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Found 195 Skills
Design production-ready SDKs with retry logic, error handling, pagination, and multi-language support. Use when building client libraries for APIs or creating developer-facing SDK interfaces.
Reactuse delivers production-ready hooks that solve real-world problems. Built with a TypeScript-first approach, SSR compatibility, and tree-shaking optimization for modern React applications.
End-to-end UI/UX design skill that takes a feature from vague idea to production-ready UI by moving sequentially through research, requirements, information architecture, wireframing, and visual design without skipping steps.
Autonomous skill creation agent that analyzes requests, automatically selects the best creation method (documentation scraping via Skill_Seekers, manual TDD construction, or hybrid), ensures quality compliance with Anthropic best practices, and delivers production-ready skills without requiring user decision-making or navigation
1600+ lines of performance optimization mastery - profiling, rendering, memory, network, battery, APK size with production-ready code examples.
Convert Figma designs to production-ready React components with Tailwind CSS. Use when user provides a Figma URL, asks to convert Figma designs to React/code, wants to extract components from Figma, or mentions "vibefigma". Requires a Figma access token (via --token flag, FIGMA_TOKEN env var, or .env file).
Comprehensive guide for production-ready Python backend development and software architecture at scale. Use when designing APIs, building backend services, creating microservices, structuring Python projects, implementing database patterns, writing async code, or any Python backend/server-side development task. Covers Clean Architecture, Domain-Driven Design, Event-Driven Architecture, FastAPI/Django patterns, database design, caching strategies, observability, security, testing strategies, and deployment patterns for high-scale production systems.
Expert guidance for building production-ready FastAPI applications with modular architecture where each business domain is an independent module with own routes, models, schemas, services, cache, and migrations. Uses UV + pyproject.toml for modern Python dependency management, project name subdirectory for clean workspace organization, structlog (JSON+colored logging), pydantic-settings configuration, auto-discovery module loader, async SQLAlchemy with PostgreSQL, per-module Alembic migrations, Redis/memory cache with module-specific namespaces, central httpx client, OpenTelemetry/Prometheus observability, conversation ID tracking (X-Conversation-ID header+cookie), conditional Keycloak/app-based RBAC authentication, DDD/clean code principles, and automation scripts for rapid module development. Use when user requests FastAPI project setup, modular architecture, independent module development, microservice architecture, async database operations, caching strategies, logging patterns, configuration management, authentication systems, observability implementation, or enterprise Python web services. Supports max 3-4 route nesting depth, cache invalidation patterns, inter-module communication via service layer, and comprehensive error handling workflows.
Comprehensive Biome (biomejs.dev) integration for professional TypeScript/JavaScript development. Use for linting, formatting, code quality, and flawless Biome integration into codebases. Covers installation, configuration, migration from ESLint/Prettier, all linter rules, formatter options, CLI usage, editor integration, monorepo setup, and CI/CD integration. Use when working with Biome tooling, configuring biome.json, setting up linting/formatting, migrating projects, debugging Biome issues, or implementing production-ready Biome workflows.
Build production-ready MCP clients in TypeScript or Python. Handles connection lifecycle, transport abstraction, tool orchestration, security, and error handling. Use for integrating LLM applications with MCP servers.
Expert MCP (Model Context Protocol) orchestration with n8n workflow automation. Master bidirectional MCP integration, expose n8n workflows as AI agent tools, consume MCP servers in workflows, build agentic systems, orchestrate multi-agent workflows, and create production-ready AI-powered automation pipelines with Claude Code integration.
Create production-ready skills from expert knowledge. Extracts domain expertise and system ontologies, uses scripts for deterministic work, loads knowledge progressively. Use when building skills that must work reliably in production.