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Use when the user wants to find "interesting" datasets — exposes the new tag / showcase / size / trait / view filters on list_datasets.
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Based on SOC occupation classification
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-discover |
| description | Use when the user wants to find "interesting" datasets — exposes the new tag / showcase / size / trait / view filters on list_datasets. |
Wraps python -m scripts.pipeline.list_datasets with the discoverability flags. The TUI (python -m scripts.pipeline.browse) is the interactive sibling.
encoding, stress. Run --showcase-help for live counts.geospatial, nlp-text, web-analytics, e-commerce, finance, social, scientific, healthcare, sports, transportation, government, benchmark. Run --tags-help for live counts.xs / s / m / l / xl (file-size on disk).has_nested, has_timestamp, has_variant, string_heavy, wide_row, high_cardinality_present. Prefix with ! to negate.python -m scripts.pipeline.list_datasets --view encoding --long
python -m scripts.pipeline.list_datasets --tag geospatial --tag scientific
python -m scripts.pipeline.list_datasets --trait has_nested --size m
python -m scripts.pipeline.list_datasets --trait '!has_nested' --vortex
python -m scripts.pipeline.list_datasets --inspect clickbench-hits
python -m scripts.pipeline.list_datasets --tags-help
python -m scripts.pipeline.list_datasets --showcase-help
Filters AND across axes, OR within an axis. --view replaces other facet flags (preset is the entire facet spec).
--trait has_nested --vortex--size xs --size s--inspect <slug>--tags-help / --showcase-help