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    ⚔️ Guardrail Fallback Linter

    2

    This skill acts as a specialized static analysis tool for LLM integrations, specifically designed for the "Fable 5" era of AI.

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    ⚔️ Guardrail Fallback Linter

    ⚔️ Guardrail Fallback Linter

    Example session with this skill installed

    Scan my codebase for LLM calls that might break if a guardrail blocks the response or falls back to an older model.

    • Read your context and instructions
    • Compiled the guardrail fallback linter

    [RISK] file: src/api/generate.ts:42
    Rule: unhandled-refusal
    Message: Anthropic client call lacks a 'refusal' or 'error' branch.
    Evidence: const result = await anthropic.messages.create({...});
    [!] Warning: Downstream JSON.parse(result.content) will fail on fallback refusal.

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

    What you get

    Identify LLM call sites lacking refusal or error branch handlingAudit hardcoded model IDs for missing graceful fallback logicPrevent runtime crashes caused by parsing blocked AI responsesPrepare legacy integrations for the Claude Fable 5 safety architecture

    About this skill

    What it does

    This skill acts as a specialized static analysis tool for LLM integrations, specifically designed for the "Fable 5" era of AI. It scans your Python or TypeScript source code to identify call sites that assume a model will always return a successful, valid response. It flags instances where code lacks 'refusal' handling or fails to account for silent fallbacks to older model versions (like Opus 4.8) when a high-risk request is blocked.

    Why use this skill

    Standard linters don't understand LLM lifecycle risks. As safety guardrails become more prevalent, your code is increasingly likely to receive a "refusal" or a response from a less-capable fallback model. If your code parses these responses blindly, it will crash or produce degraded results. This skill identifies these "blind spots" so you can implement graceful handling before they hit production.

    Supported tools

    • Languages: Python, JavaScript, TypeScript
    • Frameworks: Common LLM client patterns (OpenAI, Anthropic, LangChain)
    • Workflow: CLI-based scanning with remediation snippets provided

    The output provides a detailed report including rule IDs, severity levels, and specific lines of evidence, making it easy to integrate into your CI/CD audit process.

    How to install

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

    ~30 seconds
    1. 1

      Download the ZIP

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    2. 2

      Unzip into your skills folder

      Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.

    3. 3

      Ask your agent to use it

      Restart the agent if it was already running. It picks the skill up automatically - no config needed.

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    Click the path to copy it. Create the folder if it does not exist yet.

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