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structured-output-validator

Validate LLM structured output with schema and semantic checks before downstream use.

2|Updated Jul 6, 2026
Search Tags:#schema validation#structured output#llm output#json contract#semantic checks#tool arguments
One-click install
npx skills add https://github.com/nguyenpv1980-wq/Project-Aegis --skill structured-output-validator
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: structured-output-validator
Source: https://github.com/nguyenpv1980-wq/Project-Aegis/tree/main/.claude/skills/structured-output-validator
Command: npx skills add https://github.com/nguyenpv1980-wq/Project-Aegis --skill structured-output-validator

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill prevents applications from trusting malformed or unsafe LLM responses by turning model output into an explicit, validated contract before any downstream code uses it.

Core Features & Use Cases

  • Schema Enforcement: Define required fields, types, enums, ranges, formats, and array bounds for structured model output.
  • Validate Before Use: Parse and validate every response before reading fields, dispatching tools, or updating records.
  • Semantic Checks: Verify allowed values, tenant-scoped IDs, referential sanity, and cross-field consistency beyond basic shape.
  • Failure Handling: Choose fail-closed rejection, bounded repair-retry, or safe fallback when validation fails.
  • Safe Handoffs: Route validated tool arguments to authorization controls and validated sink-bound content to output-safety review.
  • Use Case: A product team uses this Skill to make a JSON response from an LLM safe enough for order updates, tool calls, and regulated workflows.

Quick Start

Ask for an enforceable structured output contract with validation rules, semantic checks, failure handling, and the correct safety handoffs for your LLM feature.

Frequently Asked Questions about structured-output-validator

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

FAQPage Schema
How do I validate LLM structured output before passing it to downstream code?▼

Schema enforcement for LLM structured output requires defining required fields, types, enums, ranges, and array bounds for your JSON contract. You then apply semantic checks to verify allowed values, tenant-scoped IDs, and cross-field consistency.

What is the best way to handle semantic checks for JSON contracts generated by AI models?▼

When structured output validation fails, you handle it by choosing between fail-closed rejection, bounded repair-retry, or a safe fallback to ensure the application does not process malformed or unsafe model responses.

How do I ensure safe handoffs for LLM tool arguments before authorization?▼

You ensure safe handoffs for LLM tool arguments by routing the validated arguments to explicit authorization controls, ensuring the model output is checked and parsed safely before dispatching any tools or updating records.

Does this structured output validation approach work for regulated workflows and order updates?▼

Yes, this validation approach works for regulated workflows and order updates by making LLM JSON responses safe enough through schema enforcement, semantic checks, and safe sink-bound content routing for output-safety review.

What are the limitations of relying only on basic schema validation for model output?▼

Relying only on basic schema validation limits your ability to catch cross-field inconsistencies, unauthorized tenant-scoped IDs, and semantic errors, which requires adding explicit semantic verification and bounded failure handling to ensure sink safety.

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