autoheal_skill

Overview

This skill adds AI-powered error monitoring and auto-fix generation to any project so you can detect runtime exceptions and get actionable, platform-specific fix prompts. It captures browser and server errors, routes them to an analysis service, and returns a dashboard link or fix identifier you can use with an AI coding tool. Setup is minimal: drop a small snippet in your entry point or server process and supply an API key.

How this skill works

The skill captures uncaught exceptions and unhandled promise rejections in browser or Node.js environments and posts error payloads (message, stack, source, user agent) to an analysis endpoint. The backend runs AI analysis to classify the error, determine if a fix is possible, and generate a platform-aware fix prompt and dashboard link. You poll a status endpoint with the returned error_id to learn whether analysis is new, analyzing, analyzed, ignored, or has an available fix.

When to use it

• Add lightweight runtime monitoring to client-side web apps (React, Vue, Next.js, Svelte).
• Monitor server-side Node.js processes for crashes and unhandled rejections.
• Generate AI-assisted fix prompts for common JavaScript/TypeScript runtime errors.
• Triage production errors quickly without reproducing them locally.
• Integrate into CI or alerting pipelines for faster remediation.

Best practices

• Store the API key securely in environment variables; do not hard-code in client builds.
• Send minimal, relevant context: message, stack, source URL, and user agent when available.
• Throttle or debounce duplicate errors to avoid ingestion overload.
• Sanitize sensitive user data before sending stacks or payloads to the analysis endpoint.
• Use the dashboard_url or fix_id to review suggested fixes before applying changes automatically.

Example use cases

• Capture front-end TypeErrors in a React app and receive an AI-written fix prompt to paste into your editor.
• Report unhandledRejection in a Node.js worker, inspect analysis status, and retrieve suggested patch steps.
• Manually submit a stack trace from a production log to get a quick analysis and remediation guidance.
• Integrate error status polling into an ops dashboard to prioritize issues with ready fixes.
• Use generated fix prompts with an AI coding assistant to produce PR-ready changes faster.