boundary

  • Use the exact rosclaw CLI from the checkout's .venv/bin; report a stale global CLI before changing it.
  • Use a temporary ROSCLAW_HOME for smoke tests and Hub operations.
  • Write raw trajectories, logs, receipts, and reports to an evidence directory outside the source checkout. Commit only reproducible code and tests.
  • ROS graph discovery, subscription, and simulation are allowed. Never publish actuator commands directly, call a motion service, or send an action goal.
  • Route any action request through rosclawd request_action / ROSClaw MCP so the daemon, sandbox, and firewall remain in the path.
  • Do not stop unrelated GPU processes or containers. Inspect available memory and choose the smallest useful workload.
  • A dependency failure is a failure to diagnose and install around, not a passing skip. Keep unsupported real-robot checks explicitly out of scope.

Workflow

1. Establish provenance

Read the repository AGENTS.md and the task's verification document. Record:

bash
git rev-parse HEAD
git status --short
nvidia-smi --query-gpu=index,name,memory.used,memory.free --format=csv

Fetch or compare upstream before claiming the checkout is current. Preserve all pre-existing user changes.

2. Validate the ROSClaw core

Prepend the repository environment to PATH, use a temporary home, and run doctor, status, the deterministic demo, receipt explanation, and the universal agent/MCP probe. Then run the repository test groups requested by the project verification document.

Do not claim a simulator pass from import-only evidence. Require a physics step, a bounded task outcome, and a receipt or other machine-readable result.

3. Validate ROS 2 and Gazebo

Run scripts/verify_ros2.sh from this skill directory. It builds the Humble rosbridge/turtlesim image, deploys the stack, discovers the live graph, subscribes to pose, and proves direct velocity requests are blocked.

For Gazebo, run scripts/verify_gazebo.sh. Humble's recommended pairing is Gazebo Fortress. The script now runs the Phase 3 GuardedBase world with real diff-drive, odometry, laser, independent ROS bridges, and a deadman under launch_testing. It injects actual process signals and requires bounded stop, observation-loss fail-closed behavior, cancellation/recovery, and no old-action replay. For the full MCP → rosclawd path and rosbridge-loss recovery, run:

bash
rosclaw chaos run gazebo-guarded-base \
  --faults agent-kill,rosbridge-loss,odom-stale,worker-crash \
  --output-dir /an/external/evidence/directory

Raw evidence must remain outside the checkout. A /clock sample alone is not enough to claim GuardedBase integration.

4. Validate Isaac Lab and four GPUs

Read references/verified-stack.md, then run scripts/verify_isaaclab.sh. The script performs bounded single-GPU and multi-GPU Cartpole training with Newton/MJWarp. Four visible devices must map to four ranks, synchronize gradients, advance physics, and exit zero.

An Isaac Sim container that advances physics but aborts during shutdown is a partial pass with a lifecycle defect, not a clean pass.

5. Validate G1 GoalForge

Use an external checkout of RoboNaldo's G1 deployment assets and keep all episode trajectories outside ROSClaw. The backend must qualify the exact 29-joint Unitree hg order, ONNX shape, motion tensors, body hash, and prior hash before physics starts.

Run the product surface from the checkout:

bash
.venv/bin/python -m rosclaw.entrypoint simforge doctor g1-goalforge --all \
  --output /evidence/doctor/goalforge-doctor.json
.venv/bin/python -m rosclaw.entrypoint simforge validate g1-goalforge \
  --pairs 100 --output /an/external/recovery-100.json
.venv/bin/python -m rosclaw.entrypoint simforge validate g1-goalforge \
  --profile nominal-success --workers 4 \
  --output /an/external/nominal-success-30.json
.venv/bin/python -m rosclaw.entrypoint demo run g1-goalforge \
  --target-zone random --failure-to-success --live-dashboard \
  --output-dir /an/external/new/evidence/directory
.venv/bin/python -m rosclaw.entrypoint practice start \
  --task g1_penalty_kick --generation 3 \
  --output-dir /an/external/new/practice/directory
.venv/bin/python -m rosclaw.entrypoint evolution run \
  --task g1_penalty_kick --generation 10 --gpus 0,1,2,3 \
  --output-dir /an/external/new/evolution/directory
.venv/bin/python -m rosclaw.entrypoint chaos run g1-goalforge \
  --faults agent-kill,worker-crash,dds-loss,state-stale \
  --output-dir /an/external/new/chaos/directory
MUJOCO_GL=egl \
.venv/bin/python -m rosclaw.entrypoint evolution export \
  /an/external/new/evidence/directory \
  --format video \
  --output /an/external/new/video/g1-goalforge.mp4

The four-GPU screen is a prioritizer. Require a disjoint, balanced CPU MuJoCo label-agreement run before accepting its labels, preserve mismatches as counterexamples, and use CPU MuJoCo strict replay for final physical truth. Private Holdout case rows must not enter candidate generation or public output.

