domains / families / software-engineering / llm-structured-output

llm-structured-output

authentic

Get reliable JSON, enums, and typed objects from LLMs using response_format, tool_use, and schema-constrained decoding across OpenAI, Anthropic, and Google APIs.

When to use

• 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

When not to use

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

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