Monitor live web apps for console errors, performance regressions, and page failures after deployment.
npx skills add https://github.com/pistosmin/base-one --skill canary-pistosmin
Please help me install this Agent Skill. Skill: canary Source: https://github.com/pistosmin/base-one/tree/main/.claude/skills/gstack/canary Command: npx skills add https://github.com/pistosmin/base-one --skill canary-pistosmin
Canary monitoring helps catch production surprises early by watching the live app for console errors, performance regressions, and page failures after a deployment.
Invoke the canary command with the production URL to start 10 minutes of monitoring after deployment.
High-intent search queries and answers about installing and using this skill.
Post-deploy monitoring catches production issues by continuously watching live web apps for console errors, performance regressions, and page failures. It uses the browse daemon to observe behavior, capture screenshots, compare against baselines, and alert on anomalies.
Canary monitoring is the process of observing a live app for errors and performance drops immediately after a release. You need it when deploying new versions to critical user journeys and want to detect production surprises early before they impact users.
Invoke the canary command with your production URL to start continuous monitoring. The default quick check runs for 10 minutes after deployment, observing critical paths, capturing screenshots, comparing against baselines, and alerting on anomalies.
Yes, post-deploy monitoring supports configurable duration, specific pages, and quick check options. You can target critical user journeys across your live web app and adjust the time window to match your deployment risk profile.
Yes, the browse daemon is required for live monitoring. It observes production behavior, captures periodic screenshots, enables baseline comparisons, and triggers alerting logic when console errors, performance regressions, or page failures are detected.
Continuous post-deploy monitoring compares live app behavior against established baselines to detect page failures and performance regressions. By capturing screenshots and alerting on anomalies across full user journeys, it provides actionable alerts if something regresses.
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