anthropic-api

This skill provides a comprehensive toolkit for developers integrating Anthropic's Claude API into production-grade applications. It streamlines the implementation of advanced LLM features such as prompt caching, asynchronous batch processing, and real-time streaming, ensuring both high performance and economic efficiency. Designed for software engineers and AI architects, it serves as a central knowledge hub for navigating complex model selection, API integration patterns, and best practices for Claude 3.5 and 4.5 model families.

• Expert model selection framework for matching Haiku, Sonnet, and Opus variants to specific workload requirements like classification, reasoning, or high-volume generation.

• Deep integration patterns for Python and TypeScript SDKs, covering standard messaging flows and advanced request handling.

• Advanced cost optimization strategies including ephemeral and extended prompt caching, effective use of the Batch API for 50% savings, and token counting techniques to avoid over-provisioning.

• Implementation blueprints for streaming responses, custom tool use, and handling complex multi-turn conversations efficiently.

• Production-hardened advice on anti-patterns, common developer mistakes, and error-handling strategies in AI workflows.

• Designed for use within IDE-integrated AI assistants like Claude Code, Cursor, Windsurf, and other Agentic workflows.

• Inputs typically involve specific API requirements, usage volume projections, and performance constraints; outputs provide code snippets, cost-benefit analyses, and configuration recommendations.

• Strictly prioritize model-specific constraints, such as minimum token requirements for caching and latency considerations for streaming.

• Helps bridge the gap between prototyping and scaling AI applications, ensuring that architecture decisions regarding model throughput and latency are informed by empirical pricing and performance data.