What is the primary purpose of this skill?

It ensures that when an optional service is unavailable, Claude provides a helpful message explaining what's missing and how to fix it, rather than failing silently or crashing.

How does the caching mechanism work?

The skill implements a 60-second Time-To-Live (TTL) cache for health checks, preventing the system from wasting resources by re-verifying service status on every single call.

Does this skill stop the code from running if a service is missing?

No, the goal of graceful degradation is to continue with reduced functionality whenever possible, keeping the user informed of what features are currently limited.

Can this be used for web APIs?

Yes, while the example focuses on LMStudio, the pattern is designed to work with any external service, database, or API dependency.

Graceful Service Degradation

Name: Graceful Service Degradation
Author: scooter-lacroix

byscooter-lacroix

Developer Tools

Implements resilient service availability checks and actionable fallback messaging to prevent silent failures in complex workflows.

About

The Graceful Degradation skill provides a standardized architectural pattern for handling unavailable optional services within Claude's coding environment. By checking for dependencies early and caching the results with a 60-second TTL, it prevents performance bottlenecks while ensuring that users receive clear, step-by-step instructions on how to resolve missing services. This approach replaces silent failures with helpful, non-blocking feedback that allows tasks to continue with reduced functionality rather than crashing.

Key Features

0 GitHub stars
60-second TTL health check caching for optimized performance
Early service availability detection to save compute
Resilient non-blocking execution for optional features
Actionable fallback templates for user-facing error messages
Standardized troubleshooting guides for missing local services

Use Cases

Managing connectivity to local AI tools like LMStudio
Debugging environment configurations with clear troubleshooting paths
Handling optional API dependencies that may be offline or restricted

About

Key Features

0 GitHub stars
60-second TTL health check caching for optimized performance
Early service availability detection to save compute
Resilient non-blocking execution for optional features
Actionable fallback templates for user-facing error messages
Standardized troubleshooting guides for missing local services

Use Cases

Managing connectivity to local AI tools like LMStudio
Debugging environment configurations with clear troubleshooting paths
Handling optional API dependencies that may be offline or restricted