canary

Monitor deployed pages for console errors, performance regressions, and page failures.

Updated Mar 29, 2026

Search Tags:#monitoring #production #observability #alerts #canary #baselines

Ready to run

npx skills add https://github.com/zzxtbeta/design-handbook --skill canary-zzxtbeta

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

After a deployment, this skill watches the live app to catch console errors, performance regressions, and page failures early.

Core Features & Use Cases

Baseline-driven monitoring: captures baseline screenshots, checks errors, and tracks performance across pages.
Anomaly alerts: notifies on new errors or degraded performance to stop bad releases quickly.
End-to-end canary workflow: from setup to continuous monitoring with optional baseline updates.

Quick Start

Run a canary check on your URL with a default 10-minute window, then review the health summary.

Frequently Asked Questions about canary

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

FAQPage Schema

How do I monitor production for performance regressions after a deployment?▼

Post-deploy canary monitoring captures periodic screenshots of configured pages, compares them against stored baselines, and raises alerts when it detects performance regressions or page failures.

What is baseline-driven canary monitoring and how does it work?▼

Baseline-driven canary monitoring uses a browse daemon to capture initial page screenshots, error states, and performance metrics, then continuously compares live production pages against these stored baselines to detect anomalies.

How do I set up an automated canary check to catch console errors in live web applications?▼

You can set up an automated canary check by configuring your target URLs and alert tuning parameters, then running the monitoring daemon to continuously watch for new console errors during a specified duration window.

Can I tune alert thresholds and monitoring duration for specific production pages?▼

Yes, you can tune alert sensitivity, specify monitoring duration, and target specific production pages to ensure anomaly alerts match the exact criteria needed to stop bad releases quickly.

Does canary monitoring work without external dependencies or third-party observability platforms?▼

Yes, this canary monitoring operates entirely locally by using its own browse daemon to capture baselines and storing all monitoring results locally without requiring external dependencies or third-party observability platforms.

What is the best way to detect page failures and visual anomalies after a release without manual review?▼

Automated baseline comparison is the best way to detect page failures, as it periodically captures screenshots of live pages and raises alerts automatically when visual anomalies or new errors appear, eliminating manual review.