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datachain-knowledge

datachain-ai · Данни и AI

Use whenever datasets, cloud storage buckets, or data pipelines are mentioned — creating, saving, querying, listing, exploring, deleting, or processing data in S3, GCS, Azure Blob, or local storage. Also use when running any script that may create datasets as a side effect. Maintains a knowledge base at dc-knowledge/ (JSON + markdown). ALWAYS use this skill when the user creates a dataset, saves pipeline output, runs a data script, or references any storage bucket.

Оригинален текст · няма потвърден превод на български
Платформи: Не са посочениНиво: Не е посоченоВ индекса: 22.09.2026 г.Обновен в GitHub: 20.09.2026 г.

Теми: ai-agents · claude-code · codex · data-context-layer · data-processing · harness-engineering · knowledge-base · mlops · multimodal · pydantic · unstructured-data

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Оригинален SKILL.md
GitHub хранилище
Звезди на репото
2820
Лиценз
Apache-2.0
Последна проверка на записа
22.09.2026 г.

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Командата отваря избора на skills от репозитория. Прочетете файла и условията на автора преди изпълнение.

npx skills add datachain-ai/datachain

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Откъс от SKILL.md

Началото на файла на автора, без превод.

Maintain a knowledge base at dc-knowledge/. .md files are the persistent output. .json files are intermediate (generated in Step 3, consumed in Step 4, then deleted).

{core_skill_dir}/SDK.md owns how the pipeline code is written — dataset shape, row grain, provenance, naming. This file owns the knowledge base and how the work is run: what already exists, what a run will cost, and where the results land.

Critical Rules

  1. Path is dc-knowledge/ — NOT .datachain/. The .datachain/ directory is the internal database; the knowledge base lives at dc-knowledge/.
  2. Never pass update=True to dc.read_storage() in query or exploration code unless the user explicitly asks to refresh the listing. Build scripts that read storage are the exception — they pass update=True, delta=True.
  3. Prefer DataChain operations over plain Python for all metadata analysis.
  4. Bounded output — JSON and markdown files stay small regardless of data size.
  5. Stop on auth/connection errors — bucket_scan.py runs a fast access check. If it exits with an error JSON on stderr, stop immediately and show the error to the user. Do not retry with different regions, profiles, or endpoints — ask for the missing credentials.
  6. Follow the enrichment prompt template literally in Step 4. Downstream tooling (render_index.py) parses the exact frontmatter the prompt prescribes.

Common gotchas in UDF scripts

  • parallel=N vs workers=N. parallel=N is local multiprocessing (works anywhere). workers=N is Studio-only and MUST be guarded: chain = chain.settings(parallel=N); if dc.is_studio(): chain = chain.settings(workers=N).
  • No from __future__ import annotations in UDF modules. It stringifies type hints and DataChain's signal-schema resolution rejects the string-vs-class mismatch.
  • Type the UDF return precisely. Iterator[object] / Iterator[Any] / bare dict fail schema resolution. Return a specific Iterator[T], a Pydantic BaseModel, or a primitive.
  • Generators aren't subscriptable. Iterators returned by file APIs do not support [:N]. Use enumerate + break, or list(...) only when the result is genuinely small.
  • Use datachain.__version__ to get the package version (e.g. dc.__version__).

Running the work

Reuse before building

Целият SKILL.md в GitHub