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Agent skill

Scikit Learn Best Practices

by Kilo-Org in Kilo-Org/kilo-marketplace

Best practices for scikit-learn machine learning, model development, evaluation, and deployment in Python

Apache-2.0Auto-check passedData & Analytics

Install Scikit Learn Best Practices

skills CLI
$ npx skills add Kilo-Org/kilo-marketplace --skill scikit-learn-best-practices -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Kilo-Org/kilo-marketplace scikit-learn-best-practices --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Kilo-Org/kilo-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/scikit-learn-best-practices .claude/skills/scikit-learn-best-practices && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
scikit-learn-best-practices
GitHub stars
190
Used in
1 other repo
Token cost
~1.2k tokens
SKILL.md length
441 words
Files
2
Skills in repo
85
Repo updated
First seen
Licence
Apache-2.0

At a glance

Best practices for scikit-learn machine learning, model development, evaluation, and deployment in Python

  • Tasks that involve Machine learning
  • SKILL.md covers Code Style and Structure, Machine Learning Workflow, Model Selection and Tuning and Model Evaluation, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Scikit Learn Best Practices is an agent skill from Kilo-Org/kilo-marketplace. Best practices for scikit-learn machine learning, model development, evaluation, and deployment in Python

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file.

It sits in Data & Analytics, covering Machine learning. It works with scikit-learn and Python. The repository describes itself as: Kilo Marketplace - A curated collection of Skills, MCP Servers, and Modes for enhancing AI agent capabilities across the Kilo ecosystem—including Kilo Code (VS Code extension)… The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Machine learning

Example prompts

  • “/scikit-learn-best-practices”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit ff51758. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Scikit Learn Best Practices loads about 1.2k tokens when it runs. Until then it costs about 33 tokens; SKILL.md has 441 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~33
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Kilo-Org/kilo-marketplace at commit ff51758, republished under its Apache-2.0 licence (© Kilo-Org). 441 words, ~1,170 tokens.

Download SKILL.mdSave it as .claude/skills/scikit-learn-best-practices/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
scikit-learn-best-practices
description
Best practices for scikit-learn machine learning, model development, evaluation, and deployment in Python
metadata.category
data

Scikit-learn Best Practices

Expert guidelines for scikit-learn development, focusing on machine learning workflows, model development, evaluation, and best practices.

Code Style and Structure

  • Write concise, technical responses with accurate Python examples
  • Prioritize reproducibility in machine learning workflows
  • Use functional programming for data pipelines
  • Use object-oriented programming for custom estimators
  • Prefer vectorized operations over explicit loops
  • Follow PEP 8 style guidelines

Machine Learning Workflow

Data Preparation
  • Always split data before any preprocessing: train/validation/test
  • Use train_test_split() with random_state for reproducibility
  • Stratify splits for imbalanced classification: stratify=y
  • Keep test set completely separate until final evaluation
Feature Engineering
  • Scale features appropriately for distance-based algorithms
  • Use StandardScaler for normally distributed features
  • Use MinMaxScaler for bounded features
  • Use RobustScaler for data with outliers
  • Encode categorical variables: OneHotEncoder, OrdinalEncoder, LabelEncoder
  • Handle missing values: SimpleImputer, KNNImputer
Pipelines
  • Always use Pipeline to chain preprocessing and modeling
  • Prevents data leakage by fitting transformers only on training data
  • Makes code cleaner and more reproducible
  • Enables easy deployment and serialization
python
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier

pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('classifier', RandomForestClassifier(random_state=42))
])
Column Transformers
  • Use ColumnTransformer for different preprocessing per feature type
  • Combine numeric and categorical preprocessing in single pipeline

Model Selection and Tuning

Cross-Validation
  • Use cross-validation for reliable performance estimates
  • cross_val_score() for quick evaluation
  • cross_validate() for multiple metrics
  • Use appropriate CV strategy:
    • KFold for regression
    • StratifiedKFold for classification
    • TimeSeriesSplit for temporal data
    • GroupKFold for grouped data
Hyperparameter Tuning
  • Use GridSearchCV for exhaustive search
  • Use RandomizedSearchCV for large parameter spaces
  • Always tune on training/validation data, never test data
  • Set n_jobs=-1 for parallel processing

Model Evaluation

Classification Metrics
  • Use appropriate metrics for your problem:
    • accuracy_score for balanced classes
    • precision_score, recall_score, f1_score for imbalanced
    • roc_auc_score for ranking ability
  • Use classification_report() for comprehensive overview
  • Examine confusion_matrix() for error analysis
Show full SKILL.md (169 more words)Show less
Regression Metrics
  • mean_squared_error (MSE) for general use
  • mean_absolute_error (MAE) for interpretability
  • r2_score for explained variance
Evaluation Best Practices
  • Report confidence intervals, not just point estimates
  • Use multiple metrics to understand model behavior
  • Compare against meaningful baselines
  • Evaluate on held-out test set only once, at the end

