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Found 4,094 Skills
React/TypeScript frontend implementation patterns. Use during the implementation phase when creating or modifying React components, custom hooks, pages, data fetching logic with TanStack Query, forms, or routing. Covers component structure, hooks rules, custom hook design (useAuth, useDebounce, usePagination), TypeScript strict-mode conventions, form handling, accessibility requirements, and project structure. Does NOT cover testing (use react-testing-patterns), E2E testing (use e2e-testing), or deployment.
Use when implementing BGTaskScheduler, debugging background tasks that never run, understanding why tasks terminate early, or testing background execution - systematic task lifecycle management with proper registration, expiration handling, and Swift 6 cancellation patterns
Build Solana programs with Anchor framework or native Rust. Use when developing Solana smart contracts, implementing token operations, testing programs, deploying to networks, or working with Solana development. Covers both high-level Anchor framework (recommended) and low-level native Rust for advanced use cases.
Comprehensive macOS browser automation using PyXA, Playwright, Selenium, and Puppeteer for desktop web testing, scraping, and workflow automation. Use when asked to "automate web browsers", "Selenium Chrome automation", "Playwright testing", "Puppeteer scraping", or "cross-browser automation". Supports Chrome, Edge, Brave, Arc browsers.
Comprehensive toolkit for validating, linting, testing, and analyzing Helm charts and their rendered Kubernetes resources. Use this skill when working with Helm charts, validating templates, debugging chart issues, working with Custom Resource Definitions (CRDs) that require documentation lookup, or checking Helm best practices.
Consult this skill for async Python patterns and concurrency. Use when building async APIs, concurrent systems, I/O-bound applications, implementing rate limiting, async context managers. Do not use when CPU-bound optimization - use python-performance instead. DO NOT use when: testing async code - use python-testing async module.
Provides domain-specific best practices for Node.js development with TypeScript, covering type stripping, async patterns, error handling, streams, modules, testing, performance, caching, logging, and more. Use when setting up Node.js projects with native TypeScript support, configuring type stripping (--experimental-strip-types), writing Node 22+ TypeScript without a build step, or when the user mentions 'native TypeScript in Node', 'strip types', 'Node 22 TypeScript', '.ts files without compilation', 'ts-node alternative', or needs guidance on error handling, graceful shutdown, flaky tests, profiling, or environment configuration in Node.js. Helps configure tsconfig.json for type stripping, set up package.json scripts, handle module resolution and import extensions, and apply robust patterns across the full Node.js stack.
Execute a comprehensive Flutter Project Health Audit. Analyzes tech stack, architecture, state management, testing, code quality, CI/CD, and documentation. Produces a Google Docs-ready report with section scores and weighted overall score. Use when the user asks to audit a Flutter project, run a health check, evaluate project quality, or assess technical debt. Triggers on: 'flutter audit', 'health audit', 'project audit', 'flutter health', 'tech debt assessment', 'project quality check'.
Expert guidance for Spring Boot application development with best practices for RESTful APIs, testing, security, and deployment
Essential development workflow agents for code review, debugging, testing, documentation, and git operations. Includes 7 specialized agents with strong auto-discovery triggers. Use when: setting up development workflows, code reviews, debugging errors, writing tests, generating documentation, creating commits, or verifying builds.
Clean NestJS API development with TypeScript following SOLID principles, modular architecture, and comprehensive testing practices.
Build autonomous game-playing agents using AI and reinforcement learning. Covers game environments, agent decision-making, strategy development, and performance optimization. Use when creating game-playing bots, testing game AI, strategic decision-making systems, or game theory applications.