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 ihatesea69/kiro-kit --skill scikit-learngit clone --depth 1 https://github.com/ihatesea69/kiro-kitWrote 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/ihatesea69/kiro-kit/scikit-learn)<a href="https://agentmods.dev/skills/ihatesea69/kiro-kit/scikit-learn"><img src="https://agentmods.dev/badge/skills/ihatesea69/kiro-kit/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/ihatesea69/kiro-kit/scikit-learn"><img src="https://agentmods.dev/badge/skills/ihatesea69/kiro-kit/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 | $0.00031 | $0.00271 |
| Opus 5 | $0.00015 | $0.00135 |
| Sonnet 5 | $0.00006 | $0.00054 |
| Haiku 4.5 | $0.00003 | $0.00027 |
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 5d 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
Activate this skill when working with classical ML algorithms.
When to Use
- Building classification or regression models
- Feature engineering and selection
- Implementing ML pipelines with preprocessing
- Cross-validation and hyperparameter tuning
- Clustering and dimensionality reduction
Patterns
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import cross_val_score
preprocessor = ColumnTransformer([
("num", StandardScaler(), numeric_features),
("cat", OneHotEncoder(handle_unknown="ignore"), categorical_features),
])
pipeline = Pipeline([
("preprocessor", preprocessor),
("classifier", GradientBoostingClassifier(n_estimators=200)),
])
scores = cross_val_score(pipeline, X, y, cv=5, scoring="f1_macro")
Rules
- Always split data before any preprocessing
- Use pipelines to prevent data leakage
- Cross-validate before reporting metrics
- Start simple (LogisticRegression) before complex models
- Document feature engineering decisions
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.
- 5d ago First seen · 48 lines · 31 tokens per session scan A c4ae2cf36ecd
scikit-learn is a skill published in the GitHub repository ihatesea69/kiro-kit (18 stars, last pushed 20d ago), licensed MIT. It adds 31 tokens to every session and 271 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 2026-09-03.
Other skills, from other repositories
anthropic-python
Anthropic Python SDK for Claude API integration. Covers messages API, streaming, tool use, vision, error handling, and best practices. Use when building Python applications that call the Claude API. USE WHEN: user mentions "anthropic", "claude api", "anthropic sdk", "anthropic.Anthropic()", "client.messages.create"…
pydantic-ai-model-integration
Configure LLM providers, use fallback models, handle streaming, and manage model settings in PydanticAI. Use when selecting models, implementing resilience, or optimizing API calls.
pypi-release
This skill should be used when releasing tunacode-cli to PyPI. It keeps the existing local release checks, then hands the actual PyPI upload to a GitHub Actions workflow that uses the repository's PYPIAPITOKEN secret.
pytorch-training
PyTorch model-building conventions and a neural-net training debug checklist. Use this skill whenever writing or reviewing PyTorch code that defines a model (nn.Linear, nn.Conv2d, BatchNorm) or trains one (training loop, optimizer, LR schedule), and ESPECIALLY when debugging training problems — loss not converging…
schliff
Deterministic linter and scorer for instruction files — SKILL.md, AGENTS.md, CLAUDE.md. No model in the loop: the same bytes score the same everywhere. Use for linting, scoring, auditing or CI-gating an instruction file, and for checking whether the commands a file promises actually resolve in the repo. Trigger…
sap-cloud-sdk-ai-python
Integrates the SAP Cloud SDK for AI for Python (sap-ai-sdk-gen, formerly generative-ai-hub-sdk) into Python applications. Use when building Python apps with SAP AI Core, Generative AI Hub, or the Orchestration Service: chat completion, embeddings, streaming, LangChain integration, templating, content filtering, data…