Handling Imbalanced Data

  • Use stratified splitting and cross-validation
  • Consider class weights: class_weight='balanced'
  • Use appropriate metrics (F1, AUC-PR, not accuracy)
  • Adjust decision threshold based on business needs

Feature Selection

  • Use SelectKBest with statistical tests
  • Use RFE (Recursive Feature Elimination)
  • Use model-based selection: SelectFromModel
  • Examine feature importances from tree-based models

Model Persistence

  • Use joblib for saving and loading models
  • Save entire pipelines, not just models
  • Version control model artifacts
  • Document model metadata

Performance Optimization

  • Use n_jobs=-1 for parallel processing where available
  • Consider warm_start=True for iterative training
  • Use sparse matrices for high-dimensional sparse data
  • Consider incremental learning with partial_fit() for large data

Key Conventions

  • Import from submodules: from sklearn.ensemble import RandomForestClassifier
  • Set random_state for reproducibility
  • Use pipelines to prevent data leakage
  • Document model choices and hyperparameters

© Kilo-Org, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/scikit-learn-best-practices of Kilo-Org/kilo-marketplace.

  • SKILL.md
  • LICENSE

Open the folder on GitHubat commit ff51758

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in Kilo-Org/kilo-marketplace, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Scikit Learn Best Practices next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Scikit Learn Best Practices compared with similar skills
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Time Series Analytics Useropen-edge-platform/edge-ai-libraries169—~3.1kAutomated safety check: PassApache-2.0
Aeon Time Series Machine Learningdavila7/claude-code-templates32k14 repos~2.6kAutomated safety check: PassMIT
Precisemicroprediction/precise336—~782Automated safety check: PassMIT

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Works with

Categories

Questions about Scikit Learn Best Practices

What does Scikit Learn Best Practices do?

Best practices for scikit-learn machine learning, model development, evaluation, and deployment in Python. Scikit Learn Best Practices is an agent skill from Kilo-Org/kilo-marketplace.

When should I use Scikit Learn Best Practices?

Scikit Learn Best Practices fits situations like: tasks that involve Machine learning.

How do I install Scikit Learn Best Practices in Claude Code?

Run `npx skills add Kilo-Org/kilo-marketplace --skill scikit-learn-best-practices -a claude-code`. Or copy the skill folder (skills/scikit-learn-best-practices in Kilo-Org/kilo-marketplace) into .claude/skills/scikit-learn-best-practices in your project. Claude Code loads it when a task matches its description.

How do I install Scikit Learn Best Practices in Codex?

Run `npx skills add Kilo-Org/kilo-marketplace --skill scikit-learn-best-practices -a codex`. Or copy the skill folder (skills/scikit-learn-best-practices in Kilo-Org/kilo-marketplace) into .agents/skills/scikit-learn-best-practices in your project. Codex loads it when a task matches its description.

Can I use Scikit Learn Best Practices in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Kilo-Org/kilo-marketplace --skill scikit-learn-best-practices -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/scikit-learn-best-practices, .gemini/skills/scikit-learn-best-practices, .github/skills/scikit-learn-best-practices and .opencode/skills/scikit-learn-best-practices in your project.

What does Scikit Learn Best Practices need to run?

SKILL.md names no scripts, command-line tools or credentials: Scikit Learn Best Practices is instructions for the agent only. Our summary lists: Python 3.

Does Scikit Learn Best Practices access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Scikit Learn Best Practices safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Scikit Learn Best Practices use?

Scikit Learn Best Practices is published under the Apache-2.0 licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Scikit Learn Best Practices use?

About 1.2k tokens (SKILL.md is roughly 4.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Scikit Learn Best Practices?

Skills that share tags, products or a category with Scikit Learn Best Practices: Scikit Learn (zLanqing/codex-claude-academic-skills, 4.6k stars), Senior Data Scientist (Raidriar7170/hermes-skilleval, 125 stars), Time Series Analytics User (open-edge-platform/edge-ai-libraries, 169 stars) and Aeon Time Series Machine Learning (davila7/claude-code-templates, 32k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Scikit Learn Best Practices?

Kilo-Org (a GitHub organization) maintains it in Kilo-Org/kilo-marketplace, which has 190 GitHub stars. The repository holds 85 skills in this directory. The repository was last updated on September 28, 2026.

Source: Kilo-Org/kilo-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.