dataset-generatorlisted

# Dataset Generator This skill is a tool-native dataset pipeline for Codex, Antigravity, and Claude Code. - Use the IDE's own tools for browsing, reading, search, and reasoning. - Use local Python scripts for deterministic normalization, state tracking, verification, deduplication, and export. - Do not call external LLM-provider APIs as part of this skill. ## Command surface - `dataset generate "<request>" [--count <n>]` - `dataset collect "<topic or query>" [--urls url1 url2] [--paths ./dir]` - `dataset verify <path/to/file>` - `dataset audit [<path/to/file>]` - `dataset export --format <openai|huggingface|csv|jsonl|all> [--schema-file path] [--split 0.1]` If `dataset generate` does not include a size, default to `500` records. If `dataset collect` does not include `--max-results`, default to `10` results per query. ## Core architecture - `sub-skills/` contains the cognitive instructions. - `scripts/` contains deterministic helpers. - `resources/internal-schema/canonical_schema.json` is the fixed pipeline backbone. - `resources/target-schemas/` contains preset export profiles. - `resources/templates/custom_flat_schema.json` is the starting point for custom headers. ## Fixed vs flexible schema - The canonical internal schema is fixed. - The final export schema is not universal and must be chosen per user request. - For custom CSV or flat JSONL headers, create or update a schema file and pass it to `scripts/export.py`. Read `sub-skills/dataset-strategy.md` first when