Scikit Learn

What it does

When your agent uses it

• Tasks that involve Machine learning

Example prompts

• “Use the scikit-learn skill to support machine learning in Python with scikit-learn. An agent skill from K-Dense-AI/scientific-agent-skills”
• “/scikit-learn”

Requirements

• Python 3
• Compatibility (from SKILL.md): Requires Python 3.11+ and scikit-learn 1.9.1. NumPy, SciPy, and joblib are dependencies; bundled scripts also require pandas and matplotlib. Installation needs network access; bundled examples use local datasets without credentials.
• Pre-approved tools (allowed-tools): Read, Write, Edit, Bash

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

1. 1Supervised learning — classification and regression estimator families.
2. 2Unsupervised learning — clustering, decomposition, and manifold learning.
3. 3Model evaluation and selection — metrics, cross-validation, and hyperparameter search.
4. 4Data preprocessing — scaling, encoding, imputation, and feature selection.
5. 5Pipelines and composition — Pipeline and ColumnTransformer.

What it can do on your machine

Read from SKILL.md and the folder at commit 92ace75. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves these tools, so the agent can use them without asking each time:

  • Read
  • Write
  • Edit
  • Bash

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships 2 files in scripts/ (Python), which the agent can run.

  Shell commands in SKILL.md call:

  • uv

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

• Network

  Links to these hosts (documentation or services it may open):

  • scikit-learn.org
  • arxiv.org
  • doi.org
  • export.arxiv.org

  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.

• Compatibility

  Requires Python 3.11+ and scikit-learn 1.9.1. NumPy, SciPy, and joblib are dependencies; bundled scripts also require pandas and matplotlib. Installation needs network access; bundled examples use local datasets without credentials.

  From compatibility in the SKILL.md frontmatter.

Context cost

Scikit Learn loads about 3.3k tokens when it runs, and up to ~29k if it reads all its reference files. Until then it costs about 102 tokens; SKILL.md has 968 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~102

When it runs · the whole SKILL.md, loaded when a task matches

~3.3k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~29k

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

• NotePre-approves every shell command (allowed-tools: Bash)SKILL.md

  allowed-tools: Read, Write, Edit, Bash

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); the scripts in this folder are not scanned.

SKILL.md

The full file from K-Dense-AI/scientific-agent-skills at commit 92ace75, republished under its BSD-3-Clause licence (© K-Dense-AI). 968 words, ~3,312 tokens.

name

scikit-learn

description

Supports machine learning in Python with scikit-learn. Applies when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.

allowed-tools

Read, Write, Edit, Bash

compatibility

Requires Python 3.11+ and scikit-learn 1.9.1. NumPy, SciPy, and joblib are dependencies; bundled scripts also require pandas and matplotlib. Installation needs network access; bundled examples use local datasets without credentials.

license

BSD-3-Clause license

metadata.version

1.5

metadata.last-reviewed

2026-10-01

metadata.upstream-version

1.9.1

metadata.skill-author

K-Dense Inc.

# Install scikit-learn using uv
uv pip install "scikit-learn==1.9.1"

# Optional: plotting utilities and bundled script dependencies
uv pip install "scikit-learn[plots]==1.9.1" matplotlib pandas

# Commonly used with
uv pip install pandas numpy

import sklearn
print(sklearn.__version__)

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report

# Split data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, stratify=y, random_state=42
)

# Preprocess
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Train model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train_scaled, y_train)

# Evaluate
y_pred = model.predict(X_test_scaled)
print(classification_report(y_test, y_pred))

from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.ensemble import GradientBoostingClassifier

# Define feature types
numeric_features = ['age', 'income']
categorical_features = ['gender', 'occupation']

# Create preprocessing pipelines
numeric_transformer = Pipeline([
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler())
])

categorical_transformer = Pipeline([
    ('imputer', SimpleImputer(strategy='most_frequent')),
    ('onehot', OneHotEncoder(handle_unknown='ignore'))
])

# Combine transformers
preprocessor = ColumnTransformer([
    ('num', numeric_transformer, numeric_features),
    ('cat', categorical_transformer, categorical_features)
])

# Full pipeline
model = Pipeline([
    ('preprocessor', preprocessor),
    ('classifier', GradientBoostingClassifier(random_state=42))
])

# Fit and predict
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

uv run --no-project --with scikit-learn==1.9.1 --with pandas --with matplotlib python scripts/classification_pipeline.py

uv run --no-project --with scikit-learn==1.9.1 --with pandas --with matplotlib python scripts/clustering_analysis.py

# Good: Preprocessing in pipeline
pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('model', LogisticRegression())
])

# Bad: Preprocessing outside (can leak information)
X_scaled = StandardScaler().fit_transform(X)

# Good
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)  # Only transform

# Bad
scaler = StandardScaler()
X_all_scaled = scaler.fit_transform(np.vstack([X_train, X_test]))

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, stratify=y, random_state=42
)

model = RandomForestClassifier(n_estimators=100, random_state=42)

model = LogisticRegression(max_iter=1000)

# Add regularization
model = Ridge(alpha=1.0)

# Use cross-validation
scores = cross_val_score(model, X, y, cv=5)

# Use SGD for large datasets
from sklearn.linear_model import SGDClassifier
model = SGDClassifier()

# Or MiniBatchKMeans for clustering
from sklearn.cluster import MiniBatchKMeans
model = MiniBatchKMeans(n_clusters=8, batch_size=100)

© K-Dense-AI, BSD-3-Clause. 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 10 other files (scripts, references) in skills/scikit-learn of K-Dense-AI/scientific-agent-skills.

• SKILL.md
• 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

Open the folder on GitHubat commit 92ace75

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 K-Dense-AI/scientific-agent-skills, which our catalogue first saw on October 7, 2026.

• lingxling/awesome-skills-cnclaude-scientific-skills/skills/scikit-learn