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    ⚔️ Guardrail Fallback Linter

    ⚔️ Guardrail Fallback Linter

    by JustHandled Labs

    Find the LLM integration code that breaks when a model blocks a response or falls back to a different model. Flags calls with no try/except or refusal branch, responses used or parsed with no guard for a blocked or empty answer, and hardcoded model ids with no fallback handling. Built for the Fable 5 era, where a high-risk call is blocked and silently falls back to Opus 4.8.

    Updated Jun 2026
    Security scanned
    Cursor

    $12

    · or 60 credits

    30-day refund guarantee

    Secure checkout via Stripe

    Included in download

    • Identify LLM call sites lacking refusal or error branch handling
    • Audit hardcoded model IDs for missing graceful fallback logic
    • terminal, file_read automation included
    • Ready for Cursor
    • Instant install

    Sample input

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

    Sample output

    [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.

    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.

    Use Cases

    • Identify LLM call sites lacking refusal or error branch handling
    • Audit hardcoded model IDs for missing graceful fallback logic
    • Prevent runtime crashes caused by parsing blocked AI responses
    • Prepare legacy integrations for the Claude Fable 5 safety architecture

    Reviews

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    Security Scanned

    Passed automated security review

    Permissions

    Terminal / Shell
    Read Files

    Allowed Hosts

    www.agensi.io

    File Scopes

    guardrail-fallback-linter/**
    **/*.py
    **/*.js
    **/*.ts
    **/*.jsx
    **/*.tsx

    Read-only. Recognized call patterns and refusal signals load from an editable references/llm-call-patterns.json. It does not run your code or call a model.

    Tags

    llm-ops
    static-analysis
    reliability
    python
    typescript
    guardrails

    Works with any agent that can read a repo and run a local Python script (Claude Code, Cursor, Codex CLI, and other SKILL.md-compatible agents). Standard library only, no install step. Read-only, no network. Recognizes common client call patterns across Python and JavaScript or TypeScript.

    Creator

    JustHandled Labs

    JustHandled Labs builds focused agent skills for the work nobody wants to do by hand. Each one is a single repeatable job done well: catching the security and data mistakes that quietly ship, keeping docs and tests honest, gating the commands an agent is about to run, sharpening writing, and handling the founder chores around launches, outreach, and brand setup. Not generic AI productivity. Specific workflows that are easy to run, review, and repeat. Maintained by H.J. Westerfield, with a background in communications, editing, project coordination, customer support, and practical AI systems. Tools for people who want useful automation without theatrical complexity.

    Frequently Asked Questions

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