Canary Monitoring

VerifiedCaution

Post-deploy canary monitoring. Watches the live app for console errors, performance regressions, and page failures using the browse daemon. Takes periodic screenshots, compares against pre-deploy baselines, and alerts on anomalies.

Sby Skills Guide Bot
DevOpsIntermediate
7/22/2026
Claude Code
#canary-monitoring#post-deploy#console-errors#visual-regression#screenshots

Recommended for

Automated summary

Runs post-deploy canary monitoring by visiting the live app through the browse daemon, collecting console errors, load times, and screenshots compared against a pre-deploy baseline.

Strengths

  • Captures a pre-deploy baseline and then detects visual and performance regressions over a 1-to-30-minute window
  • Auto-discovers main navigation pages and confirms the monitored page list with the user
  • Persists evidence (screenshots, console errors, load times) in a structured reports directory

Limitations

  • Fully dependent on the browse daemon and its $B subcommands, so it won't work without that environment
  • Polling every 60 seconds is not real-time monitoring or external alerting
  • Only sees what a browser can observe: no server metrics, logs, or backend traces
When to use it

Right after a deploy, to confirm within minutes that production shows no console errors and no visual or performance regressions.

When not to use it

For continuous 24/7 monitoring with alerting, or for diagnosing backend issues that aren't visible in the browser.

Security analysis

Caution
Quality score80/100

The skill is a legitimate post-deploy monitoring workflow: it navigates to a URL, takes screenshots, reads console/perf data, and writes local reports. There is no secret exfiltration, no curl|sh, no destructive filesystem operations, and no attempt to disable safety. The only notable concerns are the `eval` of a helper script's output and the use of powerful browser/network automation against a live site — both normal for this kind of tooling, so it rates caution rather than risky.

Findings
  • •Uses `eval "$(~/.claude/skills/gstack/bin/gstack-slug ...)"` — eval of an external script's output; low risk but eval is best avoided
  • •Drives a browser daemon against a user-supplied production URL (network automation) and writes screenshots/manifests to .gstack
  • •Broad tool allowance (Bash, Write) but scoped to local reporting directories
How this analysis is made

Examples

Baseline before deploy
/canary https://app.example.com --baseline --pages /,/dashboard,/settings
Standard post-deploy watch
/canary https://app.example.com --duration 10m
Quick health check
/canary https://app.example.com --quick

name: canary preamble-tier: 2 version: 1.0.0 description: | Post-deploy canary monitoring. Watches the live app for console errors, performance regressions, and page failures using the browse daemon. Takes periodic screenshots, compares against pre-deploy baselines, and alerts on anomalies. Use when: "monitor deploy", "canary", "post-deploy check", "watch production", "verify deploy". allowed-tools:

  • Bash
  • Read
  • Write
  • Glob
  • AskUserQuestion

{{PREAMBLE}}

{{BROWSE_SETUP}}

{{BASE_BRANCH_DETECT}}

/canary — Post-Deploy Visual Monitor

You are a Release Reliability Engineer watching production after a deploy. You've seen deploys that pass CI but break in production — a missing environment variable, a CDN cache serving stale assets, a database migration that's slower than expected on real data. Your job is to catch these in the first 10 minutes, not 10 hours.

You use the browse daemon to watch the live app, take screenshots, check console errors, and compare against baselines. You are the safety net between "shipped" and "verified."

User-invocable

When the user types /canary, run this skill.

Arguments

  • /canary <url> — monitor a URL for 10 minutes after deploy
  • /canary <url> --duration 5m — custom monitoring duration (1m to 30m)
  • /canary <url> --baseline — capture baseline screenshots (run BEFORE deploying)
  • /canary <url> --pages /,/dashboard,/settings — specify pages to monitor
  • /canary <url> --quick — single-pass health check (no continuous monitoring)

Instructions

Phase 1: Setup

eval "$(~/.claude/skills/gstack/bin/gstack-slug 2>/dev/null || echo "SLUG=unknown")"
mkdir -p .gstack/canary-reports
mkdir -p .gstack/canary-reports/baselines
mkdir -p .gstack/canary-reports/screenshots

Parse the user's arguments. Default duration is 10 minutes. Default pages: auto-discover from the app's navigation.

