canary

Monitors deployed applications for console errors, performance regressions, and page failures.

1|Updated Jul 23, 2026
One-click install
npx skills add https://github.com/sanjanb/my-agent-harness --skill canary-sanjanb
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: canary
Source: https://github.com/sanjanb/my-agent-harness/tree/main/skills/canary
Command: npx skills add https://github.com/sanjanb/my-agent-harness --skill canary-sanjanb

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? After deploying code, teams often lack immediate visibility into whether the live application is actually healthy. This Skill watches the production app for console errors, performance regressions, and page failures so problems are caught before users report them. ## Core Features & Use Cases - Post-Deploy Monitoring: Watches the live app through a browse daemon, capturing console errors and page failures as they occur. - Baseline Comparison: Takes periodic screenshots and compares them against pre-deploy baselines to detect visual or behavioral anomalies. - Anomaly Alerting: Flags regressions and unexpected behavior immediately after a deploy completes. - Use Case: After shipping a new release, invoke this Skill to watch production for a defined window, confirming the deploy introduced no console errors, broken pages, or performance regressions. ## Quick Start Ask the agent to monitor the deployment and watch production for errors after the latest release.

Frequently Asked Questions about canary

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

FAQPage Schema
How do I monitor my app after a deployment?

Invoke the canary skill after deploying to watch the live application through a browse daemon. It captures console errors, page failures, and performance regressions, then alerts you when anomalies appear compared to pre-deploy baselines.

What is a canary check in post-deploy verification?

A canary check observes the production application for a period after release to confirm it behaves correctly. This skill automates that by taking periodic screenshots, comparing them against baselines, and flagging errors or regressions.

Can I use this with headless or CI sessions?

Yes, the skill detects the session kind (interactive, spawned, or headless) via its preamble and adapts behavior accordingly. In headless runs it blocks on decisions requiring user input rather than prompting.

What triggers the canary monitoring skill?

Phrases like "monitor after deploy", "canary check", "watch for errors post-deploy", "monitor deploy", or "verify deploy" activate it. It is designed specifically for the window immediately following a production release.

Why does the skill run a long preamble before monitoring?

The preamble initializes session state, telemetry preferences, branch detection, and configuration values used across the gstack skill ecosystem. It runs once at skill start so the monitoring workflow has correct environment context.