structured-generation

Name: structured-generation
Availability: InStock
Author: Entelligentsia

Constrain and validate LLM outputs to a defined JSON schema.

1|1|Updated Mar 30, 2026

Search Tags:#validation #schema #llm #typing #structured-output #data-extraction #api-enforced

Ready to run

npx skills add https://github.com/Entelligentsia/skillforge --skill structured-generation

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: structured-generation
Source: https://github.com/Entelligentsia/skillforge/tree/main/llm-patterns/skills/structured-generation
Command: npx skills add https://github.com/Entelligentsia/skillforge --skill structured-generation

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

LLM outputs are frequently free-form or inconsistently structured, making downstream parsing fragile or impossible; this Skill enforces stable, predictable outputs that other systems can rely on.

Core Features & Use Cases

API-enforced schemas ensure the model's response adheres to a defined data shape.
Schema-in-prompt and post-parse validation provide layered guarantees and recovery options.
Use cases include structured data extraction, form-like responses, and generating machine-readable results for pipelines.

Quick Start

Provide an input text and a JSON schema, then request the model to return data exactly in that schema.

Frequently Asked Questions about structured-generation

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

FAQPage Schema

How do I ensure LLM outputs match a specific JSON schema?▼

Schema validation for LLM outputs works by constraining responses via API-enforced schemas when available, or using schema-in-prompt fallbacks with robust post-parse validation and retry logic to ensure predictable data shapes.

Why does my LLM data extraction return inconsistently structured text?▼

LLM data extraction returns inconsistent text because models produce free-form output. Applying structured output validation enforces stable schemas, making downstream parsing reliable for pipelines and form filling.

What is the best way to validate structured output from a language model?▼

The best way to validate structured output is layering API-enforced schemas for strict adherence, prompt-based schemas as a fallback, and post-parse validation with retry logic to recover from formatting failures.

Can I use prompt-based schemas when API-enforced typing is unavailable?▼

Yes, you can use prompt-based schemas when API-enforced typing is unavailable. This Skill supports schema-in-prompt constraints as a fallback, combined with post-parse validation to guarantee machine-readable results.

How do I handle LLM responses that fail post-parse validation?▼

To handle LLM responses that fail post-parse validation, this Skill implements robust retry logic, automatically re-prompting the model to correct formatting errors and produce outputs matching the defined schema.