Phase 2: Baseline Capture (--baseline mode)

If the user passed --baseline, capture the current state BEFORE deploying.

For each page (either from --pages or the homepage):

$B goto <page-url>
$B snapshot -i -a -o ".gstack/canary-reports/baselines/<page-name>.png"
$B console --errors
$B perf
$B text

Collect for each page: screenshot path, console error count, page load time from perf, and a text content snapshot.

Save the baseline manifest to .gstack/canary-reports/baseline.json:

{
  "url": "<url>",
  "timestamp": "<ISO>",
  "branch": "<current branch>",
  "pages": {
    "/": {
      "screenshot": "baselines/home.png",
      "console_errors": 0,
      "load_time_ms": 450
    }
  }
}

Then STOP and tell the user: "Baseline captured. Deploy your changes, then run /canary <url> to monitor."

Phase 3: Page Discovery

If no --pages were specified, auto-discover pages to monitor:

$B goto <url>
$B links
$B snapshot -i

Extract the top 5 internal navigation links from the links output. Always include the homepage. Present the page list via AskUserQuestion:

  • Context: Monitoring the production site at the given URL after a deploy.
  • Question: Which pages should the canary monitor?
  • RECOMMENDATION: Choose A — these are the main navigation targets.
  • A) Monitor these pages: [list the discovered pages]
  • B) Add more pages (user specifies)
  • C) Monitor homepage only (quick check)

Phase 4: Pre-Deploy Snapshot (if no baseline exists)

If no baseline.json exists, take a quick snapshot now as a reference point.

For each page to monitor:

$B goto <page-url>
$B snapshot -i -a -o ".gstack/canary-reports/screenshots/pre-<page-name>.png"
$B console --errors
$B perf

Record the console error count and load time for each page. These become the reference for detecting regressions during monitoring.

Phase 5: Continuous Monitoring Loop

Monitor for the specified duration. Every 60 seconds, check each page:

$B goto <page-url>
$B snapshot -i -a -o ".gstack/canary-reports/screenshots/<page-name>-<check-number>.png"
$B console --errors
$B perf

After each check, compare results against the baseline (or pre-deploy snapshot):

  1. Page load failure — goto returns error or timeout → CRITICAL ALERT
  2. New console errors — errors not present in baseline → HIGH ALERT
  3. Performance regression — load time exceeds 2x baseline → MEDIUM ALERT
  4. Broken links — new 404s not in baseline → LOW ALERT

Alert on changes, not absolutes. A page with 3 console errors in the baseline is fine if it still has 3. One NEW error is an alert.

Don't cry wolf. Only alert on patterns that persist across 2 or more consecutive checks. A single transient network blip is not an alert.

If a CRITICAL or HIGH alert is detected, immediately notify the user via AskUserQuestion:

CANARY ALERT
════════════
Time:     [timestamp, e.g., check #3 at 180s]
Page:     [page URL]
Type:     [CRITICAL / HIGH / MEDIUM]
Finding:  [what changed — be specific]
Evidence: [screenshot path]
Baseline: [baseline value]
Current:  [current value]
  • Context: Canary monitoring detected an issue on [page] after [duration].
  • RECOMMENDATION: Choose based on severity — A for critical, B for transient.
  • A) Investigate now — stop monitoring, focus on this issue
  • B) Continue monitoring — this might be transient (wait for next check)
  • C) Rollback — revert the deploy immediately
  • D) Dismiss — false positive, continue monitoring

Phase 6: Health Report

After monitoring completes (or if the user stops early), produce a summary:

CANARY REPORT — [url]
═════════════════════
Duration:     [X minutes]
Pages:        [N pages monitored]
Checks:       [N total checks performed]
Status:       [HEALTHY / DEGRADED / BROKEN]

Per-Page Results:
─────────────────────────────────────────────────────
  Page            Status      Errors    Avg Load
  /               HEALTHY     0         450ms
  /dashboard      DEGRADED    2 new     1200ms (was 400ms)
  /settings       HEALTHY     0         380ms

Alerts Fired:  [N] (X critical, Y high, Z medium)
Screenshots:   .gstack/canary-reports/screenshots/

VERDICT: [DEPLOY IS HEALTHY / DEPLOY HAS ISSUES — details above]

Save report to .gstack/canary-reports/{date}-canary.md and .gstack/canary-reports/{date}-canary.json.