Build E5 proofs only from passing source reports, then independently replay their bundle hash and primitive causal/fault/replay fields:

bash
.venv/bin/python -m rosclaw.entrypoint proof build g1-goalforge \
  --demo /evidence/demo/goalforge-demo.json \
  --recovery /evidence/recovery/recovery-100.json \
  --flywheel /evidence/practice/goalforge-flywheel.json \
  --memory /evidence/evolution/memory-ablation-100.json \
  --four-gpu /evidence/evolution \
  --agreement /evidence/evolution/cpu-gpu-label-agreement.json \
  --continual /evidence/evolution/continual-g0-g10.json \
  --chaos /evidence/chaos/goalforge-chaos.json \
  --output-dir /evidence/proofs
.venv/bin/python -m rosclaw.entrypoint proof replay /evidence/proofs \
  --modules body,provider,failure_router,sandbox,practice,memory,know,how,auto,darwin,registry,rosclawd
.venv/bin/python -m rosclaw.entrypoint promotion evaluate g1-goalforge \
  --doctor /evidence/doctor/goalforge-doctor.json \
  --recovery /evidence/recovery/recovery-100.json \
  --flywheel /evidence/practice/goalforge-flywheel.json \
  --four-gpu /evidence/evolution \
  --continual /evidence/evolution/continual-g0-g10.json \
  --chaos /evidence/chaos/goalforge-chaos.json \
  --proofs /evidence/proofs \
  --output /evidence/promotion-v4.json

The Promotion command requires a fresh simulation-only Doctor report. Treat a Doctor/Champion/Proof Body or kick-prior hash mismatch as a hard failure, and require all twelve GoalForge module proofs rather than only the four learning modules.

Unitree DDS tests must use loopback, a non-default isolated domain, canonical rt/lowcmd and rt/lowstate, simulation-only permits, and hardware_authorized=false. Never start a physical G1 transport.

GoalForge video export must consume strict-replay trajectory artifacts outside the checkout and write the MP4 and manifest outside the checkout. It is visualization-only: never use rendered pixels or subtitles as Promotion truth.

For success-rate optimization, use --profile nominal-success as the CPU MuJoCo acceptance surface. Require all 30 balanced nominal cases, at least 95% success, a gain of at least 30 percentage points over the fixed prior, 100% safe selection, 100% independent verification, and 100% strict replay. Keep the two-attempt runtime recovery budget separate from the at-most-32-candidate offline simulation search. Do not generalize that result to moving balls, randomized mass/friction/latency/noise/disturbance, or the 0.90 m target unless those cases pass a separately declared validation profile.

6. Validate MCP

First probe ROSClaw's built-in MCP server and list its tools. For the community Isaac Sim MCP adapter, isolate installation, start its extension only in a simulator, perform an MCP initialize/list-tools/call-tool round trip, and state that it is community-maintained rather than an NVIDIA-official MCP.

Never expose execute_script or scene mutation tools to real hardware.

7. Validate Hub upload/download

Run scripts/verify_hub.sh. It uses the repository fixture key and an isolated local HTTP registry to exercise validation, signature verification, login, signed publish, catalog sync/search, remote dry-run download, install, list, uninstall, and empty final state. Never reuse the fixture key for production.

8. Report the evidence ceiling

Separate PASS, PARTIAL, FAIL, and OUT OF SCOPE. Include exact commit, versions, commands, exit codes, test counts, GPU mapping, and artifact paths. The maximum claim must match the strongest verified evidence domain. Simulation evidence can promote only from baseline to SIM and never proves real-robot safety.

Resources

  • scripts/verify_ros2.sh — live ROS 2/turtlesim safety loop.
  • scripts/verify_gazebo.sh — Fortress diff-drive, odometry, laser, deadman, and real launch_testing process faults.
  • scripts/verify_isaaclab.sh — bounded one- and multi-GPU Isaac Lab loop.
  • scripts/verify_hub.sh — signed local Hub upload/download loop.
  • references/verified-stack.md — tested versions, expected evidence, and known compatibility limits.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Rosclaw Simforge AI skill do?

Safely install, validate, diagnose, and optimize ROSClaw simulation workflows across MuJoCo/MJWarp, ROS 2 rosbridge, turtlesim, Gazebo, Isaac Sim, Isaac Lab multi-GPU training, MCP, and the signed ROSClaw Hub. Use for evidence-backed physical-AI smoke tests, 4-GPU validation, simulator integration, Hub upload/download tests, or ROSClaw verification reports without a real robot.

Why use Rosclaw Simforge on TypingMind?

Because you install it once and use it with any model. Rosclaw Simforge is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Rosclaw Simforge in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ros-claw/rosclaw/tree/main/.agents/skills/rosclaw-simforge. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Rosclaw Simforge?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Rosclaw Simforge?

As many as you like. As long as a model supports skills, you can use Rosclaw Simforge with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Rosclaw Simforge AI skill free?

Yes. It is published on GitHub by ros-claw under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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