firecrawl-qa 是做什么的？
使用 Firecrawl 浏览器对实时网站进行 QA 测试并抓取证据。当用户需要进行探索性 QA、表单测试、导航/链接检查、响应式检查、性能观察、Bug 报告或上线前质量审查时使用。
使用 Firecrawl 浏览器对实时网站进行 QA 测试并抓取证据。当用户需要进行探索性 QA、表单测试、导航/链接检查、响应式检查、性能观察、Bug 报告或上线前质量审查时使用。
firecrawl/firecrawl-qa 是一个用于对实时网站进行自动化 QA 测试的 AI 代理技能。它利用 Firecrawl 浏览器引擎执行探索性测试、表单交互、导航和链接验证、响应式布局检查、性能观察以及 Bug 报告生成。在测试过程中，该技能会抓取页面截图、HTML 内容、网络请求日志等证据，以便开发者或 QA 工程师快速定位问题。适用于预发布质量检查、回归测试和用户体验评估。此技能支持通过自然语言指令触发，与 Claude Code、Cursor 等主流 AI 代理平台兼容。其核心优势在于将浏览器自动化与 AI 驱动的测试逻辑结合，减少了手动测试的工作量，提高了测试覆盖率。
npx skills add https://github.com/firecrawl/firecrawl-workflows/tree/main/skills/firecrawl-qaInstalled? Explore more 研究与数据分析 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
使用 Firecrawl 浏览器对实时网站进行 QA 测试并抓取证据。当用户需要进行探索性 QA、表单测试、导航/链接检查、响应式检查、性能观察、Bug 报告或上线前质量审查时使用。
This repo contains 15 individual skills — each has its own dedicated page.
Extract structured company lists from directories with Firecrawl. Use for scraping YC, Crunchbase, Product Hunt, G2, startup directories, category directories, or custom company databases into JSON, CSV, CRM-ready lists, or research tables.
Monitor competitor pricing, features, changelogs, dashboards, and product changes with Firecrawl. Use for recurring competitive intelligence, pricing tier extraction, feature change tracking, or structured competitor alerts.
Pull metrics from analytics dashboards and internal web tools with Firecrawl browser. Use when the user needs dashboard reporting, cross-platform metric summaries, authenticated analytics extraction, date-range reports, or structured metrics from web dashboards.
Run multi-source deep research with Firecrawl. Use when the user asks to research a topic, compare perspectives, produce a sourced briefing, investigate a technical or market question, or synthesize web evidence across many sources.
Walk through a product's key flows with Firecrawl browser and produce a structured UX/product walkthrough. Use for signup, onboarding, pricing, docs, dashboard, product demo prep, UX teardown, and first-run experience analysis.
Build a knowledge base from web content with Firecrawl. Use for local reference docs, RAG-ready chunks, fine-tuning datasets, documentation mirrors, topic corpora, or LLM-ready markdown organized from web sources.
Ingest public or authenticated knowledge bases and docs portals with Firecrawl browser. Use for JS-heavy docs, login-gated portals, paginated help centers, support knowledge bases, or structured JSON/markdown extraction from documentation sites.
Generate structured lead lists from prospect databases and web directories with Firecrawl browser. Use for finding prospects by role, company type, industry, stage, location, technologies, or other criteria and exporting CRM-ready JSON or CSV.
Produce pre-meeting lead intelligence briefs with Firecrawl. Use when the user needs company research, person research, recent news, talking points, pain points, or outreach preparation before a sales call, partnership meeting, investor conversation, or customer interview.
Extract market, financial, earnings, industry, and company metrics with Firecrawl. Use when the user asks for market research, industry trends, public company data, financial comparisons, earnings research, or structured market reports.
Find and synthesize research papers, whitepapers, PDFs, technical reports, and academic sources with Firecrawl. Use when the user wants a literature review, paper summary, research landscape, or sourced synthesis from PDFs and scholarly/industry publications.
Audit a website's SEO with Firecrawl. Use when the user asks for an SEO audit, metadata and heading review, sitemap/site-structure analysis, keyword opportunities, competitor SERP comparison, or prioritized search optimization recommendations.
Research products across the web with Firecrawl and produce a shopping recommendation or cart-ready summary. Use when the user wants to compare products, find the best option, evaluate reviews, respect budget/preferences, or shop with a saved browser session.
Extract any website's design system into an agent-ready DESIGN.md using Firecrawl scrape evidence. Use when the user wants colors, fonts, spacing, components, layout patterns, or brand/UI guidance from a website so AI agents can create new websites, clone a look, or build pages inspired by that design.
Run outcome-focused Firecrawl workflows that produce deliverables such as research reports, SEO audits, QA reports, lead lists, knowledge bases, website design systems, and other structured web-data artifacts. Use when the user wants Firecrawl to complete a business, marketing, product, or creative workflow rather than merely scrape a page or integrate API calls into code.
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.