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browser-qa

Use this skill to automate visual testing and UI interaction verification using browser automation after deploying features.

browser-qa란 무엇인가요?

browser-qa is a Claude Code agent skill that use this skill to automate visual testing and UI interaction verification using browser automation after deploying features.

지원 대상Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/browser-qa

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문서

Browser QA — Automated Visual Testing & Interaction

When to Use

  • After deploying a feature to staging/preview
  • When you need to verify UI behavior across pages
  • Before shipping — confirm layouts, forms, interactions actually work
  • When reviewing PRs that touch frontend code
  • Accessibility audits and responsive testing

How It Works

Uses the browser automation MCP (claude-in-chrome, Playwright, or Puppeteer) to interact with live pages like a real user.

Safety first — blast radius (run read-only by default)

Browser QA drives real auth and real user journeys, so treat the blast radius explicitly. Default to read-only: never run a mutating journey (checkout, payment, delete, mass-update) against a production URL — require an explicit opt-in and a staging/preview URL. Use seeded test credentials, never real production logins, and redact credentials/tokens/PII before saving any screenshot.

Phase 1: Smoke Test

1. Navigate to target URL
2. Check for console errors (filter noise: analytics, third-party)
3. Verify no 4xx/5xx in network requests
4. Screenshot above-the-fold on desktop + mobile viewport
5. Check Core Web Vitals: LCP < 2.5s, CLS < 0.1, INP < 200ms
   (INP replaced FID in March 2024; thresholds per web.dev)

Phase 2: Interaction Test

1. Click every nav link — verify no dead links
2. Submit forms with valid data — verify success state
3. Submit forms with invalid data — verify error state
4. Test auth flow: login → protected page → logout (test creds only, never prod)
5. Test critical user journeys (checkout, onboarding, search)
   — read-only by default; only exercise mutating journeys against staging
     with explicit opt-in (see "Safety first" above)

Phase 3: Visual Regression

1. Screenshot key pages at 3 breakpoints (375px, 768px, 1440px)
2. Compare against committed baseline screenshots
   — no baseline ⇒ report INCONCLUSIVE, never a silent PASS
3. Flag layout shifts > 5px, missing elements, overflow
4. Check dark mode if applicable

Phase 4: Accessibility

1. Run axe-core or equivalent on each page
2. Flag WCAG 2.2 AA violations (contrast, labels, focus order)
3. Verify keyboard navigation works end-to-end
4. Check screen reader landmarks

Note: axe-core automatically covers roughly 30–40% of WCAG. A clean run is necessary, not sufficient — keyboard nav, focus order, and a screen-reader pass still need a manual check. Don't report "accessible" from an automated pass alone.

Output Format

## QA Report — [URL] — [timestamp]

### Smoke Test
- Console errors: 0 critical, 2 warnings (analytics noise)
- Network: all 200/304, no failures
- Core Web Vitals: LCP 1.2s ✓, CLS 0.02 ✓, INP 89ms ✓

### Interactions
- [✓] Nav links: 12/12 working
- [✗] Contact form: missing error state for invalid email
- [✓] Auth flow: login/logout working

### Visual
- [✗] Hero section overflows on 375px viewport
- [✓] Dark mode: all pages consistent

### Accessibility
- 2 AA violations: missing alt text on hero image, low contrast on footer links

### Verdict: SHIP WITH FIXES (2 issues, 0 blockers)
# verdict ∈ SHIP / SHIP WITH FIXES / DO NOT SHIP; use INCONCLUSIVE if no visual baseline

Integration

Works with any browser MCP:

  • mChild__claude-in-chrome__* tools (preferred — uses your actual Chrome)
  • Playwright via mcp__browserbase__*
  • Direct Puppeteer scripts

Pair with /canary-watch for post-deploy monitoring.

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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