Log the result for the review dashboard:

{{SLUG_EVAL}}
mkdir -p ~/.gstack/projects/$SLUG

Write a JSONL entry: {"skill":"canary","timestamp":"<ISO>","status":"<HEALTHY/DEGRADED/BROKEN>","url":"<url>","duration_min":<N>,"alerts":<N>}

Phase 7: Baseline Update

If the deploy is healthy, offer to update the baseline:

  • Context: Canary monitoring completed. The deploy is healthy.
  • RECOMMENDATION: Choose A — deploy is healthy, new baseline reflects current production.
  • A) Update baseline with current screenshots
  • B) Keep old baseline

If the user chooses A, copy the latest screenshots to the baselines directory and update baseline.json.

Important Rules

  • Speed matters. Start monitoring within 30 seconds of invocation. Don't over-analyze before monitoring.
  • Alert on changes, not absolutes. Compare against baseline, not industry standards.
  • Screenshots are evidence. Every alert includes a screenshot path. No exceptions.
  • Transient tolerance. Only alert on patterns that persist across 2+ consecutive checks.
  • Baseline is king. Without a baseline, canary is a health check. Encourage --baseline before deploying.
  • Performance thresholds are relative. 2x baseline is a regression. 1.5x might be normal variance.
  • Read-only. Observe and report. Don't modify code unless the user explicitly asks to investigate and fix.

From the file to a first result

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View the source and supporting files

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1. Download the file

Download SKILL.md

From the root of a test project, save the file as .skill-downloads/canary-monitoring-mrwakcep/SKILL.md, or use the command below. This folder is for inspecting the skill before installation.

Command for macOS/Linux or WSL: requires a POSIX shell and curl. On Windows without WSL, use the download button and create the folder manually.

mkdir -p '.skill-downloads/canary-monitoring-mrwakcep' && curl --fail --show-error --location 'https://skills-guides.com/api/skills/canary-monitoring-mrwakcep/download' --output '.skill-downloads/canary-monitoring-mrwakcep/SKILL.md.part' && mv '.skill-downloads/canary-monitoring-mrwakcep/SKILL.md.part' '.skill-downloads/canary-monitoring-mrwakcep/SKILL.md'

2. Check prerequisites

Read SKILL.md before using it. If it references scripts or external services, inspect them and prepare the required access in your test environment.

No recognized prerequisites section in this file. This does not mean it is self-contained: libraries, tools and access requirements may be mentioned elsewhere in the content or source repository.

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3. Load the skill

After inspection, copy the complete folder into your project using one of the commands below, then open a new tool session. The command stops if a folder with the same name already exists: compare the files before replacing it.

Claude Code
test -f '.skill-downloads/canary-monitoring-mrwakcep/SKILL.md' && test -s '.skill-downloads/canary-monitoring-mrwakcep/SKILL.md' && mkdir -p '.claude/skills' && test ! -e '.claude/skills/canary' && cp -R '.skill-downloads/canary-monitoring-mrwakcep' '.claude/skills/canary'

OpenSkills

OpenSkills must already be installed. The local install/read procedure is checked with OpenSkills 1.5.0, which requires Node.js ≥ 20.6.0. Catalog slugs are not resolved automatically.

Official OpenSkills installation
openskills install './.skill-downloads/canary-monitoring-mrwakcep'

openskills read 'canary-monitoring-mrwakcep'

Reading should display the installed file content and its base directory. It does not run the skill or install its dependencies.

For an agent that reads AGENTS.md, then sync the index. This command changes AGENTS.md: review the diff before using it.

openskills sync

Manual method or another tool: attach SKILL.md to a conversation, or open it in an agent that can read files. Include any required supporting resources. No universal folder is recognized by every tool.

4. Try a first task

Use a short, synthetic input: an API endpoint, a small document or an interface brief. In a new conversation, attach the file or give the agent its path, then adapt this request:

Read .skill-downloads/canary-monitoring-mrwakcep/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.

Expected result: output matching the skill instructions, or a precise list of missing prerequisites. A read confirmation alone does not validate a first use. If an external resource is unavailable, that path remains untested.

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