scikit-learn
Build scikit-learn Machine Learning Workflows
Classical machine learning projects need reliable preprocessing, tuning, and evaluation patterns. This skill gives Codex, Claude, and Claude Code focused scikit-learn guidance.
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Review the Skillstore skill "scikit-learn" from https://skillstore.io/skills/k-dense-ai-scikit-learn.md and its manifest at https://skillstore.io/api/skills/k-dense-ai-scikit-learn/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "scikit-learn". I need a churn prediction model for mixed customer data.
Expected outcome:
- Recommended workflow with stratified splitting and a ColumnTransformer.
- Suggested baseline models and tuning ranges.
- Metrics focused on recall, precision, and ROC AUC.
Using "scikit-learn". My clustering results are unstable and hard to explain.
Expected outcome:
- Checklist for scaling, PCA review, and algorithm comparison.
- Guidance on silhouette, Calinski-Harabasz, and Davies-Bouldin scores.
- Notes on interpreting clusters without ground truth labels.
Using "scikit-learn". Review my pipeline before I use it in production.
Expected outcome:
- Findings about leakage risk, validation design, and metric coverage.
- Recommendations for safer persistence and reproducible training.
Security Audit
Medium RiskStatic alerts were overwhelmingly false positives from Markdown fences, sklearn imports, grid-search terminology, and documentation URLs. No prompt injection or malicious command execution was found. Two contextual issues remain: unsafe model deserialization guidance and an affiliated external service promotion.
Confirmed security concerns (2)
Risk Factors
⚡ Contains scripts (7)
📁 Filesystem access (5)
⚙️ External commands (55)
🌐 Network access (4)
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<iframe src="https://skillstore.io/embed/skills/k-dense-ai-scikit-learn.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA · BibTeX · CFF)
APA citation
K-Dense-AI. (2026). scikit-learn security audit report (audit version 7) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-scikit-learn/audits/7BibTeX citation
@techreport{k-dense-ai-k-dense-ai-scikit-learn-2026,
author = {K-Dense-AI},
title = {scikit-learn security audit report (audit version 7)},
institution = {Skillstore},
year = {2026},
number = {7},
url = {https://skillstore.io/skills/k-dense-ai-scikit-learn/audits/7},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "scikit-learn security audit report (audit version 7)"
version: "unspecified"
type: report
authors:
- name: "K-Dense-AI"
date-released: "2026-07-05"
url: "https://skillstore.io/skills/k-dense-ai-scikit-learn/audits/7"
identifiers:
- type: other
value: "skillstore:k-dense-ai-scikit-learn:audit:7"
description: "Skillstore immutable audit report identifier"
Compare variants
2 installable variantsEach author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
davila7-scikit-learn
2026-08-21
k-dense-ai-scikit-learn
2026-08-21
Skillstore Score
Why this score Evidence Confidence: HighWhat You Can Build
Build a Tabular Classifier
Create a supervised learning workflow with preprocessing, stratified splitting, tuning, and final evaluation.
Compare Clustering Methods
Select clustering algorithms, scale features, estimate cluster counts, and review unsupervised quality metrics.
Review an ML Pipeline
Check a scikit-learn workflow for leakage, weak metrics, missing validation, and brittle preprocessing choices.
Try These Prompts
Help me build a scikit-learn classifier for my tabular dataset. Include train-test split, preprocessing, model choice, and evaluation metrics.
Given these numeric and categorical columns, recommend a scikit-learn preprocessing pipeline. Explain imputation, scaling, encoding, and leakage prevention.
Compare several scikit-learn models for this task. Use cross-validation, select useful metrics, and propose a practical hyperparameter search plan.
Review this scikit-learn workflow for data leakage, unreliable validation, unsafe model persistence, and deployment risks. Suggest concrete fixes.
Best Practices
- Keep preprocessing inside Pipeline or ColumnTransformer to reduce leakage risk.
- Choose metrics that match the business cost of false positives and false negatives.
- Load saved models only from trusted sources with clear provenance.
Avoid
- Do not fit scalers or encoders on all data before the test split.
- Do not rely on accuracy alone for imbalanced classification tasks.
- Do not load pickle or joblib model files from untrusted sources.
Frequently Asked Questions
What tasks does this skill support?
Can it write scikit-learn code?
Does it handle deep learning?
Can it choose the best metric?
Is model persistence covered?
Which assistants can use this skill?
Developer Details
Author
K-Dense-AILicense
BSD-3-Clause license
Skillstore revision
r1
Version notice
The author did not declare a version.
Repository
https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/scikit-learnRef
a25199bc7d6b82598536822d1738eb5d5f54025b
Maintenance freshness
7/18/2026
Usage
5 downloads · 593 views