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Remove a dataset from sources.json and clean up its outputs. Use when the user wants to delete a slug from the manifest. Destructive — always confirm before acting.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Walk through adding a new dataset to sources.json and producing its first build. Use when the user wants to onboard a new upstream source, add a slug to the manifest, or extend the catalog.
Walk through writing a new transform handler under scripts/pipeline/handlers/. Use when the default tighten_types/identity paths can't produce the right shape — row-level JSON parsing, streaming to avoid OOM, multi-output splitting, or VARIANT-from-the-start.
Load a Raincloud dataset via the lightweight Python loader API (`raincloud.load(slug)`). Use when the user wants to read a prepared parquet/vortex artifact, inspect a slug's metadata, or pull a dataset for downstream analysis from cache → mirror → local build.
Push locally-built Raincloud artifacts to a mirror (`scripts.pipeline.publish`). Use when the maintainer wants to upload one or more slugs' parquet/vortex bytes to a configured mirror after a successful build, gated on the snapshot's recorded sha256.
Use when the user asks to compute or refresh per-column statistics for a raincloud dataset — produces `outputs/v1/<slug>/profile.json` for the TUI's detail pane and `list_datasets --inspect`.
Run the full Raincloud pipeline (fetch → extract → parse → transform → write → validate → convert) for one or more dataset slugs. Use when the user asks to build a dataset, rebuild a slug, or process a batch.
| name | raincloud-remove-dataset |
| description | Remove a dataset from sources.json and clean up its outputs. Use when the user wants to delete a slug from the manifest. Destructive — always confirm before acting. |
| argument-hint | <slug> |
| disable-model-invocation | true |
Walk through removing dataset $ARGUMENTS. Reference: SKILLS.md "Removing a dataset".
This is destructive. Confirm with the user before doing any of these steps. No backwards-compat shims are kept — git history is the fallback (.archive/ is gitignored and only present on the maintainer's tree).
Steps:
Remove the DatasetSpec entry from sources.json using the Python load-edit-dump pattern — never sed. Look up by slug and remove from m["datasets"].
Delete the output parquet (and sibling .vortex if present):
rm -rf outputs/v1/<slug>/
Optionally delete the raw cache:
rm -rf outputs/raw_downloads/<slug>/
Only do this if you're confident the dataset won't be re-added. The raw cache is unversioned and could feed a future schema_version bump.
Watch for sibling slugs sharing the same upstream URL (GloVe sizes, OSM Germany kinds) — those are deduped via hardlink. Removing outputs/raw_downloads/<slug>/ is fine because each slug gets its own subdir, but verify there isn't a multi-output handler producing the parquet from a shared raw dir before deleting either.
Regenerate docs:
python -m scripts.pipeline.docs
(Or invoke /raincloud-docs.)
If a handler became unused (only this slug referenced it), remove the handler file from scripts/pipeline/handlers/ and unregister it from __init__.py. No stub or deprecation shim — fully delete.
Removed means removed.