eval
GitHub用于评估已训练的模型检查点。支持解析.pt文件、运行目录或日志目录路径,自动处理歧义并展示最终路径。执行交互式MuJoCo可视化评估,支持设置回合长度、随机种子及视频输出目录,并在完成后报告结果。
Trigger Scenarios
用户请求评估训练好的模型
需要可视化验证智能体性能
查看最新或指定检查点的表现
Install
npx skills add rohanpsingh/LearningHumanoidWalking --skill eval -g -y
SKILL.md
Frontmatter
{
"name": "eval",
"description": "Evaluate a trained checkpoint with visualization",
"allowed-tools": "Bash, Read, Glob",
"argument-hint": [
"path-or-logdir"
],
"disable-model-invocation": true
}
/eval — Evaluate a Trained Checkpoint
Parse the user's request from $ARGUMENTS and run evaluation.
Command Template
uv run python run_experiment.py eval --path <PATH> [OPTIONS...]
Path Resolution
The user may provide:
- A .pt file: Use directly (
--path /tmp/.../actor_999.pt) - A run directory: Contains actor*.pt files (
--path /tmp/.../26-03-07-00-26-36_cartpole/) - A logdir: Contains timestamped run subdirectories (
--logdir /tmp/training_runs)
If no path is given, check /tmp/training_runs for the most recent run.
Use Glob to verify the path exists and resolve it before running.
Options
| Flag | Default | Description |
|---|---|---|
--ep-len |
10 | Episode length in seconds |
--seed |
None | Random seed for reproducible eval |
--out-dir |
None | Directory to save videos |
Instructions
- Resolve the model path from the user's input. If ambiguous, list available checkpoints and ask.
- Show the user which checkpoint will be evaluated (full path).
- Run the eval command. This opens an interactive MuJoCo viewer window — it is NOT a background job.
- Report the results when done.
Version History
- cd8c655 Current 2026-07-24 11:47


