# Scikit Learn

> Classical machine learning with scikit-learn. Use when building classification, regression, clustering models, or implementing feature engineering pipelines.

- **Type:** Skill
- **Install:** `agentstack add skill-ihatesea69-kiro-kit-scikit-learn`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [ihatesea69](https://agentstack.voostack.com/s/ihatesea69)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [ihatesea69](https://github.com/ihatesea69)
- **Source:** https://github.com/ihatesea69/kiro-kit/tree/main/.kiro/skills/scikit-learn
- **Website:** https://www.npmjs.com/package/kiro-kit

## Install

```sh
agentstack add skill-ihatesea69-kiro-kit-scikit-learn
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Scikit-learn

Activate this skill when working with classical ML algorithms.

## When to Use

- Building classification or regression models
- Feature engineering and selection
- Implementing ML pipelines with preprocessing
- Cross-validation and hyperparameter tuning
- Clustering and dimensionality reduction

## Patterns

```python
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import cross_val_score

preprocessor = ColumnTransformer([
    ("num", StandardScaler(), numeric_features),
    ("cat", OneHotEncoder(handle_unknown="ignore"), categorical_features),
])

pipeline = Pipeline([
    ("preprocessor", preprocessor),
    ("classifier", GradientBoostingClassifier(n_estimators=200)),
])

scores = cross_val_score(pipeline, X, y, cv=5, scoring="f1_macro")
```

## Rules

- Always split data before any preprocessing
- Use pipelines to prevent data leakage
- Cross-validate before reporting metrics
- Start simple (LogisticRegression) before complex models
- Document feature engineering decisions

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [ihatesea69](https://github.com/ihatesea69)
- **Source:** [ihatesea69/kiro-kit](https://github.com/ihatesea69/kiro-kit)
- **License:** MIT
- **Homepage:** https://www.npmjs.com/package/kiro-kit

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-ihatesea69-kiro-kit-scikit-learn
- Seller: https://agentstack.voostack.com/s/ihatesea69
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.