MJLab
SkillDev toolsUse when working on MJLab locomotion evaluation, SONIC checkpoint scoring, SkyPilot MJLab YAMLs, or Workbench MJLab CLI behavior.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the MJLab skill
What this skill tells your AI
The instructions your AI receives, as published by nebius/nebius-physical-ai in skills/tools/mjlab/SKILL.md and read by ahel’s review.
MJLab is the locomotion evaluation stage for SONIC Workbench workflows.
Interfaces
CLI:
npa workbench mjlab eval
npa workbench mjlab workflow
npa workbench mjlab status
npa workbench mjlab list
SkyPilot YAML:
workflows/testing/mjlab-eval.yamlworkflows/testing/sonic-locomotion-finetuning.yaml
Routing And Data Flow
Route MJLab evaluation to H100 for the checked-in workflow templates.
Inputs and outputs use S3 paths:
--input-path: retargeted motion or rollout artifacts.--checkpoint: SONIC checkpoint artifact.--output-path: MJLab evaluation output prefix.
The result artifact is mjlab_eval.json.
Workflow Constraint
Keep orchestration logic in SkyPilot YAML. Do not add a Python runner script for the SONIC locomotion fine-tuning path.
Signals
- GitHub stars
- 28
- Forks
- 15
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
mjlab- Source
- github.com/nebius/nebius-physical-ai