canary

Monitor live deployments for console errors, performance regressions, and page failures.

Updated Apr 1, 2026

Search Tags:#monitoring #production #alerting #canary #baselines #post-deploy

Ready to run

npx skills add https://github.com/whd4/gstack --skill canary-whd4

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Please help me install this Agent Skill.
Skill: canary
Source: https://github.com/whd4/gstack/tree/main/canary
Command: npx skills add https://github.com/whd4/gstack --skill canary-whd4

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Canary monitors live deployments to catch console errors, performance regressions, and page failures before users notice.

Core Features & Use Cases

Watches the live app after deployment for errors, slowdowns, and broken pages using the browse daemon.
Takes periodic screenshots and compares them against pre-deploy baselines to detect anomalies.
Supports post-deploy verification workflows such as monitor deploy, canary checks, watch production, and verify deploy.

Quick Start

Run a canary check on your live deployment to validate health and detect regressions.

Frequently Asked Questions about canary

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I monitor live deployments for console errors and performance regressions?▼

Post-deploy monitoring watches the live app for console errors, performance regressions, and page failures using the browse daemon and baseline comparisons to detect anomalies.

What is a canary check in production monitoring?▼

A canary check validates deployment health by watching the live app post-deploy, taking periodic screenshots, and comparing them against pre-deploy baselines to detect anomalies.

How do I verify deploy health after a staged release or hotfix?▼

You verify deploy health after staged releases or hotfixes using post-deploy verification workflows that watch production, check for slowdowns, and alert the team of broken pages.

Can I use baseline comparisons to detect production anomalies?▼

Yes, baseline comparisons detect production anomalies by taking periodic screenshots of the live app post-deploy and comparing them against pre-deploy states to catch regressions.

What is the best way to alert the team fast after a deployment?▼

The best way to alert the team fast is using post-deploy canary monitoring that continuously watches production for console errors and page failures, triggering immediate alerting.