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data-check

Acquire and verify data for CNS papers that passed paper-filter. Clones repositories, downloads and organizes datasets per evaluation setting, performs Level 4 deep verification (acquisition integrity, initial state, separability, consistency, per-setting completeness), creates an organized data environment. Updates filter_result.json with verification results, acquisition details, and task_info corrections.

naturegym/.claude/skills/data-check/SKILL.md

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# Data-Check Skill

Downloads repositories and datasets referenced by papers that passed paper-filter, then performs Level 4 deep verification — checking acquisition integrity, initial state, separability, consistency, per-setting completeness. Results are written back into `filter_result.json`, providing a verified data foundation for downstream task package construction.

## Context

paper-filter's Level 3 (Data Completeness) is **lightweight** — it checks link accessibility, views directory structures, and reads READMEs, but does **not actually download data**. This skill performs the actual download and deep verification, then writes results back into `filter_result.json` as **Level 4**.

### Terminology Reference

This skill uses the same core definitions as paper-filter. See [references/core_definitions.md](references/core_definitions.md) for formal definitions of:
- **T = (A, Data, M, S, B)**: The ML task tuple
- **D_dev**: Development data space (all prior information available to the original authors for solving the problem)
- **D_eval = (X_test, Y_ref)**: Evaluation data space with formal input/reference decomposition