by datachain-ai · Codex Skill · ★ 2.8k
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DataChain - Data Context Layer for Object Storage DataChain is a data context layer for object storage. It gives AI agents and pipelines a typed, versioned, queryable view of your files - what exists, what schema it has, what's already been computed - without copying data or loading it into memory. Metadata queries across 100M+ files execute in milliseconds against a backend database Pipelines checkpoint - re-running the same script resumes compute without duplicating expensive LLM-call or ML scoring makes re-runs incremental — only new or changed files are processed Every registers a named, versioned dataset with schema and lineage A generated knowledge base () reflects the operational layer as markdown for agents to read before writing code Works with S3, GCS, Azure, and local filesystems. bash pip
| Stars | 2,792 |
| Forks | 147 |
| Language | Python |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 62.3698151356239/100 |
| Open Issues | 69 |
| Last Updated | 2026-07-02 |
| Created | 2024-06-25 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~16k |
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datachain is The Context Layer for unstructured data: typed, versioned datasets over S3, GCS, Azure. It is categorized as a Codex Skill with 2.8k GitHub stars.
datachain is primarily written in Python. It covers topics such as ai-agents, claude-code, codex.
You can find installation instructions and usage details in the datachain GitHub repository at github.com/datachain-ai/datachain. The project has 2.8k stars and 147 forks, indicating an active community.
datachain is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to datachain on Agent Skills Hub include OpenContext, superset, pilot-shell. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.