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Found 2,408 Skills
Use when implementing data analysis pipelines, statistical tests, or bioinformatics workflows in code (Python/R), particularly for genomics, transcriptomics, proteomics, or other -omics data.
Optimize code performance through iterative improvements (max 2 rounds). Benchmark execution time and memory usage, compare against baseline implementations, and generate detailed optimization reports. Supports C++, Python, Java, Rust, and other languages.
Logback - flexible and powerful logging framework for Java and Spring Boot applications. Successor to Log4j with native SLF4J support, async logging, and automatic file rotation. USE WHEN: user mentions "logback", "spring boot logging", "java logging configuration", asks about "logback-spring.xml", "rolling file appender", "async logging in java" DO NOT USE FOR: SLF4J API usage - use `slf4j` instead, Log4j2 - use separate Log4j2 skill, Node.js logging - use `winston` or `pino` instead, Python logging - use `python-logging` instead
Use when designing APIs, Architecture, Security, or Scalability for Node, Python, Go, or Java backend systems.
Build agentic UIs using AG-UI protocol with Pydantic AI (Python backend) and CopilotKit (React/Next.js frontend). Use when creating AI-powered applications that need bidirectional agent-UI communication, shared state between frontend and backend, human-in-the-loop workflows, tool-based generative UI, or predictive state updates. Triggers on requests involving CopilotKit hooks (useCoAgent, useCopilotAction, useCoAgentStateRender), pydantic_ai with ag_ui adapters, or building chat interfaces with backend AI agents.
Enterprise-grade access control patterns to prevent broken access control, IDOR, tenant data leaks, privilege escalation, and admin route exposure. Covers RBAC, ABAC, ownership checks, multi-tenant scoping, policy middleware, row-level security, and audit logging for Node.js, Cloudflare Workers, Laravel, and Python. Use when protecting routes, implementing roles, reviewing authorization, or fixing OWASP A01 findings.
Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Enables building and training quantum circuits with automatic differentiation, seamless integration with PyTorch/JAX/TensorFlow, and device-independent execution across simulators and quantum hardware (IBM, Amazon Braket, Google, Rigetti, IonQ, etc.). Use when working with quantum circuits, variational quantum algorithms (VQE, QAOA), quantum neural networks, hybrid quantum-classical models, molecular simulations, quantum chemistry calculations, or any quantum computing tasks requiring gradient-based optimization, hardware-agnostic programming, or quantum machine learning workflows.
Deterministic syntax for Frappe Whitelisted Methods (Python API endpoints) for v14/v15/v16. Use when Claude needs to generate code for API functions, REST endpoints, @frappe.whitelist() decorator, frappe.call() or frm.call() invocations, permission checks in APIs, error handling patterns, or when questions concern API structure, response formats, or client-server communication. Triggers: whitelisted, API endpoint, frappe.call, frm.call, REST API, @frappe.whitelist, allow_guest, API method.
Docker containerization patterns for Python/React projects. Use when creating or modifying Dockerfiles, optimizing image size, setting up Docker Compose for local development, or hardening container security. Covers multi-stage builds for Python (python:3.12-slim) and React (node:20-alpine -> nginx:alpine), layer optimization, .dockerignore, non-root user, security scanning with Trivy, Docker Compose for dev (backend + frontend + PostgreSQL + Redis), and image tagging strategy. Does NOT cover deployment orchestration (use deployment-pipeline).
A Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Great for exploring relationships between variables and visualizing distributions. Use for statistical data visualization, exploratory data analysis (EDA), relationship plots, distribution plots, categorical comparisons, regression visualization, heatmaps, cluster maps, and creating publication-quality statistical graphics from Pandas DataFrames.
Composable transformations of Python+NumPy programs. Differentiate, vectorize, JIT-compile to GPU/TPU. Built for high-performance machine learning research and complex scientific simulations. Use for automatic differentiation, GPU/TPU acceleration, higher-order derivatives, physics-informed machine learning, differentiable simulations, and automatic vectorization.
FastAPI advanced patterns including lifespan, dependencies, middleware, and Pydantic settings. Use when configuring FastAPI lifespan events, creating dependency injection, building Starlette middleware, or managing async Python services with uvicorn.