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structured-outputs

Validate LLM outputs against Pydantic schemas with function calling.

Updated Apr 8, 2026
Search Tags:#pydantic#validation#llm#parsing#function-calling#structured-output#json-schema
One-click install
npx skills add https://github.com/bmsull560/Fabric_4L --skill structured-outputs-bmsull560
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: structured-outputs
Source: https://github.com/bmsull560/Fabric_4L/tree/main/.windsurf/skills/structured-outputs
Command: npx skills add https://github.com/bmsull560/Fabric_4L --skill structured-outputs-bmsull560

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Ensures LLM responses conform to a well-defined data contract by validating outputs against Pydantic models, mitigating hallucinations and format drift.

Core Features & Use Cases

  • Pydantic-based response models for strict validation and typed results.
  • OpenAI and Anthropic function-calling integration with JSON schema enforcement.
  • Fallback handling to gracefully degrade when validation fails.

Quick Start

Provide a Pydantic schema and enable function calling to parse LLM outputs into the model.

Frequently Asked Questions about structured-outputs

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I validate LLM outputs against a strict schema?▼

You validate LLM outputs against a strict schema by parsing responses into Pydantic models, enforcing a data contract that guarantees typed results and mitigates format drift or hallucinations.

What is the best way to parse LLM responses into Pydantic models?▼

Parsing LLM responses into Pydantic models is best achieved by enabling OpenAI and Anthropic function-calling workflows with JSON schema enforcement to guarantee typed, verifiable extraction results.

Does this approach work with both OpenAI and Anthropic function calling?▼

Yes, structured validation works with both OpenAI and Anthropic by applying their respective function-calling workflows and JSON schema validation to enforce strict Pydantic response models.

How do I handle fallbacks when LLM structured output validation fails?▼

When structured output validation fails, you handle fallbacks by implementing fallback handling logic to gracefully degrade and manage non-conforming LLM responses that fail Pydantic model validation.

Why do I need JSON schema validation for LLM extraction tasks?▼

You need JSON schema validation for LLM extraction tasks to guarantee typed, verifiable results, ensuring responses conform to a well-defined Pydantic data contract and preventing format drift.

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