Ray Data

Ray Data is a Data skill for Claude Code, published by OpenRaiser in NanoResearch.

1.4K stars95 forkson OpenRaiser/NanoResearchAdded 2026/08/19Repository updated 2026/05/26

agent-skillsagentsaiai-agentsai-scientistartificial-intelligenceautonomous-agentsautonomous-researchautoresearchclaude-codeclaude-skillsnanobotopenclaw

Install in seconds

Install Ray Data

Copy Ray Data into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/OpenRaiser/NanoResearch/tree/main/skills/vendor-ai-research/ray-data ~/.claude/skills/ray-data

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/OpenRaiser/NanoResearch.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: skills/vendor-ai-research/ray-data/SKILL.md in OpenRaiser/NanoResearch
Installs to: ~/.claude/skills/ray-data
Collection: One of 16 skills cataloged from this repository
Category: Data — 668 skills

What Ray Data does

Ray Data processes large datasets in parallel across CPU/GPU clusters for ML workloads. Use it for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

Ray Data is cataloged under Data on DirSkills. Ray Data comes from a repository tagged agent-skills, agents, ai, ai-agents and ai-scientist.

Documentation

README

Excerpt

Ray Data - Scalable ML Data Processing

Distributed data processing library for ML and AI workloads.

When to use Ray Data

Use Ray Data when:

Processing large datasets (>100GB) for ML training
Need distributed data preprocessing across cluster
Building batch inference pipelines
Loading multi-modal data (images, audio, video)
Scaling data processing from laptop to cluster

Key features:

Streaming execution: Process data larger than memory
GPU support: Accelerate transforms with GPUs
Framework integration: PyTorch, TensorFlow, HuggingFace
Multi-modal: Images, Parquet, CSV, JSON, audio, video

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Ray Data

What else does OpenRaiser publish alongside Ray Data?
Ray Data is one of 16 skills that DirSkills catalogs from OpenRaiser/NanoResearch, the repository it ships in. Its siblings there include Academic Plotting, Autoresearch and Creative Thinking for Research. Each one is a separate skill with its own page in this directory, installs the same way Ray Data does, and is maintained by OpenRaiser in that same repository. The rest of the collection is listed on the OpenRaiser/NanoResearch page.
How does Ray Data compare to other Data skills?
Ray Data ranks #408 by stars among the 668 Data skills in this catalog. The most-starred ones next to it are Benchmark Methodology, Jupyter Notebook and Solana. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Ray Data against them. Open each page to compare what they document and how they install.