Read {skill_dir}/SDK.md in full before answering DataChain SDK questions or generating DataChain Python code. It holds the SDK rules: API usage, UDF signatures, settings, delta semantics, materialization patterns, saving, exporting. The last section below holds the steps that need a local checkout and the dc-knowledge/ knowledge base.

Scope of this skill

SDK.md owns how DataChain code is written — API usage, UDF signatures, the shape of a saved dataset, saving and exporting. It is self-sufficient on its own.

The datachain-knowledge skill owns the knowledge base at dc-knowledge/: what datasets already exist, what each one holds, how long a run will take, and keeping that record current. When it is loaded, it drives the session and calls the rules here to write the code.

Before writing any pipeline code

1. If dc-knowledge/index.md exists, read it first.
2. When the user's task overlaps with an existing dataset, read its .md under dc-knowledge/datasets/ for schema, code patterns, and lineage.
3. Bucket access: anonymous or authenticated? Check dc-knowledge/buckets/ for a .md file with anon: true/false in frontmatter. If none, run datachain bucket status <uri> to detect. If denied or not found, stop and ask the user.

Never create or modify files under dc-knowledge/ — that directory is owned by the knowledge skill.