claude-cookbooks_skill

Overview

This skill bundles practical code examples, tutorials, and best practices for integrating and building with the Claude API. It focuses on Python examples, multimodal features, tool/function calling, RAG, agents, and production patterns. Use it to learn APIs quickly and to bootstrap Claude-powered applications with copy-pasteable code.

How this skill works

The skill organizes concise cookbook entries by capability: core tasks (classification, summarization, text-to-SQL), tool use and function schemas, multimodal image handling, retrieval-augmented generation, and advanced agent patterns. Each entry provides ready-to-run Python snippets, recommended model choices, prompt patterns, and operational advice like caching, retries, and cost controls. Examples show end-to-end flows: defining tools, invoking Claude, processing vision inputs, and integrating vector stores.

When to use it

• Learning the Claude API and SDK with hands-on examples
• Building Claude-powered chatbots, assistants, or customer service agents
• Implementing function calling, tool orchestration, or multi-tool workflows
• Adding multimodal features: image analysis, OCR, or visual Q&A
• Implementing RAG with embeddings and vector stores
• Designing agent architectures, sub-agents, or advanced prompt patterns

Best practices

• Choose the right model for the task (balance, speed, or capability) and set sensible max_tokens
• Use prompt caching for large system prompts and repeated context to reduce cost and latency
• Define clear tool schemas and validate inputs/outputs to avoid runtime errors
• Implement retry with exponential backoff and rate-limit handling
• Structure outputs with JSON mode for reliable parsing and downstream processing
• Use high-quality images and respect size/format limits for multimodal calls

Example use cases

• Customer service agent that calls CRM and ticketing tools via defined function schemas
• RAG-powered knowledge base search using vector DBs (Pinecone, FAISS) to augment answers
• Document processing pipeline: OCR via vision API, extraction, and summarization
• Multi-agent system that routes tasks to specialized sub-agents and aggregates results
• Text-to-SQL interface that maps natural language questions to validated SQL queries