{"id":"83b4cc40-5694-40a5-b2c4-f3d37d47d7ab","shortId":"Fwku35","kind":"skill","title":"awt-e2e-testing","tagline":"AI-powered E2E web testing — eyes and hands for AI coding tools. Declarative YAML scenarios, Playwright execution, visual matching (OpenCV + OCR), platform auto-detection (Flutter/React/Vue), learning DB. Install: npx skills add ksgisang/awt-skill --skill awt -g","description":"# AWT — AI-Powered E2E Testing (Beta)\n\n> `npx skills add ksgisang/awt-skill --skill awt -g`\n\nAWT gives AI coding tools the ability to see and interact with web applications through a real browser. Your AI designs YAML test scenarios; AWT executes them with Playwright.\n\n## When to Use\n- You need AI-assisted end-to-end testing through a real browser with declarative YAML scenarios.\n- The test flow depends on visual matching, OCR, or platform auto-detection instead of stable DOM selectors.\n- You want an E2E toolchain that can both execute tests and explain failures for AI coding workflows.\n\n## What works now\n- YAML scenarios → Playwright with human-like interaction\n- Visual matching: OpenCV template + OCR (no CSS selectors needed)\n- Platform auto-detection: Flutter, React, Next.js, Vue, Angular, Svelte\n- Structured failure diagnosis with investigation checklists\n- Learning DB: failure→fix patterns in SQLite\n- 5 AI providers: Claude, OpenAI, Gemini, DeepSeek, Ollama\n- Skill Mode: no extra AI API key needed\n\n## Links\n- Main repo: https://github.com/ksgisang/AI-Watch-Tester\n- Skill repo: https://github.com/ksgisang/awt-skill\n- Cloud demo: https://ai-watch-tester.vercel.app\n\nBuilt with the help of AI coding tools — and designed to help AI coding tools test better.\n\nActively developed by a solo developer at AILoopLab. 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