llm-structured-output

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 form validation or API input validation (use zod-validation-expert instead)
• The user needs prompt engineering for better text quality (not structure)
• The user wants to call real external tools/APIs (this skill covers using tool_use as a structured output hack, not actual tool orchestration)

Core Workflow

1. Identify the target schema. Ask the user what fields they need extracted. Define every field with its type, whether it's required or optional, and valid enum values if applicable. Do not proceed without a concrete schema.