eval
- Repo stars 1,155
- License BSD-2-Clause
- Author updated Live
- Author repo LearningHumanoidWalking
- Domain
- Other
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 94 / 100 · audit passed
- Author / version / license
- @rohanpsingh · BSD-2-Clause
- Token usage
- Lean
- Setup complexity
- Plug-and-play
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- Python
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
---
name: eval
description: Evaluate a trained checkpoint with visualization Parse the user's request from $ARGUMENTS and ru…
category: other
runtime: Python
---
# eval output preview
## PART A: Task fit
- Use case: Evaluate a trained checkpoint with visualization Parse the user's request from $ARGUMENTS and run evaluation. uv run python run_experiment.py eval --path <PATH> [OPTIONS...] runs entirely locally; runs on Python. Works with Claude Code, Cursor, Cline and 23 more..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Command Template / Path Resolution / Options” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Evaluate a trained checkpoint with visualization Parse the user's request from $ARGUMENTS and run evaluation. uv run python run_experiment.py eval --path <PATH> [OPTIONS...] runs entirely locally; runs on Python. Works with Claude Code, Cursor, Cline and 23 more.”.
- **02** When the source has headings, the agent prioritizes “Command Template / Path Resolution / Options” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. Choose the trigger
The source mentions slash commands such as `/eval`, `/tmp`; use them first when your agent supports command triggers.
Provide inputs and boundaries
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Validate with a small run
Start with a small task and check whether the result follows “Command Template / Path Resolution / Options”. Inspect diffs, logs, previews, or tests before expanding scope.
Review before delivery
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: eval
description: Evaluate a trained checkpoint with visualization Parse the user's request from $ARGUMENTS and ru…
category: other
source: rohanpsingh/LearningHumanoidWalking
---
# eval
## When to use
- Evaluate a trained checkpoint with visualization Parse the user's request from $ARGUMENTS and run evaluation. uv run p…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Command Template / Path Resolution / Options” and keep inference separate from source facts.
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. Evidence boundary and execution chain
The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "eval" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Command Template / Path Resolution / Options
rules -> SKILL.md triggers / order / output contract
runtime -> Python | read files, write/modify files | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}/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.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review