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name: datasets summary: "Manage rLLM datasets, task layouts, registries, and eval-to-training data curation." description: "Use rLLM dataset APIs and CLI for dataset registration, local benchmark/task formats, verifier metadata, Harbor-compatible layouts, and SFT data curation from eval episodes." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

rLLM Datasets and Tasks

Use this sub-skill when the task mentions rllm dataset, DatasetRegistry, BenchmarkLoader, dataset.toml, task.toml, task_path, local benchmark directories, Harbor datasets, registered splits, or converting evaluation episodes into SFT data.

Start Here

1. Read references/dataset-operations.md for CLI workflows: list, pull, info, inspect, register, remove, and from-eval.
2. Read references/data-formats.md for file formats, task directory shapes, verifier metadata, and how rows become Task objects.
3. Read references/troubleshooting.md when a dataset cannot be found, loaded, inspected, or scored.
4. Switch to ../evaluation/SKILL.md for actually running an agent on the dataset, and ../training/SKILL.md for RL/SFT consumption.

Ownership Boundaries

• This sub-skill owns data shape and registry state, not provider/model setup.
• It owns the task/verifier metadata that evaluation and training consume.
• It does not prove the evaluator/verifier succeeds; use evaluation/training sub-skills for runtime execution.

Safe Check

For a local benchmark directory, run:

python scripts/inspect_task_layout.py /path/to/benchmark

The helper is read-only. It reports dataset.toml, per-task directories, data files, and environment/test directories without executing verifiers.