원클릭으로 Manus에서 모든 스킬 실행

raincloud-build

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업데이트2026년 5월 16일 22:26

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.

설치

Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.

Manus에서 실행

출처

spiraldb

spiraldb/raincloud

GitHub 저장소 열기 Creator 저장소 보기

다운로드

Manus에서 실행

관련 직업SOC

SOC 직업 분류 기준

소프트웨어 개발자컴퓨터 및 수학직·SOC 15-1252

SKILL.md

readonly

name	raincloud-build
description	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.
argument-hint	<slug>... \| --all [--loose] [--clean-workdir]
disable-model-invocation	true
allowed-tools	Bash(python -m scripts.pipeline.build *)

Run the Raincloud build orchestrator with the user's args:

python -m scripts.pipeline.build $ARGUMENTS

Selection (at least one required):

<slug>... — positional dataset slugs (any number)
--all — every dataset in sources.json

Modifiers:

--loose — downgrade expect.rows mismatches from errors to warnings. Use on the first build of a new slug before you know the exact row count.
--clean-workdir — wipe _workdir/<slug>/ after each successful build. Essential for large batch runs (Public BI decompressed CSVs can hit ~100 GB).

Before running:

Confirm with the user before triggering anything non-trivial. JSONBench 100M ≈ 6 h, Wikipedia Structured Contents → 34 GB parquet, OSM Germany ~45 min per kind. Small (<100 MB) parquets are fine without asking. (See AGENTS.md "Rebuilding is expensive".)
For large builds, set RAINCLOUD_DUCKDB_MEMORY_LIMIT and RAINCLOUD_DUCKDB_TEMP_DIRECTORY — see /raincloud-large-build for the full pattern.
For Kaggle/HF datasets, ensure uv sync --extra kaggle --inexact (or --extra huggingface --inexact) was run. The --inexact flag is important: without it, syncing one extra removes the others.

After a successful build, suggest running /raincloud-docs to regenerate derived docs.

Context: SKILLS.md, AGENTS.md.

Note (0.1.4)

Per-column profiles are a separate opt-in stage: python -m scripts.pipeline.profile <slug> after a build. Not part of the default build pipeline. See raincloud-profile skill.

이 저장소의 다른 Skills

같은 저장소

raincloud-add-dataset

spiraldb/raincloud

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.

2026-05-294

raincloud-add-handler

spiraldb/raincloud

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.

2026-05-294

raincloud-load

spiraldb/raincloud

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.

2026-05-294

raincloud-publish

spiraldb/raincloud

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.

2026-05-294

raincloud-profile

spiraldb/raincloud

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`.

2026-05-174

raincloud-convert

spiraldb/raincloud

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.

2026-05-164

name	raincloud-build
description	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.
argument-hint	<slug>... \| --all [--loose] [--clean-workdir]
disable-model-invocation	true
allowed-tools	Bash(python -m scripts.pipeline.build *)

Run the Raincloud build orchestrator with the user's args:

python -m scripts.pipeline.build $ARGUMENTS

Selection (at least one required):

<slug>... — positional dataset slugs (any number)
--all — every dataset in sources.json

Modifiers:

--loose — downgrade expect.rows mismatches from errors to warnings. Use on the first build of a new slug before you know the exact row count.
--clean-workdir — wipe _workdir/<slug>/ after each successful build. Essential for large batch runs (Public BI decompressed CSVs can hit ~100 GB).

Before running:

Confirm with the user before triggering anything non-trivial. JSONBench 100M ≈ 6 h, Wikipedia Structured Contents → 34 GB parquet, OSM Germany ~45 min per kind. Small (<100 MB) parquets are fine without asking. (See AGENTS.md "Rebuilding is expensive".)
For large builds, set RAINCLOUD_DUCKDB_MEMORY_LIMIT and RAINCLOUD_DUCKDB_TEMP_DIRECTORY — see /raincloud-large-build for the full pattern.
For Kaggle/HF datasets, ensure uv sync --extra kaggle --inexact (or --extra huggingface --inexact) was run. The --inexact flag is important: without it, syncing one extra removes the others.

After a successful build, suggest running /raincloud-docs to regenerate derived docs.

Context: SKILLS.md, AGENTS.md.

Note (0.1.4)

Per-column profiles are a separate opt-in stage: python -m scripts.pipeline.profile <slug> after a build. Not part of the default build pipeline. See raincloud-profile skill.