DataChain is a Python library and data-processing system that turns files from cloud storage or local filesystems into typed, versioned datasets that can be queried and processed. Teams use it to build repeatable pipelines for unstructured data, with optional knowledge-base and agent features that help coding agents understand and work with those datasets. The catalogue entries connect agents to DataChain's data workflows.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add datachain-ai/datachain --skill coregit clone --depth 1 https://github.com/datachain-ai/datachainWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/datachain-ai/datachain/core)<a href="https://agentmods.dev/skills/datachain-ai/datachain/core"><img src="https://agentmods.dev/badge/skills/datachain-ai/datachain/core/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/datachain-ai/datachain/core"><img src="https://agentmods.dev/badge/skills/datachain-ai/datachain/core.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00054 | $0.00391 |
| Opus 5.5 | $0.00022 | $0.00156 |
| Sonnet 5.5 | $0.00011 | $0.00078 |
| Haiku 4.5 | $0.00005 | $0.00039 |
Grade A, and why
datachain-core scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 25d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
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.mdexists, read it first. - When the user's task overlaps with an existing dataset, read its
.mdunderdc-knowledge/datasets/for schema, code patterns, and lineage. - Bucket access: anonymous or authenticated? Check
dc-knowledge/buckets/for a.mdfile withanon: true/falsein frontmatter. If none, rundatachain bucket status <uri>to detect. Ifdeniedornot found, stop and ask the user.
Never create or modify files under dc-knowledge/ — that directory is owned by the knowledge skill.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 25d ago Changed · -2 lines 6ef35001a4c2
- 1mo ago Changed · -795 lines 7d621ff3d614
- 1mo ago Changed · +42 lines 100c2c04cee2
- 1mo ago First seen · 776 lines · 54 tokens per session scan A 6efd3b97c239
datachain-core is a skill published in the GitHub repository datachain-ai/datachain (2,827 stars, last pushed today), licensed Apache-2.0. It adds 54 tokens to every session and 391 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
mlops-industrialization
Convert notebook prototypes into a distributable Python package with a src layout, a domain/io/application split, and validated OmegaConf plus Pydantic configuration. Use when moving code out of notebooks or designing entrypoints.
remove-deadcode-py
A procedure for finding and removing unused code from Python projects. It combines lint tools, dead-code scanning, repository searches, and checks for dynamic or framework-based references.
fastapi-pro
Production FastAPI patterns — async endpoints, SQLAlchemy 2.0 async, Pydantic V2, dependency injection, JWT auth, testing. Use for Python 3.11+ FastAPI backends. NOT for Django (→ django-patterns) or Node.js (→ backend-patterns).
matlab
Builds, reviews, migrates, and plans MATLAB or GNU Octave numerical workflows. Use for arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
pennylane
Builds and differentiates PennyLane quantum circuits, hybrid PyTorch or JAX models, molecular VQE and QAOA workflows. Use for variational quantum algorithms, quantum machine learning, simulator validation, and moving validated circuits to provider plugins. For hardware-specific compilation use qiskit or cirq; for…
rocm-kernels
Provides guidance for writing and benchmarking optimized Triton kernels for AMD GPUs (MI355X, R9700) on ROCm, targeting HuggingFace diffusers (LTX-Video, SD3, FLUX) and transformers. Core kernels: RMSNorm, RoPE 3D, GEGLU, AdaLN. Includes XCD swizzle, autotune, diffusers integration patterns, and LTX-Video pipeline…