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.
465 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.
A comprehensive configuration suite for Claude Code, featuring production-grade agents, skills, hooks, and automated workflows optimized for high-intensity development.
Implement production-grade AI agents with LangGraph, tool-calling guardrails, SSE streaming, and episodic memory. Includes anti-patterns, fix pairs, and stateful architecture patterns.
Efficiently extract, filter, and transform specific fields from JSON files using jq, saving up to 95% of context window usage compared to reading full files.
Framework for building professional competitive landscape decks, including market positioning, peer benchmarking, and strategic synthesis for finance professionals.
Plan features through an interactive, multi-step process that generates comprehensive Product Requirements Documents (PRDs) with user stories, acceptance criteria, and technical specifications.
Identify and document Customer Problems (CP) from business context. Use when starting requirements engineering or when stakeholders describe solutions instead of problems. Step 1 of Problem-Based SRS methodology.
Anthropic Claude AI models for high-performance coding, large-context analysis, and GUI interaction.
Master cross-language error handling patterns: exceptions, Result types, and graceful degradation for resilient application development.
Execute implementation plans in separate sessions with review checkpoints, ensuring task-by-task verification and robust code quality.
Retrieve real-time library documentation, code examples, and technical guidance using the Context7 API for frameworks like React, FastAPI, and Next.js.
Decision framework for choosing between MCP tools and direct API skills to optimize agent performance, cost, and efficiency.