القائمة
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.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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.