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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.

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    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 use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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