Scikit Learn
Scikit-learn machine learning library. Use for classical ML.
Install
$ agentstack add skill-g1joshi-agent-skills-scikit-learn ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-g1joshi-agent-skills-scikit-learn) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Scikit-learn
Scikit-learn is the gold standard for "Classical ML" (Regression, SVM, Random Forest). v1.6 (2025) adds Array API support (running on GPUs via PyTorch/CuPy).
When to Use
- Tabular Data: Random Forests / Gradient Boosting.
- Preprocessing:
StandardScaler,LabelEncoder. - Small Data: When Deep Learning is overkill.
Core Concepts
Estimators
Everything implements .fit(X, y) and .predict(X).
Pipelines
Chaining preprocessing and modeling: Pipeline([('scaler', StandardScaler()), ('svc', SVC())]).
Array API
Passing PyTorch tensors directly to Scikit-learn without converting to NumPy (keeping data on GPU).
Best Practices (2025)
Do:
- Use Pipelines: Prevent data leakage during cross-validation.
- Use
HistGradientBoostingClassifier: It is much faster than standard extraction implementation (inspired by LightGBM).
Don't:
- Don't use for Images/Audio: Use PyTorch/DL for unstructured data.
References
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: G1Joshi
- Source: G1Joshi/Agent-Skills
- License: MIT
- Homepage: https://skills.sh/g1joshi/agent-skills
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.