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 skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-scikit-learngit clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-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/alterlab-ieu/alterlab-academic-skills/alterlab-scikit-learn)<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-scikit-learn"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-scikit-learn/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-scikit-learn"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-scikit-learn.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00079 | $0.02670 |
| Opus 5.5 | $0.00032 | $0.01068 |
| Sonnet 5.5 | $0.00016 | $0.00534 |
| Haiku 4.5 | $0.00008 | $0.00267 |
Grade A, and why
alterlab-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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 296 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scikit-learn
Overview
This skill provides comprehensive guidance for machine learning tasks using scikit-learn, the industry-standard Python library for classical machine learning. Use this skill for classification, regression, clustering, dimensionality reduction, preprocessing, model evaluation, and building production-ready ML pipelines.
Installation
# Install scikit-learn using uv
uv pip install scikit-learn
# Optional: Install visualization dependencies
uv pip install matplotlib seaborn
# Commonly used with
uv pip install pandas numpy
When to Use This Skill
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
Does NOT Trigger
| Scenario | Use Instead |
|---|---|
| Bayesian models with posteriors, credible intervals, or MCMC diagnostics | alterlab-pymc |
| Censored time-to-event outcomes (Cox models, survival forests, C-index) | alterlab-scikit-survival |
UMAP embeddings with n_neighbors / min_dist tuning |
alterlab-umap |
| Explaining a fitted model's predictions with SHAP values and plots | alterlab-shap |
| Coefficient tables, p-values, and residual diagnostics for inference | alterlab-statsmodels |
Quick Start
Minimal classification flow: stratified train_test_split → StandardScaler (fit on train only) → fit estimator → classification_report. For mixed numeric/categorical data, wrap preprocessing in a ColumnTransformer inside a Pipeline so it cross-validates without leakage.
See references/worked_examples.md for full copy-paste classification and mixed-data pipeline examples.
What ships with it
11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- evals/evals.json 6.1 KB
- references/best_practices_and_troubleshooting.md 2.2 KB
- references/model_evaluation.md 15 KB
- references/pipelines_and_composition.md 14 KB
- references/preprocessing.md 16 KB
- references/quick_reference.md 11 KB
- references/supervised_learning.md 11 KB
- references/unsupervised_learning.md 15 KB
- references/worked_examples.md 5.3 KB
- scripts/classification_pipeline.py 7.9 KB runs code
- scripts/clustering_analysis.py 11 KB runs code
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.
- 1mo ago Changed · +19 lines cbac16acc80d
- 1mo ago First seen · 277 lines · 79 tokens per session scan A 522d23ca14ac
alterlab-scikit-learn is a skill published in the GitHub repository AlterLab-IEU/AlterLab-Academic-Skills (78 stars, last pushed yesterday), licensed MIT. It adds 79 tokens to every session and 2,670 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-03.
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