Skip to content
TrustedSkillsbeta
Back to all skills

Evaluating Machine Learning Models

Checked
jeremylongshore·vlatest·ai-ml·MIT

This skill assesses ML model performance across various metrics, providing insights for optimization and ensuring accurate predictions.

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 jeremylongshore/tons-of-skills-marketplace --skill evaluating-machine-learning-models -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

jeremylongshore

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 jeremylongshore/claude-code-plugins-plus-skills at a7be181f58.

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

Commit
a7be181f58
Manifest
plugins/ai-ml/model-evaluation-suite/skills/evaluating-machine-learning-models/SKILL.md
Scanned
5 files, 286 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 evaluate machine learning models. It can assess model performance based on provided metrics and data, helping users understand a model's strengths and weaknesses. The skill provides insights into accuracy, precision, recall, and other relevant evaluation criteria for deployed or experimental ML models.

When to use it

  • You have trained a machine learning model and need to determine its effectiveness before deployment.
  • You want to compare the performance of different machine learning models on the same dataset.
  • You are troubleshooting poor performance in an existing machine learning application.
  • You're analyzing results from an A/B test involving different ML model versions.

Key capabilities

  • Model evaluation based on provided metrics.
  • Assessment of accuracy, precision, and recall.
  • Analysis of machine learning models.

Example prompts

  • "Evaluate this machine learning model using the following data: [data] and these metrics: [metrics]."
  • "Analyze the performance of my image classification model based on its confusion matrix."
  • "Compare the accuracy, precision, and recall of Model A versus Model B given these results: [Model A results], [Model B results]."

Tips & gotchas

The skill requires accurate and well-defined metrics to perform a meaningful evaluation. Ensure the provided data is representative of the intended use case for the model.