llm-structured-outputlisted

# LLM Structured Output ## What This Skill Does Extract typed, validated data from LLM API responses instead of parsing free-text. This skill covers the three main approaches: OpenAI's `response_format` with JSON Schema, Anthropic's `tool_use` block for structured extraction, and Google's `responseSchema` in Gemini. You will learn when each approach works, when it breaks, and how to build retry logic around schema validation failures that every production system encounters. ## When to Use This Skill - The user needs to extract structured data (JSON objects, arrays, enums) from an LLM response - The user is building a pipeline where LLM output feeds directly into code (database writes, API calls, UI rendering) - The user asks about `response_format`, `json_mode`, `json_object`, or `json_schema` in OpenAI - The user asks about using Anthropic's `tool_use` or `tool_result` blocks for data extraction (not for actual tool execution) - The user asks about Zod schemas with `zodResponseFormat()` from the `openai` npm package - The user needs to parse LLM output into Pydantic models using `instructor`, `marvin`, or manual validation - The user is getting malformed JSON, missing fields, or wrong types from LLM responses and needs a fix - The user asks about `controlled generation`, `constrained decoding`, or `grammar-based sampling` in local models Do NOT use this skill when: - The user wants free-form text generation (summaries, essays, chat) - The user is asking about Zod for fo