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name: evaluation-and-sharing description: "Evaluate CleanRL saved models, inspect local artifacts, and safely reason about Hugging Face sharing without network or credential side effects by default." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Evaluation And Sharing

Use this sub-skill when a task is about evaluating a trained CleanRL model, finding the right evaluation entry point, inspecting a saved run artifact, or explaining how CleanRL's Hugging Face model zoo upload/download path works.

Route Here For

• Local artifact checks for runs/<run_name>/<exp_name>.cleanrl_model, TensorBoard event hints, and related videos/<run_name> or videos/<run_name>-eval folders.
• CleanRL model-zoo loading through cleanrl_utils.enjoy and its --exp-name, --env-id, --seed, --hf-entity, --hf-repository, and --eval-episodes arguments.
• Mapping an experiment name to the correct cleanrl_utils.evals function, model class tuple, serialization format, and optional dependencies.
• Explaining --save-model, --upload-model, --hf-entity, push_to_hub, and model card/upload behavior without performing network or credential actions by default.
• Diagnosing missing models, wrong Hugging Face repository names, env/script/eval mismatches, video capture failures, and optional Atari/JAX/MuJoCo/EnvPool dependency gaps.

Do Not Use For

• Choosing or configuring training algorithms; route to training-scripts.
• W&B benchmark reproduction, sweeps, or experiment orchestration; route to experiment-operations.
• Pull requests, documentation contribution, or repository maintenance work; route to repo-maintenance.

Safe Default Workflow

1. Read references/model-zoo-and-artifacts.md for artifact layout, save/upload flags, and network side effects.
2. Read references/evaluation-api.md to choose cleanrl_utils.enjoy versus a local cleanrl_utils.evals function.
3. Run python sub-skills/evaluation-and-sharing/scripts/check_cleanrl_artifact.py --run-dir <run-dir> --exp-name <exp-name> or python sub-skills/evaluation-and-sharing/scripts/check_cleanrl_artifact.py --runs-dir runs --exp-name <exp-name> --env-id <env-id> --seed <seed> before attempting evaluation.
4. Keep all Hugging Face downloads, uploads, repository creation, logins, and token use behind explicit user approval.
5. Use references/troubleshooting.md when an artifact, repository name, optional dependency, video backend, or eval function does not match the model.

Guardrails

• Do not run cleanrl_utils.enjoy unless the user accepts Hugging Face network download behavior.
• Do not run --upload-model, push_to_hub, or huggingface-cli login unless the user explicitly requests credential-backed sharing.
• Prefer local inspection and direct eval-function calls for already downloaded or locally trained .cleanrl_model files.
• Treat --track as W&B tracking and --upload-model as Hugging Face sharing; they are separate network integrations.