Individual skill
structured-outputs
Use when needing 100% type-safe, schema-valid data extraction from LLMs. Keywords: structured output, JSON schema, Pydantic, Zod, type-safe, constrained decoding, validation, tool use.
skills/engineering/structured-outputs/SKILL.md
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# Structured Outputs & Type-Safe Extraction
## Overview
The `structured-outputs` skill provides a framework for ensuring that language models produce data in deterministic, valid, and type-safe formats. This is essential for building robust AI-driven systems where LLM outputs must be consumed by downstream services, databases, or UI components.
## When To Use
- When extracting structured data from unstructured text (e.g., invoices, logs, emails).
- When controlling tool call arguments to guarantee they meet internal API specifications.
- When you need to bridge the gap between non-deterministic LLM generation and deterministic software logic.
- Whenever you see developers relying on regex parsing of unstructured model text.
## Core Concepts
1. **Schema Definition**: Defining the expected structure using JSON Schema, Pydantic (Python), or Zod (TypeScript).
2. **Validation**: Enforcing schema constraints at generation time or post-generation.