How do I install the Train skill?

Run npx skillmds@latest add rohanpsingh/train in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Train skill do?

Train It is listed under Coding & Dev Tools on SkillMD.

Is Train safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Train?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Train free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

rohanpsingh (@rohanpsingh) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Train by rohanpsingh — Agent Skill

Name: Train
Author: rohanpsingh

File contents

/train — Launch a PPO Training Run

Parse the user's request from $ARGUMENTS and construct a training command.

Command Template

RAY_ADDRESS= uv run python run_experiment.py train --env <ENV> --logdir <LOGDIR> [OPTIONS...]

Available Environments

Name	Description
`cartpole`	Cartpole swing-up (simplest, good for testing)
`h1`	Unitree H1 standing task
`jvrc_walk`	JVRC humanoid basic walking
`jvrc_step`	JVRC humanoid stepping with planned footsteps

Hyperparameters (defaults)

Flag	Default	Description
`--n-itr`	20000	Training iterations
`--lr`	1e-4	Learning rate
`--gamma`	0.99	Discount factor
`--std-dev`	0.223	Action noise
`--learn-std`	off	Learn action noise (flag)
`--entropy-coeff`	0.0	Entropy regularization
`--clip`	0.2	PPO clipping
`--minibatch-size`	64	Minibatch size
`--epochs`	3	Optimization epochs per update
`--num-procs`	12	Parallel workers
`--num-envs-per-worker`	1	Vectorized envs per worker
`--max-grad-norm`	0.05	Gradient clipping
`--max-traj-len`	400	Episode horizon
`--eval-freq`	100	Eval every N iterations
`--seed`	None	Random seed
`--device`	auto	Training device (auto/cpu/cuda)
`--no-mirror`	off	Disable symmetry wrapper (flag)
`--recurrent`	off	Use LSTM policy (flag)
`--continued`	None	Path to pretrained weights

Instructions

Determine the environment name from the user's request. If ambiguous, ask.
Use --logdir /tmp/training_runs unless the user specifies a different path.
Only include flags that differ from defaults — keep the command clean.
Show the user the full command you're about to run.
Run the command in the background using run_in_background: true on the Bash tool. Set a generous timeout (600000ms).
After launching, tell the user the logdir path and how to check progress (you can tail the output using the task ID).
If the user asks to check on training, use TaskOutput with block: false to check the latest output.

Cartpole-Specific Defaults

For cartpole, these settings are known to work well with the current defaults (--lr 3e-4 --max-grad-norm 0.5 --lam 0.95 --gamma 0.99):

--minibatch-size 256
--std-dev 0.15 --learn-std --entropy-coeff 0.01
--max-traj-len 500 --n-itr 500 --num-procs 12
--no-mirror (cartpole has no body symmetry)

Suggest these defaults when the user trains cartpole, but let them override.

---name: traindescription: Train---# /train — Launch a PPO Training RunParse the user's request from `$ARGUMENTS` and construct a training command.## Command Template```RAY_ADDRESS= uv run python run_experiment.py train --env <ENV> --logdir <LOGDIR> [OPTIONS...]```## Available Environments| Name | Description ||------|-------------|| `cartpole` | Cartpole swing-up (simplest, good for testing) || `h1` | Unitree H1 standing task || `jvrc_walk` | JVRC humanoid basic walking || `jvrc_step` | JVRC humanoid stepping with planned footsteps |## Hyperparameters (defaults)| Flag | Default | Description ||------|---------|-------------|| `--n-itr` | 20000 | Training iterations || `--lr` | 1e-4 | Learning rate || `--gamma` | 0.99 | Discount factor || `--std-dev` | 0.223 | Action noise || `--learn-std` | off | Learn action noise (flag) || `--entropy-coeff` | 0.0 | Entropy regularization || `--clip` | 0.2 | PPO clipping || `--minibatch-size` | 64 | Minibatch size || `--epochs` | 3 | Optimization epochs per update || `--num-procs` | 12 | Parallel workers || `--num-envs-per-worker` | 1 | Vectorized envs per worker || `--max-grad-norm` | 0.05 | Gradient clipping || `--max-traj-len` | 400 | Episode horizon || `--eval-freq` | 100 | Eval every N iterations || `--seed` | None | Random seed || `--device` | auto | Training device (auto/cpu/cuda) || `--no-mirror` | off | Disable symmetry wrapper (flag) || `--recurrent` | off | Use LSTM policy (flag) || `--continued` | None | Path to pretrained weights |## Instructions1. Determine the environment name from the user's request. If ambiguous, ask.2. Use `--logdir /tmp/training_runs` unless the user specifies a different path.3. Only include flags that differ from defaults — keep the command clean.4. Show the user the full command you're about to run.5. Run the command in the background using `run_in_background: true` on the Bash tool. Set a generous timeout (600000ms).6. After launching, tell the user the logdir path and how to check progress (you can tail the output using the task ID).7. If the user asks to check on training, use `TaskOutput` with `block: false` to check the latest output.## Cartpole-Specific DefaultsFor cartpole, these settings are known to work well with the current defaults(`--lr 3e-4 --max-grad-norm 0.5 --lam 0.95 --gamma 0.99`):- `--minibatch-size 256`- `--std-dev 0.15 --learn-std --entropy-coeff 0.01`- `--max-traj-len 500 --n-itr 500 --num-procs 12`- `--no-mirror` (cartpole has no body symmetry)Suggest these defaults when the user trains cartpole, but let them override.

Frequently asked questions

Train