Run post-deploy monitoring checks on production web apps and services.
npx skills add https://github.com/putrawitama/DenchClaw --skill canary-putrawitama
Please help me install this Agent Skill. Skill: canary Source: https://github.com/putrawitama/DenchClaw/tree/main/skills/gstack/canary Command: npx skills add https://github.com/putrawitama/DenchClaw --skill canary-putrawitama
Extended monitoring after deployment to catch issues that only manifest under real user traffic.
Run the canary skill after deployment to start an automated post-deploy monitoring loop.
High-intent search queries and answers about installing and using this skill.
Post-deploy monitoring runs automated checks on real-user traffic to verify production health. It operates at a 2-5 minute cadence for up to 30 minutes to detect new console errors, network failures, and performance regressions.
Automated post-deploy anomaly detection identifies issues in production deployments under real-user traffic. It flags new console errors, network failures, performance regressions, and visual layout changes by running regular checks across multiple flows.
To detect performance regressions after releasing, run post-deploy monitoring checks on your web app. This process collects metrics during real-user traffic at regular intervals and flags any detected performance drops or anomalies.
Yes, post-deploy monitoring works for catching new console errors in web apps. It performs regular checks during real-user traffic and explicitly reports new console errors alongside network failures and visual layout changes.
Automated post-deploy checks are limited to a maximum duration of 30 minutes with a strict cadence of 2-5 minutes. They monitor real-user traffic on web apps and services but do not perform continuous long-term monitoring beyond this window.
samjhechtCompare UI component screenshots against baselines to detect visual regressions.
cegit27Monitor live production apps for console errors and performance regressions.
BradliebsMonitor live web apps for console errors, performance regressions, and visual anomalies.
SensLiaoAutomate smoke tests for QA environments with synthetic accounts and health checks.
