Datachain — DevOps skill for Claude Code

 DataChain: The Context Layer for Unstructured Data

[](https://pypi.org/project/datachain/) [](https://pypi.org/project/datachain) [](https://codecov.io/gh/datachain-ai/datachain) [](https://github.com/datachain-ai/datachain/actions/workflows/tests.yml) [](https://deepwiki.com/datachain-ai/datachain)

**A Python library that turns files in S3, GCS, and Azure into versioned, typed datasets, queryable at warehouse speed.**

• Compute Engine: parallel Python over files, distributed on Studio. Async I/O, checkpoint recovery, incremental updates.
• Dataset DB: Pydantic schemas, versioning, file pointers, automatic lineage. Sub-second filter, join, and group_by over millions of typed records locally, hundreds of millions on Studio. Vector search over the same rows, no separate store.

Optional, for agent workflows:

• Knowledge Base: markdown summaries derived from the Dataset DB and enriched by LLM. Readable by humans and LLMs.
• Agent Harness: a skill that plugs all three into Claude Code, Cursor, Codex, GitHub Copilot, and Pi, so they understand your data. On Studio, agents reach the same datasets over MCP.

Bytes never leave your storage. Every run deposits a typed dataset the next pipeline (or agent) reads instead of recomputing.

1. Install

pip install datachain

To add the agent skill (Knowledge Base + code generation):

datachain skill install --target claude     # also: cursor, codex, copilot, pi

Works with S3, GCS, Azure, and local filesystems.

2. Quickstart: agent-driven pipeline

Task: find dogs in S3 similar to a reference image, filter