Claude Skills

[EXPLORE] Coordinate multiple Claude instances across worktrees. Use when managing parallel development.

Use this skill for implementing Motion (Framer Motion) animations in React applications. Covers animation presets, page transitions, modal animations, list stagger effects, hover interactions, skeleton loaders, and RTL-aware animation patterns.

Enforce 2026 folder structure standards - feature-based organization, max nesting depth, unidirectional imports. Blocks structural violations. Use when creating files or reviewing project architecture.

Use when building Next.js 16+ apps with React Server Components. Covers App Router, streaming SSR, Server Actions, and React 19 patterns for server-first architecture.

CC 2.1.16 Task Management patterns with TaskCreate, TaskUpdate, TaskGet, TaskList tools. Decompose complex work into trackable tasks with dependency chains. Use when managing multi-step implementations, coordinating parallel work, or tracking completion status.

Database version control and change management patterns. Use when managing schema history, coordinating database changes across environments, implementing audit trails, or versioning database objects.

Use when creating or improving golden datasets for AI evaluation. Defines quality criteria, curation workflows, and multi-agent analysis patterns for test data.

Advanced Celery patterns including canvas workflows, priority queues, rate limiting, multi-queue routing, and production monitoring. Use when implementing complex task orchestration, task prioritization, or enterprise-grade background processing.

Advanced Vite 7+ patterns including Environment API, plugin development, SSR configuration, library mode, and build optimization. Use when customizing build pipelines, creating plugins, or configuring multi-environment builds.

Use when assessing task complexity, before starting complex tasks, or when stuck after multiple attempts. Provides quality-gates scoring (1-5) and escalation workflows.

SOLID principles, hexagonal architecture, ports and adapters, and DDD tactical patterns for maintainable backends. Use when implementing clean architecture, decoupling services, separating domain logic, or creating testable architecture.

Use when building secure AI pipelines or hardening LLM integrations. Defense-in-depth implements 8 validation layers from edge to storage with no single point of failure.

Database and HTTP connection pooling patterns for Python async applications. Use when configuring asyncpg pools, aiohttp sessions, or optimizing connection lifecycle in high-concurrency services.

Use when conversation context is too long, hitting token limits, or responses are degrading. Compresses history while preserving critical information using anchored summarization and probe-based validation.

Input validation and sanitization patterns. Use when validating user input, preventing injection attacks, implementing allowlists, or sanitizing HTML/SQL/command inputs.

Core Web Vitals optimization for LCP, INP, CLS with 2026 thresholds, performance budgets, and RUM. Use when improving page performance, diagnosing CWV regressions, or setting performance budgets.

CQRS (Command Query Responsibility Segregation) patterns for separating read and write models. Use when optimizing read-heavy systems, implementing event sourcing, or building systems with different read/write scaling requirements.

Slack MCP server integration patterns. Use when setting up team notifications, PR alerts, or CI status updates via Slack bot token

WCAG 2.2 AA accessibility compliance patterns for web applications. Use when auditing accessibility or implementing WCAG requirements.

LangGraph supervisor-worker pattern. Use when building central coordinator agents that route to specialized workers, implementing round-robin or priority-based agent dispatch.

[EXPLORE] Deep codebase exploration with parallel agents. Use when exploring a repo or discovering architecture.

LLM streaming response patterns. Use when implementing real-time token streaming, Server-Sent Events for AI responses, or streaming with tool calls.

Use when conversation context is too long, hitting token limits, or responses are degrading. Compresses history while preserving critical information using anchored summarization and probe-based validation.

CLIP, SigLIP 2, Voyage multimodal-3 patterns for image+text retrieval, cross-modal search, and multimodal document chunking. Use when building RAG with images, implementing visual search, or hybrid retrieval.