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🛡️ Model Resilience Linter
The skill scans your codebase—supporting Python, JavaScript, and TypeScript—to detect patterns that signal poor reliability.
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See it in action
You say
Check my backend directory to see if we have any single provider risks or missing failovers.
Your agent does
Model Resilience Audit Results
MRL001: Single-provider lock-in
services/gpt_client.ts:L42: Direct dependency on OpenAI without fallback provider detected.
MRL003: No timeout configured
utils/llm_query.py:L12: API call lacks an explicit timeout setting.
What you get
About this skill
Audit Your LLM Infrastructure for Single-Point-of-Failure Risk
In the rapidly shifting landscape of AI providers, relying on a single model or endpoint is a significant architectural risk. The Model Resilience Linter is a specialized static analysis tool designed for developers to identify brittle LLM integrations before a provider outage or API deprecation brings down your application.
What it does
The skill scans your codebase—supporting Python, JavaScript, and TypeScript—to detect patterns that signal poor reliability. It specifically looks for:
- Single-Provider Lock-in: Code that lacks a secondary fallback or failover logic.
- Configuration Gaps: Missing timeouts, hardcoded base URLs, and lack of exponential backoff.
- Graceful Degradation: Missing default states or "degraded-mode" handlers for when AI services are entirely unavailable.
Why use this skill?
Manually auditing a large repository for reliability anti-patterns is time-consuming and prone to human error. This skill automates the detection of infrastructure debt using a specialized rule set (MRL001-MRL006). Unlike general linters, it understands the specific failure modes of LLM calls, providing high-signal feedback and remediation references to help you build a provider-agnostic, resilient AI stack.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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Creator
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.
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