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生活与工具 #browser #automation #testing

browser-qa

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

DeepseekModel 官方收录技能 质量 优秀 · 90 v1.0.0

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https://deepseekmodel.com/api/download.php?id=affaan-m-ecc-skills-browser-qa-skill-md&format=skill
下载 .skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
name browser-qa description Use this skill to automate visual testing and UI interaction verification using browser automation after deploying features. metadata {"origin":"ECC"} 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.
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下载的 .skill 包内含以下字段。
字段 说明
format格式标识(skill/v1)
skill_id技能唯一 ID
name技能名称
version版本号
description技能描述
category所属分类(数组)
trigger_words触发词列表
tags标签列表
source来源标识
source_url来源链接(本页地址)
exported_at导出时间(每次下载生成)
system_prompt系统提示词正文
model_config模型参数:provider / model / temperature / max_tokens / top_p
examples示例
install_guide各平台导入说明(Coze / Dify / Claude / 自定义框架)
同一份技能可按不同平台格式导出。
.skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用 下载
.skillpro 增强格式,额外含脚本 / 工具 / 依赖 / 钩子占位 下载
.json 纯 JSON 导出,只含 system_prompt 与模型参数 下载
Coze 带 frontmatter 的 Markdown,Coze 平台导入用 下载
Dify Dify DSL,创建应用后直接导入 下载

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