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
- If
dc-knowledge/index.md exists, read it first.
- When the user's task overlaps with an existing dataset, read its
.md under dc-knowledge/datasets/ for schema, code patterns, and lineage.
- 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.