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Ml Model Evaluation

Checked
kentoshimizu·vlatest·ai-ml·Apache-2.0

Helps with machine learning, data modeling as part of agent 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 kentoshimizu/sw-agent-skills --skill ml-model-evaluation -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

kentoshimizu

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 kentoshimizu/sw-agent-skills at 5b7bafb626.

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

Commit
5b7bafb626
Manifest
skills/ml-model-evaluation/SKILL.md
Scanned
3 files, 73 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 evaluates machine learning models based on provided metrics. It can analyze performance indicators like accuracy, precision, recall, and F1-score to provide insights into a model's effectiveness. The skill aims to help users understand how well their ML models are performing against defined criteria.

When to use it

  • You have trained an ML model and need to quantify its performance.
  • You want to compare the performance of different ML models on the same dataset.
  • You're troubleshooting a poorly performing ML model and need to identify areas for improvement.
  • You require automated reporting of ML model evaluation metrics.

Key capabilities

  • Accepts various machine learning evaluation metrics as input.
  • Provides analysis based on provided performance indicators.
  • Offers insights into model effectiveness.

Example prompts

  • "Evaluate this model's accuracy, precision, recall and F1-score: [metrics data]"
  • "Analyze these results to determine if the model is performing adequately."
  • “Compare the metrics for Model A and Model B.”

Tips & gotchas

The skill requires a structured input of evaluation metrics. Ensure the provided data is correctly formatted for accurate analysis.