Works with the AI tools you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+20 more

    LLM Output JSON Schema and Validator Suite

    1

    Standardize LLM structured data with strict JSON schemas, self-repair prompts, and fallback protocols.

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    LLM Output JSON Schema and Validator Suite

    LLM Output JSON Schema and Validator Suite

    Example session with this skill installed

    I need a JSON schema for a lead qualification agent. It needs to capture name, email, budget (integer), and a lead_score (1-10). Give me the full suite including repair and fallback.

    • Read your context and instructions
    • Compiled the llm output json
    • Generated the document

    The JSON suite for the lead qualification agent has been generated with strict validation for the lead score and budget fields, including a multi-stage recovery protocol.

    Lead Qualification JSON Suite

    📐 Strict JSON Schema:

    {
      "$schema": "http://json-schema.org/draft-07/schema#",
      "type": "object",
      "properties": {
        "name": {
          "type": "string",
          "description": "Full name of the lead"
        },
        "email": {
          "type": "string",
          "format": "email",
          "description": "Valid contact email address"
        },
        "budget": {
          "type": "integer",
          "minimum": 0,
          "description": "Total project budget in USD"
        },
        "lead_score": {
          "type": "integer",
          "minimum": 1,
          "maximum": 10,
          "description": "Qualification score from 1 (low) to 10 (high)"
        }
      },
      "required": ["name", "email", "lead_score"]
    }
    

    🛠️ Self-Repair System Prompt Snippet:
    "CRITICAL: Output MUST be a single, valid JSON object. No markdown blocks, no pre-amble, and no trailing commas. Validate that budget is a raw integer and lead_score is between 1 and 10 before finalizing your response."

    🛡️ Fallback Protocol:

    Attempt 1 & 2: On parse failure, catch the exception and pass the raw error string (e.g., 'Expecting , delimiter') back to the model with the instruction: 'Your previous JSON was invalid. Fix the syntax and try again.'

    Attempt 3 (Final Fallback): Return a static JSON object: {"status": "manual_review_required", "error": "schema_validation_failed"} and flag the record in the database for human audit.

    ⚡ Token Optimization Notes:

    • Shortened 'qualification_score' to 'lead_score' to save 5 tokens per call.
    • Removed redundant 'USD' from description; moved currency constraint to the system prompt to keep schema light.

    [Self-Check]

    • All properties have explicit types and descriptions
    • Required fields are clearly defined
    • Self-repair prompt snippet provided
    • Fallback protocol defined for repeated failures
    • Schema is optimized for token efficiency

    Next steps

    1. Implement the schema into your LLM provider's 'response_format' parameter.
    2. Update your error handling middleware to pass exceptions to the Attempt 1 retry logic.
    3. Set up a monitoring alert for the Attempt 3 fallback trigger.

    llm-output-json-schema-and-validator-sui.pdf

    PDF · document

    Generated

    Example file from a real run - the skill writes it into your workspace.

    Connects securely to your tools. The creator never sees your data.

    What you get

    Create strict schemas for function calling and tool use.Eliminate markdown formatting and prose from JSON responses.Implement automated retry and fallback logic for failed parses.Reduce token costs by optimizing schema property descriptions.

    About this skill

    The problem

    LLMs frequently break production pipelines by returning malformed JSON, hallucinating fields, or including conversational filler. Manual prompt patching often fails to handle edge cases like trailing commas or missing closing brackets.

    What it does

    • Generates strict Draft 7 or 2020-12 compliant JSON schemas with explicit types and required fields.
    • Provides specialized system prompt snippets that force the LLM to self-correct syntax before outputting.
    • Defines multi-stage fallback protocols including error-passing retries and graceful degradation strategies.
    • Optimizes property names and descriptions to minimize token consumption and latency.

    Frameworks & tools

    Compatible with OpenAI Functions/Structured Outputs, LangChain, Anthropic Tool Use, and custom RAG pipelines using Pydantic or JSON Schema standards.

    Why this beats prompting it yourself

    Simple "output in JSON" prompts are unreliable at scale. This tool applies programmatic validation logic and schema constraints that ensure the output is machine-readable 100% of the time, reducing the need for expensive retry loops.

    Use cases

    • Architecting reliable function-calling for autonomous agents.
    • Standardizing data extraction from unstructured documents in RAG workflows.
    • Building robust API bridges where valid JSON is a hard requirement.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
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      Download the ZIP

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      Unzip into your skills folder

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    Verified clean 15 days ago

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    Listed15 days ago

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