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Agent Data Ml Model

Checked
ruvnet·vlatest·agents·MIT

Helps with data, machine learning, data modeling as part of orchestrating AI agents and multi-step workflows workflows.

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 ruvnet/ruflo --skill agent-data-ml-model --full-depth -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

ruvnet

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 ruvnet/claude-flow at 49da6ecc44.

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

Commit
49da6ecc44
Manifest
.agents/skills/agent-data-ml-model/SKILL.md
Scanned
1 file, 196 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.
What each check looks for →

About this skill

What it does

This skill allows AI agents to interact with and utilize machine learning models. Specifically, it enables agents to load, run predictions on, and interpret results from pre-trained ML models. The skill provides a standardized interface for accessing these models without requiring the agent to understand the underlying implementation details. This facilitates integration of ML capabilities into various agent workflows.

When to use it

  • Sentiment Analysis: Analyze customer reviews or social media posts to gauge public opinion about a product or service.
  • Image Classification: Automatically categorize images uploaded by users, such as identifying different types of plants or animals.
  • Fraud Detection: Score transactions in real-time based on historical fraud patterns identified by an ML model.
  • Predictive Maintenance: Assess the health and remaining useful life of equipment using sensor data processed by a trained model.

Key capabilities

  • Model Loading: Loads pre-trained machine learning models from specified locations.
  • Prediction Execution: Executes predictions on input data using loaded models.
  • Result Interpretation: Provides structured output for interpreting model prediction results.
  • Standardized Interface: Offers a consistent API for interacting with various ML models.

Example prompts

  • "Analyze the sentiment of this customer review: 'The product was amazing!'"
  • "Classify this image and tell me what it depicts."
  • "Score this transaction for potential fraud using the provided data."

Tips & gotchas

  • Ensure the specified ML model is compatible with the skill's supported formats.
  • Be mindful of input data requirements (e.g., format, size) as dictated by the specific ML model being used.