How do I install the Datachain Core skill?

Run npx skillmds@latest add datachain-ai/datachain-core-2 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Datachain Core safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Datachain Core?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Datachain Core free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Datachain Core?

datachain-ai (@datachain-ai) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Datachain Core by datachain-ai — Agent Skill

Name: Datachain Core
Rating: 4.2 (5 reviews)
Author: datachain-ai

Datachain Core

Use ONLY for abstract DataChain SDK questions — API usage, method signatures, or code patterns — when no specific dataset or bucket is referenced. If the request mentions creating, saving, listing, exploring datasets or buckets, use datachain-knowledge instead.

datachain-ai Updated Sep 22, 2026

File contents

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

This skill does not own methodology. Decisions about which datasets to build, what scope, what shape (Container / Asset / Sense / Task), what fields to save, and when to dialogue with the user about layer choices — those are the CAST methodology, which lives in the datachain-knowledge skill at {knowledge_skill_dir}/CAST.md.

When knowledge is loaded, it is the orchestrator: it plans the layers (CAST §4), invokes the rules in SDK.md to write the code, then runs the KB pipeline. When knowledge is not loaded (raw SDK use, no dc-knowledge/ directory), SDK.md is self-sufficient — CAST doctrine simply does not apply.

If you find yourself reasoning about "should I build a Sense layer here?" or "should this be scoped to the bucket or the directory?" from inside this skill, stop — those questions belong upstream. Ask the user to load the knowledge skill, or fall through to a direct solve.

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.

---name: datachain-core-2description: Use ONLY for abstract DataChain SDK questions — API usage, method signatures, or code patterns — when no specific dataset or bucket is referenced. If the request mentions creating, saving, listing, exploring datasets or buckets, use datachain-knowledge instead.---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**This skill does not own methodology.** Decisions about *which* datasets to build, what scope, what shape (Container / Asset / Sense / Task), what fields to save, and when to dialogue with the user about layer choices — those are the CAST methodology, which lives in the **datachain-knowledge** skill at `{knowledge_skill_dir}/CAST.md`.When knowledge is loaded, it is the orchestrator: it plans the layers (CAST §4), invokes the rules in `SDK.md` to write the code, then runs the KB pipeline. When knowledge is *not* loaded (raw SDK use, no `dc-knowledge/` directory), `SDK.md` is self-sufficient — CAST doctrine simply does not apply.If you find yourself reasoning about "should I build a Sense layer here?" or "should this be scoped to the bucket or the directory?" from inside this skill, stop — those questions belong upstream. Ask the user to load the knowledge skill, or fall through to a direct solve.## Before writing any pipeline code1. 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.

Frequently asked questions