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DataTeX

Ray Data

by Orchestra-Research

Ray Data is a Data skill for Claude Code, published by Orchestra-Research in AI-Research-SKILLs.

11.6K stars838 forkson Orchestra-Research/AI-Research-SKILLsAdded 2026/07/19+1% in starsRepository updated 2026/06/16
View source on GitHubDownload repo ZIP
aiai-researchclaudeclaude-codeclaude-skillscodexgeminigpt-5grpohuggingfacemachine-leanringmegatronskillsvllm
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/Orchestra-Research/AI-Research-SKILLs/tree/main/05-data-processing/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/Orchestra-Research/AI-Research-SKILLs.git

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

In this catalog

Source file
05-data-processing/ray-data/SKILL.md in Orchestra-Research/AI-Research-SKILLs
Installs to
~/.claude/skills/ray-data
Collection
One of 50 skills cataloged from this repository
Category
Data — 812 skills

What Ray Data does

Ray Data is a distributed data processing library for ML workloads, supporting streaming execution on CPUs and GPUs. It handles Parquet, CSV, JSON, and images, and integrates with PyTorch, TensorFlow, and Ray Train for preprocessing, batch inference, and ETL.

Ray Data is cataloged under Data on DirSkills. Ray Data comes from a repository tagged ai, ai-research, claude, claude-code and claude-skills.

Documentation

README

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 Orchestra-Research publish alongside Ray Data?

    Ray Data is one of 50 skills that DirSkills catalogs from Orchestra-Research/AI-Research-SKILLs, the repository it ships in. Its siblings there include AWQ Quantization, Autoresearch and Axolotl. Each one is a separate skill with its own page in this directory, installs the same way Ray Data does, and is maintained by Orchestra-Research in that same repository. The rest of the collection is listed on the Orchestra-Research/AI-Research-SKILLs page.

  • How does Ray Data compare to other Data skills?

    Ray Data ranks #127 by stars among the 812 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.

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