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canary-watch

Use this skill to monitor and verify a deployed URL after releases — checks HTTP endpoints, SSE streams, static assets, console errors, and performance regressions after deploys, merges, or dependency upgrades. Smoke / canary / post-deploy verification.

¿Qué es canary-watch?

canary-watch is a Claude Code agent skill that use this skill to monitor and verify a deployed URL after releases — checks HTTP endpoints, SSE streams, static assets, console errors, and performance regressions after deploys, merges, or dependency upgrades. Smoke / canary / post-deploy verification.

Compatible conClaude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/canary-watch

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Documentación

Canary Watch — Post-Deploy Monitoring

When to Use

  • After deploying to production or staging
  • After merging a risky PR
  • When you want to verify a fix actually fixed it
  • Continuous monitoring during a launch window
  • After dependency upgrades

How It Works

Monitors a deployed URL for regressions. Runs in a loop until stopped or until the watch window expires.

What It Watches

1. HTTP Status — is the page returning 200?
2. Console Errors — new errors that weren't there before?
3. Network Failures — failed API calls, 5xx responses?
4. Performance — LCP/CLS/INP regression vs baseline?
5. Content — did key elements disappear? (h1, nav, footer, CTA)
6. API Health — are critical endpoints responding within SLA?
7. Static Assets — are JS, CSS, image, and font requests returning 2xx/3xx with expected content types?
8. SSE Streams — do event-stream endpoints connect and receive an initial event or heartbeat?

Watch Modes

Quick check (default): single pass, report results

/canary-watch https://myapp.com

Sustained watch: check every N minutes for M hours

/canary-watch https://myapp.com --interval 5m --duration 2h

Diff mode: compare staging vs production

/canary-watch --compare https://staging.myapp.com https://myapp.com

Alert Thresholds

critical:  # immediate alert
  - HTTP status != 200
  - Console error count > 5 (new errors only)
  - LCP > 4s
  - API endpoint returns 5xx
  - Static asset returns 4xx/5xx
  - SSE endpoint cannot connect or drops before first heartbeat

warning:   # flag in report
  - LCP increased > 500ms from baseline
  - CLS > 0.1
  - New console warnings
  - Response time > 2x baseline
  - Static asset content type changed unexpectedly
  - SSE heartbeat latency > 2x baseline

info:      # log only
  - Minor performance variance
  - New network requests (third-party scripts added?)

Notifications

When a critical threshold is crossed:

  • Desktop notification (macOS/Linux)
  • Optional: Slack/Discord webhook
  • Log to ~/.claude/canary-watch.log

Output

## Canary Report — myapp.com — 2026-03-23 03:15 PST

### Status: HEALTHY ✓

| Check | Result | Baseline | Delta |
|-------|--------|----------|-------|
| HTTP | 200 ✓ | 200 | — |
| Console errors | 0 ✓ | 0 | — |
| LCP | 1.8s ✓ | 1.6s | +200ms |
| CLS | 0.01 ✓ | 0.01 | — |
| API /health | 145ms ✓ | 120ms | +25ms |
| Static assets | 42/42 ✓ | 42/42 | — |
| SSE /events | connected ✓ | connected | +80ms heartbeat |

### No regressions detected. Deploy is clean.

Integration

Pair with:

  • /browser-qa for pre-deploy verification
  • Hooks: add as a PostToolUse hook on git push to auto-check after deploys
  • CI: run in GitHub Actions after deploy step

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

api-connector-builder

Build a new API connector or provider by matching the target repo

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