What is scikit-learn?

A guide to using scikit-learn, a Python library for building and testing machine-learning models. It covers data preparation, processing pipelines, model selection, parameter tuning, and evaluation.

Use it to build scikit-learn pipelines, choose models, tune their settings, and measure how well they perform.

It helps keep preparation and modeling steps consistent and reduces mistakes caused by applying transformations differently during training and testing.

scikit-learn — Skill by andikarachman

Install

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.

agentmods

npx agentmods add skills/andikarachman/data-science-plugin/scikit-learn

Any agent

npx skills add andikarachman/data-science-plugin --skill scikit-learn

Clone the repo

git clone --depth 1 https://github.com/andikarachman/data-science-plugin

Made for: Claude Code, Codex.

Or install ds, the plugin that ships this one along with the rest of its 19 skills, 8 commands.

Per session 42 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 3,893 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin unknown No closer match found in the catalogue.

Token cost

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	$0.00042	$0.03893
Opus 5	$0.00021	$0.01946
Sonnet 5	$0.00008	$0.00779
Haiku 4.5	$0.00004	$0.00389

Measured 3d ago against content hash ef6dd328c818, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 3d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/classification_pipeline.py, scripts/clustering_analysis.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/scikit-learn/SKILL.md · 522 lines

The source is not reproduced here

No licence file

Read it on GitHub

Files

What ships with it

8 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.

Changes

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.

3d ago First seen · 522 lines · 42 tokens per session scan A ef6dd328c818

Subscribe to this mod's changes

scikit-learn is a skill published in the GitHub repository andikarachman/data-science-plugin (14 stars, last pushed 6mo ago), with no licence file. It adds 42 tokens to every session and 3,893 once invoked, about $0.0002 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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