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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

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sickn33-agentic-awesome-skills
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Install
$ npx -y skills add sickn33/antigravity-awesome-skills --skill awt-e2e-testing --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/awt-e2e-testing

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

SKILL.md

awt-e2e-testing.SKILL.md
name: awt-e2e-testing
description: "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"
risk: critical
source: "https://github.com/ksgisang/awt-skill"
date_added: "2026-09-04"

AWT — AI-Powered E2E Testing (Beta)

> `npx skills add ksgisang/awt-skill --skill awt -g`

AWT 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.

When to Use

  • You need AI-assisted end-to-end testing through a real browser with declarative YAML scenarios.
  • The test flow depends on visual matching, OCR, or platform auto-detection instead of stable DOM selectors.
  • You want an E2E toolchain that can both execute tests and explain failures for AI coding workflows.

What works now

  • YAML scenarios → Playwright with human-like interaction
  • Visual matching: OpenCV template + OCR (no CSS selectors needed)
  • Platform auto-detection: Flutter, React, Next.js, Vue, Angular, Svelte
  • Structured failure diagnosis with investigation checklists
  • Learning DB: failure→fix patterns in SQLite
  • 5 AI providers: Claude, OpenAI, Gemini, DeepSeek, Ollama
  • Skill Mode: no extra AI API key needed

Links

  • Main repo: https://github.com/ksgisang/AI-Watch-Tester
  • Skill repo: https://github.com/ksgisang/awt-skill
  • Cloud demo: https://ai-watch-tester.vercel.app

Built with the help of AI coding tools — and designed to help AI coding tools test better.

Actively developed by a solo developer at AILoopLab. Feedback welcome!

Example

**User request:**

> Create and run a declarative browser test for the checkout flow, including visual assertions and a clear failure report.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
Read more
Ships withsickn33-agentic-awesome-skills

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Python
Language
MIT
License
7d ago
Last commit
8mo ago
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Repo: sickn33/antigravity-awesome-skills