api development向けのClaudeスキルを発見してください。83個のスキルを閲覧し、AIワークフローに最適な機能を見つけましょう。
Implements high-performance microservice communication using gRPC and Protocol Buffers for Python-based backend systems.
Guides system architecture planning through structured questions across scalability, security, data, and operational dimensions.
Implements production-ready FastAPI architectures including lifespan management, dependency injection, and specialized middleware.
Implements end-to-end type safety and runtime validation using Zod, tRPC, and Prisma for robust TypeScript and Python applications.
Simplifies AI workflow development using Python decorators for orchestration, parallel execution, and state persistence.
Implements robust saga patterns to manage distributed transactions and maintain consistency across microservices through orchestration and choreography.
Implements production-ready tool calling patterns and structured output schemas for AI agents.
Implements durable, fault-tolerant distributed workflows and saga patterns using the Temporal.io execution engine.
Evaluates architectural plans across five critical dimensions to prevent technical debt and scalability issues before implementation.
Implements RFC 9457 Problem Details to provide standardized, machine-readable HTTP API error responses for modern backend services.
Implements high-performance Python concurrency using modern structured patterns, TaskGroups, and robust error handling for Python 3.11+ applications.
Implements high-performance data fetching, caching, and optimistic UI updates using TanStack Query v5 best practices.
Protects web services and APIs from abuse by implementing distributed rate limiting strategies using Redis and modern algorithms.
Develops custom Model Context Protocol (MCP) servers to extend Claude's capabilities with specialized tools, data resources, and prompt templates.
Enforces FastAPI Clean Architecture principles by validating layer separation and dependency injection patterns in real-time.
Implements production-grade fault tolerance and resilience patterns for distributed systems and LLM-based workflows.
Implements robust asynchronous communication and event-driven architectures using Kafka, RabbitMQ, Redis, and Postgres.
Provides comprehensive, production-ready design patterns and architectural standards for REST, GraphQL, and gRPC services.
Manages persistent workflow states, fault-tolerant execution recovery, and multi-threaded conversation memory for LangGraph agents.
Implements production-ready real-time data streaming using Server-Sent Events (SSE), WebSockets, and ReadableStream APIs.
Builds type-safe, production-ready GraphQL APIs using the Strawberry library and FastAPI integration.
Implements maintainable, decoupled backend systems using SOLID principles, hexagonal architecture, and Domain-Driven Design (DDD).
Implements high-performance asynchronous messaging and event-driven architectures using RabbitMQ, Kafka, and Redis Streams.
Builds type-safe, production-ready GraphQL APIs in Python using Strawberry and FastAPI with advanced patterns for performance and scalability.
Implements high-performance Python concurrency using modern 3.11+ asyncio features and structured concurrency patterns.
Implements high-performance Redis caching patterns including cache-aside, write-through, and stampede prevention for backend services.
Models complex business logic using tactical DDD patterns including entities, value objects, and bounded contexts.
Implements production-grade Model Context Protocol patterns for high-performance tool orchestration and resource management.
Implements production-grade fault tolerance and reliability patterns for distributed systems and LLM integrations.
Standardizes HTTP API error responses using the machine-readable RFC 9457 Problem Details format for robust backend services.
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