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name: offline_dataset_protocol description: Validate fixed offline reinforcement learning transition datasets and summarize D4RL-style provenance before recovery experiments.

Offline Dataset Protocol

Use this skill when a recovery or benchmark task needs to verify that training data is a fixed offline RL transition dataset. Do not use it to collect online rollouts, run simulators, or claim that synthetic proxy data is a real D4RL dataset.

Inputs

• A JSON file containing either a list of transition records or an object with a transitions list.
• Each transition must contain observation, action, reward, next_observation, terminal, and timeout.
• Optional metadata fields such as source, domain, variant, and is_resource_derived.

Outputs

• A JSON validation report with ok, counts, quality_tags, errors, and metadata.
• The report preserves whether the dataset is synthetic proxy data, resource-derived data, or a real benchmark item.

Workflow

1. Load the dataset JSON without mutating it.
2. Confirm that every transition has the required keys.
3. Coerce rewards to floats and terminal/timeout markers to booleans.
4. Count transitions, terminal endings, timeout endings, and combined episode endings.
5. Add the fixed_dataset tag and optional tags from metadata.
6. Reject empty datasets or missing fields.

Validation

Run python scripts/validate_offline_dataset.py <dataset.json> for CLI validation. Run python -m pytest tests or the Distiller validate_skill_tree.py --run-tests command for deterministic tests.

Limitations

This skill validates dataset structure and provenance; it does not implement a full D4RL environment, simulator, or benchmark download.