canary

Monitor live apps post-deployment for console errors and performance regressions.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/Prestonigo/Claude-Skills --skill canary-prestonigo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: canary
Source: https://github.com/Prestonigo/Claude-Skills/tree/main/gstack-main/canary
Command: npx skills add https://github.com/Prestonigo/Claude-Skills --skill canary-prestonigo

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Post-deploy canary monitoring to detect console errors, performance regressions, and page failures in the live app. It uses the browse daemon to take periodic screenshots, compare against pre-deploy baselines, and raise alerts on anomalies.

Core Features & Use Cases

  • Post-deploy health checks with console error detection, performance regression monitoring, and page failure alerts.
  • Baseline-driven screenshot comparisons to catch visual regressions.
  • Automated alerting and quick triage in production environments for deployments and post-deploy verification.

Quick Start

Run the canary after deploy to start monitoring, baseline comparisons, and alerting.

Frequently Asked Questions about canary

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

FAQPage Schema
How do I monitor for post-deploy console errors and performance regressions in my live app?▼

Post-deploy monitoring watches your live app after deployment to detect console errors and performance regressions. It uses the browse daemon to take periodic screenshots and compares live behavior against pre-deploy baselines to surface alerts.

What is canary monitoring and how does it detect production page failures?▼

Canary monitoring is a post-deploy verification process that detects production page failures by taking periodic screenshots of the live app and alerting on anomalies when live behavior deviates from pre-deploy baselines.

How do I set up baseline comparisons to catch visual regressions after a production deployment?▼

To catch visual regressions, the monitoring tool takes periodic screenshots of the live app after deployment and compares them against pre-deploy baselines, raising automated alerts when visual anomalies or page failures are detected.

Can I use this for ongoing health checks and not just immediate post-deploy verification?▼

Yes, this monitoring approach supports ongoing health checks in production environments alongside immediate post-deploy verification, continuously comparing live behavior against baselines to alert on console errors and performance regressions.

Do I need a browse daemon to run automated alerting and triage for production deployments?▼

Yes, the automated alerting and quick triage mechanism relies on the browse daemon to take periodic screenshots and compare live app behavior against pre-deploy baselines for detecting anomalies.