发现api development类别的 Claude 技能。浏览 83 个技能,找到适合您 AI 工作流程的完美功能。
Optimizes database and HTTP performance using production-ready async connection pooling patterns for Python applications.
Implements high-performance server state management patterns for React using TanStack Query v5.
Implements production-ready tool calling patterns and structured output schemas for AI agents.
Evaluates architectural plans across five critical dimensions to prevent technical debt and scalability issues before implementation.
Implements production-grade fault tolerance and resilience patterns for distributed systems and LLM-based workflows.
Implements RFC 9457 Problem Details to provide standardized, machine-readable HTTP API error responses for modern backend services.
Guides system architecture planning through structured questions across scalability, security, data, and operational dimensions.
Implements high-performance microservice communication using gRPC and Protocol Buffers for Python-based backend systems.
Provides comprehensive, production-ready design patterns and architectural standards for REST, GraphQL, and gRPC services.
Implements high-performance microservice communication in Python using gRPC and Protobuf for robust, type-safe APIs.
Implements high-performance Python concurrency using modern structured patterns, TaskGroups, and robust error handling for Python 3.11+ applications.
Implements robust idempotency and deduplication patterns for APIs and distributed systems to ensure exactly-once execution.
Manages persistent workflow states, fault-tolerant execution recovery, and multi-threaded conversation memory for LangGraph agents.
Implements high-performance data fetching, caching, and optimistic UI updates using TanStack Query v5 best practices.
Standardizes the architecture and implementation of production-grade REST, GraphQL, and gRPC APIs using industry best practices.
Implements production-grade fault tolerance and reliability patterns for distributed systems and LLM integrations.
Builds type-safe, production-ready GraphQL APIs in Python using Strawberry and FastAPI with advanced patterns for performance and scalability.
Implements exclusive resource access and prevents race conditions across distributed service instances using Redis and PostgreSQL.
Implements high-performance Python concurrency using modern 3.11+ asyncio features and structured concurrency patterns.
Implements Domain-Driven Design (DDD) aggregate patterns to maintain data consistency and enforce business invariants in complex backend systems.
Implements Command Query Responsibility Segregation (CQRS) to optimize data access by separating read and write operations into distinct models.
Implements high-performance real-time token streaming and Server-Sent Events (SSE) for AI-powered applications.
Enforces FastAPI Clean Architecture principles by validating layer separation and dependency injection patterns in real-time.
Builds type-safe, production-ready GraphQL APIs using the Strawberry library and FastAPI integration.
Manages system architecture changes through automated impact analysis, ADR generation, and phased migration planning.
Accelerates the creation and deployment of Model Context Protocol (MCP) servers using Python and TypeScript.
Analyzes GraphQL operations to identify complexity metrics, query depth issues, and potential performance bottlenecks.
Validates GraphQL operations against schemas while enforcing performance and complexity constraints.
Identifies breaking changes and validates GraphQL schema migrations across branches, files, and live endpoints.
Executes raw EVM transactions and custom contract calls across multiple blockchains using the Bankr DeFi infrastructure.
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