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Ray Data

Checked
orchestra-research·vlatest·data·MIT

Ray-Data efficiently manages and retrieves large datasets for AI training and experimentation within Orchestra Research's platform.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add orchestra-research/ai-research-skills --skill ray-data -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

orchestra-research

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned orchestra-research/ai-research-skills at 773a52944b.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
773a52944b
Manifest
05-data-processing/ray-data/SKILL.md
Scanned
3 files, 440 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
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About this skill

ray-data

What it does

This skill enables AI agents to interact with Ray Data, a distributed data processing framework designed for high-performance analytics. It allows agents to manage large-scale datasets efficiently across clusters, facilitating complex data transformations and serving requests at scale.

When to use it

  • Processing massive datasets that exceed the memory capacity of a single machine.
  • Building scalable data pipelines that require distributed computing resources.
  • Serving real-time analytics queries on terabytes of structured or unstructured data.
  • Optimizing data workflows where low-latency access and high throughput are critical.

Key capabilities

  • Distributed data storage and retrieval across multiple nodes.
  • High-performance parallel processing for large-scale analytics tasks.
  • Efficient management of data lifecycles within a cluster environment.
  • Integration with the broader Ray ecosystem for unified compute and data handling.

Example prompts

  • "Load this 50GB CSV file into Ray Data and perform an aggregation by region."
  • "Set up a distributed dataset in Ray Data to serve real-time user activity metrics."
  • "Optimize this ETL pipeline using Ray Data's parallel processing capabilities."

Tips & gotchas

Ensure your environment has sufficient cluster resources allocated, as distributed processing requires multiple nodes. Be aware that while Ray Data excels at scale, it may introduce higher setup complexity compared to local file-based solutions for small datasets.