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    Repair LLM Structured Output & Schema Failures

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    Repair LLM Structured Output & Schema Failures is a bounded ToolForge Labs workflow.

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    Repair LLM Structured Output & Schema Failures

    Repair LLM Structured Output & Schema Failures

    Example session with this skill installed

    Our service asks an LLM for JSON matching a typed schema. After a model update we started seeing missing fields, enum values outside the contract, and occasional markdown fences around the JSON. The parser retries, but some requests loop and others silently coerce bad values. Repair the output contract without weakening validation or changing product policy.

    • Read your context and instructions
    • Compiled the repair llm structured

    The likely issue is a contract mismatch around LLM structured outputs, JSON/schema contracts, parser failures, optional fields, enums, retries, validation, fallbacks, truncation, and provider/model drift. I would first define the invariant: Only schema-valid, semantically acceptable model output may cross into business logic; invalid or incomplete output must remain explicitly invalid and follow a bounded fallback path. Then I would trace the smallest owning boundary, apply or propose a bounded repair/review step, and add deterministic normal, boundary, failure and replay tests. The workflow preserves existing authorization, data-handling, model-selection, public API, and product behavior. If repository/workspace files are available, the findings can be mapped to exact paths and focused local validation; with partial evidence, the result stays an explicit diagnosis/evidence map with assumptions and no invented execution claims.

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

    About this skill

    Repair LLM Structured Output & Schema Failures is a bounded ToolForge Labs workflow. Diagnose, review, and repair LLM structured outputs, JSON/schema contracts, parser failures, optional fields, enums, retries, validation, fallbacks, truncation, and provider/model drift. Works from repositories/workspaces or partial supplied evidence. Produces bounded fixes or evidence gates with deterministic validation while preserving existing authorization, data-handling, model-selection, public API, and product behavior and avoiding invented execution claims. It works from a full repository/workspace or partial evidence such as snippets, logs, configuration, traces, documents, tickets, and architecture notes. The skill separates verified facts from assumptions, avoids invented execution claims, and produces concrete repair/review steps with focused deterministic validation or traceable evidence.

    Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse Claude Skills for LLM Ops.

    Does not perform production deployments or live database migrations. Requires supplied logs or repository access for high-precision diagnosis.

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    Permissions

    Terminal / Shell
    Read Files
    Write Files

    Use a clean branch or controlled review workspace. Supply repository/workspace files, logs, traces, configuration, documents, or evidence needed for the task. Review patches/findings before merge or operational action; keep production credentials and destructive production actions out of the workflow.

    Listed1 month ago
    Updated9 days ago

    What's inside

    Frequently Asked Questions

    This skill resolves issues where LLMs produce malformed JSON, violate schema constraints, or fail due to model drift, providing deterministic fixes to ensure your AI outputs remain reliable and parseable.

    The skill is designed for versatility, working across major frameworks like Pydantic and Zod, and is compatible with any AI agent or IDE that supports ToolForge Labs workflows.

    No, you can provide full repository access or partial evidence like logs, snippets, and error traces, making it ideal for debugging production issues where a full environment isn't available.

    Upon purchase, you receive the full ToolForge Labs workflow, including automated diagnostic routines, repair templates, and deterministic validation gates for structured data.

    The skill is designed to improve schema adherence while strictly preserving your existing authorization logic, data-handling protocols, and specific model selections without introducing side effects.

    Updates are delivered through the marketplace whenever new common failure modes are identified or when major updates to schema validation libraries are released.

    New in LLM Ops