From file to useful result
How to ask with this skill installed
Installing the skill gives your AI a method. Your request still has to provide the case-specific facts, constraints and expected output.
1Name the real task
Describe the decision or deliverable, not just the topic.
2Provide the evidence
Add source material, audience, limits and known facts.
3Define done
Set format, quality criteria and checks.
Adaptable starting prompt
Replace the brackets with your case
When to use it
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
Mistakes to avoid
ConvergenceWarning
Issue: Model didn't converge Solution: Increase max_iter or scale features
model = LogisticRegression(max_iter=1000)
Poor Performance on Test Set
Issue: Overfitting Solution: Use regularization, cross-validation, or simpler model
# Add regularization
model = Ridge(alpha=1.0)
# Use cross-validation
scores = cross_val_score(model, X, y, cv=5)
Memory Error with Large Datasets
Solution: Use algorithms designed for large data
# 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)