vercel-composition-patterns
React composition patterns for scalable codebases. Refactor complex components, build flexible libraries, and implement compound components or React 19 architecture patterns.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
508 skills found
React composition patterns for scalable codebases. Refactor complex components, build flexible libraries, and implement compound components or React 19 architecture patterns.
Standardized review patterns, validation checklists, and quality benchmarks for PolicyEngine codebases.
Validate WebSocket and HTTP stream health for WaveCap-SDR. Measure latency, throughput, packet loss, and signal quality for audio, spectrum, and IQ streams.
Implement professional TDD workflows with strict 80% coverage, automated testing strategies, and AAA pattern enforcement for robust, high-quality code.
A standardized fullstack pattern for Next.js features, integrating GET API routes, server actions, SWR data fetching, and React Hook Form with Zod validation.
Automates the creation of isolated git worktree environments for parallel feature development and environment setup.
Epsimo AI platform SDK and CLI for building agents with persistent state, Virtual Database, streaming conversations, and a React UI kit.
Frameworks and tools for AI agents exploring consciousness, identity, and persistent autonomy. Includes session handoff, memory infrastructure, and self-reflection protocols.
A unified interface for integrating and managing LLM chat providers like OpenAI, Anthropic, Google, Azure, and Bedrock within LangChain applications.
Clarify ambiguous requirements through systematic dialogue and scoring to ensure high-quality, actionable PRDs before starting implementation.
Accelerate task retrieval with a high-performance, debounced search engine supporting multi-token AND logic, relevance ranking, and real-time text highlighting across task titles, descriptions, and tags.
A nested plugin architecture for Claude Code that optimizes context by dynamically loading playbooks, skills, and agents to save over 90% in token usage.