본문으로 건너뛰기
cccskills
무료GitHub에 공개

browser-qa

Test deployed web pages through browser automation. Check forms, login flows, loading and responsiveness metrics, visual changes at three screen widths, and accessibility issues, then receive a report with fixes and a release verdict.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Run automated post-deploy UI verification with a browser automation MCP (claude-in-chrome, Playwright, or Puppeteer): console-error and Core Web Vitals smoke checks, form and auth-flow interaction tests, screenshot visual regression across three breakpoints, and axe-core accessibility audits ending in a SHIP / DO-NOT-SHIP verdict. Use when testing a deployed feature on staging or preview, before shipping frontend changes, reviewing a frontend PR, or checking responsive layout and accessibility.

연동 도구Playwright
설치 방법 보기

이럴 때 유용합니다

  • Testing a feature deployed to staging or preview
  • Checking forms and login flows before release
  • Comparing layouts at three screen widths
  • Reviewing accessibility before a frontend release

포함된 파일(1)

  • SKILL.md4.0 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Design and audit interfaces for Web, iOS, and Android using WCAG 2.2 accessibility guidance. Check screen-reader labels, keyboard navigation, contrast, and control sizes to identify barriers and guide implementation for people using assistive technology.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Adding descriptive labels to icon-only buttons
  • Reviewing keyboard navigation and modal focus
  • Checking contrast and control sizes
affaan-m/ECC28.3만2026년 10월 16일 업데이트

Investigate unreliable AI agents across 12 layers, including instructions, memory, tool execution, and display. Use code and logs to trace failures, rank findings by severity, and produce an ordered repair plan that prioritizes code changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Investigating failures that appear only inside an agent application
  • Tracing old conversation content leaking into new sessions
  • Checking skipped or falsely claimed tool execution
affaan-m/ECC28.3만2026년 10월 16일 업데이트

Compare coding agents on tasks from your own codebase. Define success checks, run agents in separate Git working directories, and report pass rates, available API costs, completion time, and consistency across repeated trials.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Comparing coding agents on real development tasks
  • Evaluating a new tool or model before adoption
  • Checking performance after agent updates
affaan-m/ECC28.3만2026년 10월 16일 업데이트

Design and refine an AI agent’s available actions, tool inputs, and result formats. Define recovery steps for errors, manage the information kept in context, and evaluate changes through completion rates, retries, and cost per successful task.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Define an AI agent’s tool set and input formats
  • Make tool results and next steps easier to interpret
  • Specify safe retries and stopping conditions
affaan-m/ECC28.3만2026년 10월 16일 업데이트

Diagnose failed AI agent runs by capturing errors, checking likely causes, and choosing small, reversible recovery actions. The workflow produces a structured report of the failure, recovery steps, and evidence, helping explain repeated retries or stalled progress.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Diagnosing repeated tool calls with no progress
  • Investigating quality loss as context grows
  • Checking file paths and environment mismatches
affaan-m/ECC28.3만2026년 10월 16일 업데이트

Diagnose AI agent runs that fail repeatedly, loop through tools, or lose track of the task. Capture errors and environment details, test a focused recovery action, and produce a report explaining the evidence and outcome.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Diagnosing repeated failures in an agent run
  • Investigating loops through the same tool calls
  • Reviewing output degradation as context grows
affaan-m/ECC28.3만2026년 10월 5일 업데이트

affaan-m의 스킬 모두 보기

이 스킬의 문제 신고