Skip to content
TrustedSkillsbeta
Back to all skills

Debug:Scikit Learn

Checked
snakeo·vlatest·ai-ml

Identifies and suggests fixes for errors in scikit-learn model code, improving accuracy and efficiency.

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 snakeo/claude-debug-and-refactor-skills-plugin --skill debug:scikit-learn -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

snakeo

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 snakeo/claude-debug-and-refactor-skills-plugin at e2e79df88e.

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

Commit
e2e79df88e
Manifest
plugins/debug-and-refactor/skills/debug-scikit-learn/SKILL.md
Scanned
1 file, 614 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 assists in debugging Scikit-learn code. It can identify and explain errors within your Python scripts utilizing the Scikit-learn library, providing insights into potential causes and suggesting corrections. The skill aims to streamline the debugging process for machine learning workflows involving Scikit-learn models.

When to use it

  • You're encountering an error message while training a Scikit-learn model (e.g., ValueError, TypeError).
  • Your trained Scikit-learn model is producing unexpected or inaccurate predictions.
  • You need help understanding why a specific Scikit-learn function isn’t behaving as expected.
  • You're receiving warnings during the execution of your Scikit-learn code and want to understand their implications.

Key capabilities

  • Error identification within Scikit-learn code.
  • Explanation of error causes.
  • Suggestion of potential corrections for errors.
  • Understanding of Scikit-learn function behavior.

Example prompts

  • "I'm getting a ValueError in my scikit-learn pipeline. Here’s the traceback: [paste traceback here]"
  • "My RandomForestClassifier is overfitting; can you help me debug my code and identify potential issues?"
  • "Why am I getting this warning when using GridSearchCV? FutureWarning: The use of joblib..."

Tips & gotchas

  • Providing the full error traceback significantly improves debugging accuracy.
  • The skill's effectiveness depends on the clarity and completeness of the provided code snippet.