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Scikit-learn · lingxling | DecimalAI

ai mlby lingxling

Scikit-learn

lingxling/scikit-learn

Source lingxling/awesome-skills-cn ↗License MIT

Machine learning in Python with scikit-learn.

SkillScore100Maintained

Automatic

AI Engineer pack

Last verified 8/28/2026

What it changes

Passed 23 of 23 test cases with the skill, and 22 of 23 without it.

Without skill96%

With skill100%

Checks it passed that the model alone missed

Output notes that scikit-learn LinearRegression does not compute coefficient p-values.

1 fixed22 passed either way0 still failing0 broke

Pass-rate lift: +4 pp
Tokens: +173%
Agent turns: 0%
Response time: -4%

Model gemini-3.6-flashMeasured 8/28/2026v1 · latest is v223 of 23 cases passed with skill

Tokens, turns and time describe resource use, not a dollar price.

This benchmark measured an earlier version of the skill.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/lingxling-scikit-learn/SKILL.md https://app.decimal.ai/s/lingxling-scikit-learn/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add lingxling-scikit-learn, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/lingxling-scikit-learn/ and pins it in .decimal/skills.lock

decimalai skills add lingxling-scikit-learn

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/lingxling-scikit-learn/SKILL.md https://app.decimal.ai/s/lingxling-scikit-learn/SKILL.md

Raw file: /s/lingxling-scikit-learn/SKILL.md ↗ · machine-readable summary at /s/lingxling-scikit-learn.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

A test case the skill fixed. Same model, same prompt, with and without the skill.

Prompt

Provide Python code to fit an Ordinary Least Squares (OLS) linear model on tabular data and output a comprehensive statistical summary table containing individual coefficient p-values, standard errors, and t-statistics.

What was checked, without and with the skill
Checked	Without skill	With skill
Output uses statsmodels OLS or formula API.	Passed	Passed
Output notes that scikit-learn LinearRegression does not compute coefficient p-values.	Missed	Passed

case-21 · gemini-3.6-flash · 8/28/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md55 lines2 files

Scikit-learn

Detailed Guide

Read the detailed guide before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.

When to Use This Skill

Use the scikit-learn skill when:

Building classification or regression models
Performing clustering or dimensionality reduction
Preprocessing and transforming data for machine learning
Evaluating model performance with cross-validation
Tuning hyperparameters with grid or random search
Creating ML pipelines for production workflows
Comparing different algorithms for a task
Working with both structured (tabular) and text data
Need interpretable, classical machine learning approaches

Example Scripts

Classification Pipeline

Run a complete classification workflow with preprocessing, model comparison, hyperparameter tuning, and evaluation:

python scripts/classification_pipeline.py

This script demonstrates:

Handling mixed data types (numeric and categorical)
Model comparison using cross-validation
Hyperparameter tuning with GridSearchCV
Comprehensive evaluation with multiple metrics
Feature importance analysis

Clustering Analysis

Perform clustering analysis with algorithm comparison and visualization:

Track record

Benchmark

23 / 23

test cases passed, vs 22 / 23 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v5 — 10/10/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

lingxling / awesome-skills-cn

claude-scientific-skills/skills/scikit-learn/SKILL.md

View on GitHub ↗

License BSD-3-Clause licenseImported 10/10/2026Version v2Bundled 1 file

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Ready to supercharge your AI agents?

See it in action

What's inside

Scikit-learn

Detailed Guide

When to Use This Skill

Example Scripts

Classification Pipeline

Clustering Analysis

Track record

Benchmark

Adoption

SkillSafety

Source

Other skills for the same job

Related skills