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name: experiment-operations description: "Operate CleanRL experiment matrices, tracking, tuning, resume, plotting, container, Slurm, and cloud workflows safely without executing long-running jobs or leaking credentials." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Experiment Operations

Use this sub-skill when the task is about CleanRL experiment orchestration rather than changing an algorithm implementation: benchmark command matrices, W&B tracking, Optuna tuning, checkpoint resume, reproduction command recovery, plotting, Docker images, Slurm arrays, or AWS Batch-style submissions.

Fast Routing

• Generate local benchmark commands or Slurm previews with references/benchmark-launchers.md and the bundled sub-skills/experiment-operations/scripts/generate_benchmark_commands.py helper.
• Configure W&B tracking, Optuna tuning, checkpoint resume, reproducibility command recovery, or W&B-based plotting with references/tuning-and-tracking.md.
• Inspect W&B, AWS, and Hugging Face credential readiness without printing secrets with sub-skills/experiment-operations/scripts/check_wandb_env.py before any tracked, cloud, or sharing workflow.
• Plan Docker, AWS Batch, Terraform, Slurm, and container image operations with references/cloud-and-containers.md; default to dry-run or command preview until the user approves side effects.
• Diagnose operational failures with references/troubleshooting.md before changing training scripts or credentials.

Safety Rules

• Do not run long training, Slurm submission, Docker build/push/run, Terraform, or AWS Batch commands unless the user explicitly asks for execution and confirms credentials/resources.
• Do not print API keys, cloud secrets, Hugging Face tokens, full .netrc contents, or copied environment values; report only whether required variables are set, missing, or placeholder-like.
• Treat generated benchmark commands as plans. Verify command count, --env-id, --seed, tracking flags, tags, and resource settings before handing them to execution.
• Keep algorithm-specific hyperparameter meanings and training flags in training-scripts; use this sub-skill only to organize how those commands are launched, tracked, tuned, resumed, or plotted.
• Route model artifact evaluation, Hub upload/download, and policy-enjoy workflows to evaluation-and-sharing; route contribution or test policy questions to repo-maintenance.

Operational Checklist

1. Identify the operation type: command matrix, tuner, tracking, resume/reproduce, plotting, Slurm, container, or cloud.
2. Decide whether the task is read-only, dry-run, local execution, or external-resource execution; require explicit approval for side-effectful operations.
3. Validate environment readiness with sub-skills/experiment-operations/scripts/check_wandb_env.py when W&B, AWS, or Hugging Face credentials may be involved.
4. Generate commands with sub-skills/experiment-operations/scripts/generate_benchmark_commands.py or manually mirror its env_ids × seeds ordering.
5. Review troubleshooting guidance for the relevant failure mode before modifying credentials, Docker tags, Slurm templates, checkpoint paths, or tuning objectives.