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Scikit-learn Machine Learning Guide

Guides an agent through classical machine learning in Python with scikit-learn: models, preprocessing, pipelines, and evaluation.

Source Download skill
Repository
K-Dense-AI/scientific-agent-skills
Stars
55,780
Licence
MIT
Files
11 files

What it does

Once installed, the agent knows how to build classical machine learning workflows with the scikit-learn Python library. It can pick suitable classification, regression, or clustering methods, prepare data without leaking test information into training, tune hyperparameters, and evaluate results with cross-validation and metrics. It also has bundled reference documents and two example scripts it can run to demonstrate a full classification or clustering workflow on local example data.

Great for

  • Analysts or students learning practical machine learning on tabular data
  • Developers who need a correct scikit-learn Pipeline with preprocessing and cross-validation
  • Data scientists comparing several algorithms on a structured dataset

Skip it if

  • Deep learning or large language model work, which is outside scikit-learn's scope
  • Production models trained on grouped or time-ordered data without adapting the scripts, because the bundled classification script assumes independent rows and its splits can leak information
  • Use with sensitive personal data without review, since the skill itself gives no privacy guidance

Try asking

  • “Build a classification pipeline for my customer churn CSV with proper scaling and imputation”
  • “Compare random forest and logistic regression with stratified cross-validation”
  • “Cluster my survey responses and help me choose the number of clusters using silhouette scores”
  • “Tune hyperparameters with GridSearchCV and explain the evaluation metrics”
Step-by-step guide: how to use Scikit-learn Machine Learning GuideInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 11 original files, the MIT licence and an attribution note.

    scikit-learn.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip scikit-learn.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip scikit-learn.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Give the agent your data file and target column so it can adapt the reference snippets, which use placeholder X and y variables.
  • Run the bundled scripts in a sandbox or virtual environment first, since they execute Python code on your machine.
  • Ask the agent to check your splitting strategy for grouped or time-based data before trusting scores.

What's inside

  • references/common_workflows.md
  • references/core_capabilities.md
  • references/model_evaluation.md
  • references/pipelines_and_composition.md
  • references/preprocessing.md
  • references/quick_reference.md
  • references/supervised_learning.md
  • references/unsupervised_learning.md
  • scripts/classification_pipeline.py
  • scripts/clustering_analysis.py
  • SKILL.md

Redistributed unmodified from K-Dense-AI/scientific-agent-skills under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.