Automate Web E2E Tests with AI
AWT lets AI coding tools write and run browser E2E tests using YAML scenarios and visual matching.
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Why it matters
Execute AI-designed YAML test scenarios in a real browser using Playwright. This skill enables AI coding tools to perform end-to-end testing with human-like interaction, visual matching, and OCR.
Outcomes
What it gets done
Translate YAML test scenarios into executable Playwright scripts.
Perform end-to-end testing using visual matching and OCR.
Auto-detect platforms like Flutter, React, Vue, and Angular.
Diagnose and explain test failures with investigation checklists.
Install
Add it to your toolbox
Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/ag-awt-e2e-testing | bash After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.
Reports
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Overview
AWT gives AI coding tools eyes and hands for end-to-end web testing: the AI writes YAML scenarios and AWT runs them through Playwright. It matches elements visually with OpenCV and OCR instead of relying on DOM selectors, auto-detects the frontend framework, and learns failure-to-fix patterns in a SQLite database. It supports 5 AI providers and a no-extra-key Skill Mode. Use it for AI-driven browser E2E testing, especially when the UI lacks stable selectors. Skip it if you need a mature, non-beta testing platform.
What it does
AWT gives AI coding tools the ability to see and interact with web applications through a real browser: the AI designs declarative YAML test scenarios, and AWT executes them with Playwright using human-like interaction. Instead of relying on stable DOM selectors, it matches elements visually using OpenCV templates plus OCR, and it auto-detects the target platform (Flutter, React, Next.js, Vue, Angular, or Svelte). Failures are diagnosed with structured investigation checklists, and a learning database in SQLite stores failure-to-fix patterns over time. It supports 5 AI providers (Claude, OpenAI, Gemini, DeepSeek, Ollama), and a Skill Mode that needs no extra AI API key.
When to use - and when NOT to
Use it when you need AI-assisted end-to-end browser testing driven by declarative YAML scenarios, when the test flow depends on visual matching, OCR, or platform auto-detection rather than stable DOM selectors, or when you want a toolchain that both executes tests and explains failures for an AI coding workflow. It is currently in beta and actively developed by a solo developer at AILoopLab, so it is not positioned as a mature, fully-supported testing platform.
Inputs and outputs
Install with:
npx skills add ksgisang/awt-skill --skill awt -g
Given a request like "create and run a declarative browser test for the checkout flow, including visual assertions and a clear failure report," it produces a YAML test scenario, runs it through Playwright, and returns a structured failure report with an investigation checklist when something breaks.
Integrations
Built on Playwright for browser execution, OpenCV and OCR for visual matching, and a SQLite-backed learning database for failure patterns. It plugs into 5 AI providers - Claude, OpenAI, Gemini, DeepSeek, and Ollama - and auto-detects Flutter, React, Next.js, Vue, Angular, and Svelte front ends.
Who it's for
Teams using AI coding tools that want the AI itself to write and run browser E2E tests, especially where the UI is visually driven or lacks stable selectors, and want failures explained rather than just reported as pass or fail. A cloud demo is available and the main project lives at AI-Watch-Tester on GitHub.
FAQ
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