How do I achieve 90% cost savings with Claude?

By using Prompt Caching on large, static contexts like system instructions or codebases, you can reuse tokens at a significantly lower price point than standard input.

How should I handle Claude API rate limits (429 errors)?

This skill recommends implementing exponential backoff and specifically respecting the 'retry-after' header provided in the API response to maintain stability.

What are Structured Outputs in the Claude API?

Structured Outputs ensure Claude's responses strictly adhere to a provided JSON schema, eliminating parsing errors and providing guaranteed data formats for your application.

Why is my prompt cache not activating?

A common cause is placing the cache_control block incorrectly. It must be placed on the last block of the cacheable content to trigger correctly.

Which models support Structured Outputs?

As of November 2025, Structured Outputs are supported on Claude Sonnet 4.5 and Claude Opus 4 using the structured-outputs-2025-11-13 beta header.

Claude API & Structured Outputs

Name: Claude API & Structured Outputs
Author: jezweb

byjezweb

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117

API Development

Streamlines Claude Messages API integration with structured outputs, prompt caching, and robust error handling patterns.

About

This skill provides expert guidance for implementing the Anthropic Messages API (v0.69.0+), focusing on high-performance and cost-effective AI development. It enables developers to implement guaranteed JSON schema validation via structured outputs, achieve up to 90% cost savings through optimized prompt caching, and manage complex streaming SSE responses. It is essential for developers building autonomous agents, data extraction pipelines, or chatbots that require strict reliability and efficient context management.

Key Features

Guaranteed JSON schema validation via Structured Outputs (v0.69.0+)
Comprehensive migration guides for model deprecations and breaking changes
Robust error handling for Rate Limits (429) and mid-stream SSE failures
117 GitHub stars
Strict Tool Use patterns for reliable function calling and agentic workflows
Prompt caching implementation for up to 90% input cost reduction

Use Cases

Building AI agents that require validated JSON data for system integration
Developing resilient streaming interfaces with proper SSE event parsing
Reducing operational costs for large-scale applications using context caching

About

Key Features

Guaranteed JSON schema validation via Structured Outputs (v0.69.0+)
Comprehensive migration guides for model deprecations and breaking changes
Robust error handling for Rate Limits (429) and mid-stream SSE failures
117 GitHub stars
Strict Tool Use patterns for reliable function calling and agentic workflows
Prompt caching implementation for up to 90% input cost reduction

Use Cases

Building AI agents that require validated JSON data for system integration
Developing resilient streaming interfaces with proper SSE event parsing
Reducing operational costs for large-scale applications using context caching