canary

Overview of canary skill

The canary skill is for post-deploy monitoring when you need to verify that a live app still behaves correctly after shipping. It watches production for console errors, page failures, and performance regressions, then compares current behavior against a pre-deploy baseline. If you want a canary skill that checks real user-facing risk instead of relying on a static prompt, this is built for monitoring after release.

What canary is for

Use canary when the job is to monitor a deploy, catch broken pages, or confirm that a release did not introduce visible regressions. It is especially relevant for teams that want canary for Monitoring across console, screenshots, and page-level failures.

Why it differs from a generic prompt

A generic “check the site” prompt usually stops at surface-level review. canary is designed around a monitoring workflow: run after deploy, observe live behavior over time, compare against a baseline, and flag anomalies. That makes it more useful when the question is “is production healthy right now?” rather than “does this page look okay once?”

Best fit and limits

This skill fits CI-adjacent or operator-style workflows where post-deploy confidence matters. It is less useful if you only need a one-off content review, a design critique, or a manual QA checklist without ongoing monitoring. The main adoption blocker is usually context: canary works best when you can point it at the right live target and define what “normal” looks like.

How to Use canary skill

canary install and setup

Install canary with the gstack skills flow shown in the repo, then start by reading SKILL.md and SKILL.md.tmpl. The skill does not ship with extra support folders, so the core install context lives in those two files. If you are adapting the canary guide to your own repo, keep the production URL, deploy event, and baseline source explicit in your prompt.

What to provide in your first prompt

Give canary the smallest set of facts that makes monitoring meaningful:

• the app or route to watch
• what changed in the deploy
• what “good” looked like before release
• what counts as a failure
• how long to observe
  A weak prompt says “monitor the app.” A stronger prompt says “watch /checkout after today’s deploy, compare screenshots to the pre-release baseline, and flag any new console errors, broken buttons, or layout shifts over 10 minutes.”

Suggested workflow for canary usage

Start with the deploy moment, then move from baseline to observation to verdict. First confirm the target branch or environment, then define the baseline behavior, then ask for live checks and anomaly reporting. If you are using the skill interactively, the most important early decision is whether you want proactive monitoring or a single verification pass, because that changes how the skill should frame its checks.

Files to read first

Read SKILL.md first, then SKILL.md.tmpl to understand how the skill is generated and which parts are intended as workflow logic. Pay special attention to the sections on preamble, plan mode safety, skill invocation during plan mode, and routing. Those are the parts most likely to affect whether canary triggers correctly and runs at the right time.

canary skill FAQ

Is canary only for production monitoring?

No. It is built for post-deploy canary checks, so production is the most obvious use case, but the same pattern also works for staging or any live environment where you want a baseline comparison after change.

How is canary different from ordinary QA prompts?

Ordinary prompts often ask for a single inspection. canary is more operational: it is meant to watch for regressions, capture evidence, and compare current state against prior state. That makes it better when you need canary for Monitoring rather than a general review.

Is canary beginner-friendly?

Yes, if you can describe the deploy, the page, and the failure conditions. The hard part is not using the skill; it is giving it enough context to judge change against a meaningful baseline. If you cannot define what changed or what should stay stable, the output will be weaker.

When should I not use canary?

Do not use it for broad product analysis, content editing, or tasks that do not depend on live app health. It is also a poor fit when you have no baseline, no access to the target environment, or no clear pass/fail threshold for the deploy.

How to Improve canary skill

Give canary a sharper baseline

The most useful upgrade is a better definition of normal. Include pre-deploy screenshots, known-good URLs, expected console behavior, and any critical UI elements that must remain intact. The more precise the baseline, the less likely the skill is to over-report harmless differences.

State the failure modes you care about

canary becomes much more valuable when you name likely regressions up front: blank screens, missing API data, broken navigation, CSS shifts, console errors, slow page load, or interaction failures. A canary skill that knows what to look for will produce more decision-ready output than one asked to “find issues” generically.

Iterate after the first run

Use the first pass to learn what the skill surfaces, then tighten the prompt. If it reports noise, narrow the routes or increase the anomaly threshold. If it misses important issues, add the key user flows, expected text, or comparison points. Good canary guide usage is iterative: baseline, check, refine, rerun.