Where are the monitoring alerts sent?

Canary Watch triggers desktop notifications on macOS and Linux, logs results to a local file, and can be configured to send alerts to Slack or Discord.

What metrics does Canary Watch track?

It monitors HTTP status codes, console errors, network failures, Core Web Vitals (LCP, CLS, INP), and the presence of key DOM elements.

Can I use this for continuous monitoring?

Yes, the skill supports a sustained watch mode where you can define a specific interval and duration to monitor a URL over time.

How does Diff mode work?

Diff mode allows you to compare two different URLs, such as staging and production, to identify visual, performance, or technical regressions before merging code.

Canary Watch

Name: Canary Watch
Author: affaan-m

byaffaan-m

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172,650

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Analíticas y Monitorización

Monitors deployed URLs for performance regressions, console errors, and status failures to ensure stable production releases.

Canary Watch is a robust post-deployment monitoring tool designed to automate the verification of web applications after merges, dependency upgrades, or production launches. It provides a comprehensive health check by analyzing HTTP status codes, console logs, network failures, and Core Web Vitals like LCP and CLS. With support for sustained monitoring windows and a unique 'diff' mode for comparing staging against production, it ensures that regressions are identified and alerted immediately, reducing the risk of broken deployments and performance degradation.

Características Principales

01Configurable watch windows and polling intervals for sustained post-launch monitoring

02Performance regression monitoring for LCP, CLS, and INP metrics

03Automated alerting via desktop notifications and Slack/Discord webhooks

04Comparative 'Diff' mode to identify discrepancies between staging and production

05172,650 GitHub stars

06Real-time tracking of HTTP status, console errors, and network failures

Casos de Uso

01Monitoring critical performance metrics during high-traffic launch windows

02Automating post-deploy smoke tests to verify production environment health

03Comparing staging environments against production to catch configuration drift

Características Principales

01Configurable watch windows and polling intervals for sustained post-launch monitoring

02Performance regression monitoring for LCP, CLS, and INP metrics

03Automated alerting via desktop notifications and Slack/Discord webhooks

04Comparative 'Diff' mode to identify discrepancies between staging and production

05172,650 GitHub stars

06Real-time tracking of HTTP status, console errors, and network failures

Casos de Uso

01Monitoring critical performance metrics during high-traffic launch windows

02Automating post-deploy smoke tests to verify production environment health

03Comparing staging environments against production to catch configuration drift