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Domain Ml

Checked
goooice·vlatest·ai-ml

Analyzes domain-specific text data using machine learning to extract insights and identify trends for improved decision-making.

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 goooice/rust-skills --skill domain-ml -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

goooice

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 goooice/rust-skills at 57af2e8da9.

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

Commit
57af2e8da9
Manifest
skills/domain-ml/SKILL.md
Scanned
1 file, 170 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

The goooice-domain-ml skill provides machine learning capabilities within a Rust environment. It allows agents to perform tasks such as data analysis, model training, and prediction based on provided datasets. This skill is designed for users who need robust ML functionality integrated into their AI agent workflows.

When to use it

  • Data Analysis: Use the skill to analyze structured data and identify trends or anomalies.
  • Predictive Modeling: Train machine learning models to predict future outcomes based on historical data.
  • Automated Decision Making: Integrate ML predictions into automated decision-making processes within your agent.
  • Custom Model Development: Develop and deploy custom machine learning models tailored to specific domain needs.

Key capabilities

  • Rust-based implementation for performance and reliability.
  • Data analysis tools for exploring datasets.
  • Machine learning model training functionality.
  • Prediction generation based on trained models.

Example prompts

  • "Analyze this CSV file and tell me the average value of column 'price'."
  • "Train a linear regression model using these features to predict 'sales'."
  • "Given this dataset, what is the predicted outcome for [specific input]?"

Tips & gotchas

The skill requires familiarity with Rust programming concepts. Ensure you have appropriate data preprocessing steps in place before feeding data into the ML models for optimal results.