Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/g1joshi/agent-skills/scikit-learnnpx skills add G1Joshi/Agent-Skills --skill scikit-learngit clone --depth 1 https://github.com/G1Joshi/Agent-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/g1joshi/agent-skills/scikit-learn)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/scikit-learn"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/scikit-learn.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00018 | $0.00297 |
| Opus 5 | $0.00009 | $0.00148 |
| Sonnet 5 | $0.00004 | $0.00059 |
| Haiku 4.5 | $0.00002 | $0.00030 |
Grade A, and why
scikit-learn scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 44 lines · 18 tokens per session scan A 191a35d3997e
scikit-learn is a skill published in the GitHub repository G1Joshi/Agent-Skills (12 stars, last pushed 6mo ago), licensed MIT. It adds 18 tokens to every session and 297 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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