Ml Model Training

Handles the full ML training pipeline from data prep through evaluation with scikit-learn, PyTorch, and TensorFlow. The examples cover the essentials like proper train/val/test splits, feature scaling, and regularization techniques. What's actually useful here is the "Known Issues Prevention" section that walks through common mistakes like data leakage and ignoring class imbalance with concrete before/after code. The reference files go deeper on framework-specific patterns like PyTorch training loops with early stopping and Keras callbacks. Good for spinning up a model quickly or debugging why your validation metrics look suspicious.

skills.sh installs: 474
GitHub stars: 248
last updated: Aug 21, 2026

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Install

$ npx -y skills add secondsky/claude-skills --skill ml-model-training --agent claude-code

Installs into .claude/skills of the current project.

Files

ML Model Training

Train machine learning models with proper data handling and evaluation.

Training Workflow

Data Preparation → 2. Feature Engineering → 3. Model Selection → 4. Training → 5. Evaluation

Data Preparation

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, LabelEncoder

# Load and clean data
df = pd.read_csv('data.csv')
df = df.dropna()

# Encode categorical variables
le = LabelEncoder()
df['category'] = le.fit_transform(df['category'])

# Split data (70/15/15)
X = df.drop('target', axis=1)
y = df['target']
X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3)
X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5)

# Scale features
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_val = scaler.transform(X_val)
X_test = scaler.transform(X_test)

Scikit-learn Training

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, accuracy_score

model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

SKILL.md on GitHub