القائمة
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`.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
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.
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.
Run only stage 7 — emit a sibling .vortex next to each opted-in parquet. Use when the user asks to (re)convert parquet files to Vortex format without rebuilding from raw bytes.
استنادا إلى تصنيف SOC المهني
| name | raincloud-profile |
| description | 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`. |
Wraps python -m scripts.pipeline.profile. Opt-in stage; off the default
build path. Idempotent against parquet sha256.
list_datasets --inspect <slug>python -m scripts.pipeline.profile <slug> # one
python -m scripts.pipeline.profile --all # every built parquet
python -m scripts.pipeline.profile --sample-rows 1000000 <slug> # cap for huge slugs
Per-dtype stats:
Profiles live at outputs/v1/<slug>/profile.json and are read by:
python -m scripts.pipeline.browse)--inspect <slug> renderingdocs.py for backfilling shape_traits.high_cardinality_present into snapshot.jsonThe tracked mirror at docs/v1/profiles/<slug>.json is the fallback path that
fresh clones ship — both list_datasets --inspect and the TUI's Columns pane
read it when no built outputs/v1/<slug>/profile.json exists locally. The
profile stage auto-runs python -m scripts.pipeline.promote_profiles at the
end of a successful run (copies built profile.json into the tracked mirror,
byte-identical mirror skipped), so the tracked snapshot stays in sync without
a manual step. Pass --no-promote to suppress that auto-step while iterating;
invoke promote_profiles explicitly (or with --check) to re-sync or audit
the mirror after manual edits.