scikit-learn
Classical machine learning with scikit-learn. Use for classification, regression, clustering, dimensionality reduction, preprocessing, model evaluation, and building robust ML pipelines in Python.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
157 skills found
Classical machine learning with scikit-learn. Use for classification, regression, clustering, dimensionality reduction, preprocessing, model evaluation, and building robust ML pipelines in Python.
High-performance document intelligence library for extracting text, tables, code, and metadata from 91+ file formats, with OCR and LLM-ready output.
Create interactive, custom data visualizations using d3.js — including charts, graphs, and network diagrams. Ideal for when you need fine-grained control over visual elements, transitions, and interactions.
Perform deep security analysis on codebases using CodeQL for interprocedural data flow, taint tracking, and automated vulnerability detection across multiple languages.
Load, validate, and preprocess weekly insurance policy CSV data with intelligent period detection and standardization.
Decision framework for choosing between MCP tools and direct API skills to optimize agent performance, cost, and efficiency.
Process massive files and large codebases (10M+ tokens) by recursively chunking, sub-querying, and aggregating results to overcome LLM context limits.
Reverse engineer web APIs by capturing browser traffic (HAR files) and generating production-ready Python API clients for automation and data extraction.
Chrome DevTools MCP server for AI-driven browser automation, testing, and debugging via Puppeteer. Features input automation, visual snapshots, performance tracing, and network inspection.
Provides data-driven trading strategies for cryptocurrencies using Binance market data, technical analysis indicators, and aggregated crypto market sentiment.
A guide for building high-quality MCP (Model Context Protocol) servers in Python or TypeScript to integrate external APIs and services into LLM workflows.
Meta-skill for generating publication-ready scientific figures, multi-panel layouts, and journal-compliant visualizations using Python's matplotlib, seaborn, and plotly libraries.