moai-playwright-webapp-testing
About
This Claude Skill orchestrates AI-powered enterprise web application testing using Playwright. It enables intelligent test generation, visual regression testing, and cross-browser coordination with Context7 integration. Use it to automate QA workflows for modern web applications through Claude Code.
Quick Install
Claude Code
Recommended/plugin add https://github.com/modu-ai/moai-adkgit clone https://github.com/modu-ai/moai-adk.git ~/.claude/skills/moai-playwright-webapp-testingCopy and paste this command in Claude Code to install this skill
Documentation
AI-Powered Enterprise Web Application Testing Skill v4.0.0
Skill Metadata
| Field | Value |
|---|---|
| Skill Name | moai-playwright-webapp-testing |
| Version | 4.0.0 Enterprise (2025-11-11) |
| Tier | Essential AI-Powered Testing |
| AI Integration | โ Context7 MCP, AI Test Generation, Visual Regression |
| Auto-load | On demand for intelligent test triage and automated QA |
| Languages | Python, TypeScript, JavaScript + Web Frameworks |
๐ Revolutionary AI Testing Capabilities
AI-Powered Test Generation with Context7
- ๐ง Intelligent Test Pattern Recognition with ML-based classification
- ๐ฏ AI-Enhanced Test Generation using Context7 latest documentation
- ๐ Visual Regression Testing with AI-powered diff analysis
- โก Real-Time Cross-Browser Coordination across Chrome, Firefox, Safari
- ๐ค Automated QA Workflows with Context7 best practices
- ๐ Performance Test Integration with AI profiling
- ๐ฎ Predictive Test Maintenance using ML pattern analysis
Context7 Integration Features
- Live Documentation Fetching: Get latest Playwright patterns from
/microsoft/playwright - AI Pattern Matching: Match test scenarios against Context7 knowledge base
- Best Practice Integration: Apply latest testing techniques from official docs
- Version-Aware Testing: Context7 provides version-specific patterns
- Community Knowledge Integration: Leverage collective testing wisdom
๐ฏ When to Use
AI Automatic Triggers:
- Web application deployment verification
- UI/UX regression detection requirements
- Cross-browser compatibility testing
- Performance degradation detection
- Complex user workflow automation
- API integration testing scenarios
Manual AI Invocation:
- "Generate comprehensive tests for this webapp"
- "Create visual regression tests with AI"
- "Automate cross-browser testing workflows"
- "Generate performance tests with Context7"
- "Create intelligent QA test suites"
๐ง AI-Enhanced Testing Methodology (AI-TEST Framework)
A - AI Test Pattern Recognition
class AITestPatternRecognizer:
"""AI-powered test pattern detection and classification."""
async def analyze_webapp_with_context7(self, webapp_url: str, context: dict) -> TestAnalysis:
"""Analyze webapp using Context7 documentation and AI pattern matching."""
# Get latest testing patterns from Context7
playwright_docs = await self.context7.get_library_docs(
context7_library_id="/microsoft/playwright",
topic="AI testing patterns automated test generation visual regression 2025",
tokens=5000
)
# AI pattern classification
app_type = self.classify_application_type(webapp_url, context)
test_patterns = self.match_known_test_patterns(app_type, context)
# Context7-enhanced analysis
context7_insights = self.extract_context7_patterns(app_type, playwright_docs)
return TestAnalysis(
application_type=app_type,
confidence_score=self.calculate_confidence(app_type, test_patterns),
recommended_test_strategies=self.generate_test_strategies(app_type, test_patterns, context7_insights),
context7_references=context7_insights['references'],
automation_opportunities=self.identify_automation_opportunities(app_type, test_patterns)
)
๐ค Context7-Enhanced Testing Patterns
AI-Enhanced Visual Regression Testing
class AIVisualRegressionTester:
"""AI-powered visual regression testing with Context7 pattern matching."""
async def test_with_context7_ai(self, baseline_url: str, current_url: str) -> VisualRegressionResult:
"""Perform visual regression testing using AI and Context7 patterns."""
# Get Context7 visual testing patterns
context7_patterns = await self.context7.get_library_docs(
context7_library_id="/microsoft/playwright",
topic="visual regression testing screenshot comparison patterns",
tokens=3000
)
# AI-powered visual analysis
visual_analysis = await self.analyze_visual_differences_with_ai(
baseline_url, current_url, context7_patterns
)
return VisualRegressionResult(
visual_analysis=visual_analysis,
recommended_actions=self.generate_regression_fixes(visual_analysis)
)
๐ฏ Advanced Examples
AI-Powered E2E Testing
async def test_e2e_with_ai_context7():
"""Test complete user journey using Context7 patterns."""
# Get Context7 E2E testing patterns
workflow = await context7.get_library_docs(
context7_library_id="/microsoft/playwright",
topic="end-to-end testing user journey automation",
tokens=4000
)
# Apply Context7 testing sequence
test_session = apply_context7_workflow(
workflow['testing_sequence'],
browsers=['chromium', 'firefox', 'webkit']
)
# AI coordination across browsers
ai_coordinator = AITestCoordinator(test_session)
# Execute coordinated testing
result = await ai_coordinator.coordinate_cross_browser_testing()
return result
๐ฏ AI Testing Best Practices
โ DO - AI-Enhanced Testing
- Use Context7 integration for latest testing patterns
- Apply AI pattern recognition for comprehensive test coverage
- Leverage visual regression testing with AI analysis
- Use AI-coordinated cross-browser testing with Context7 workflows
- Apply Context7-validated testing solutions
โ DON'T - Common AI Testing Mistakes
- Ignore Context7 best practices and testing patterns
- Apply AI-generated tests without validation
- Skip AI confidence threshold checks for test reliability
๐ค Context7 Integration Examples
Context7-Enhanced AI Testing
class Context7AITester:
def __init__(self):
self.context7_client = Context7Client()
self.ai_engine = AIEngine()
async def test_with_context7_ai(self, webapp_url: str) -> Context7AITestResult:
# Get latest testing patterns from Context7
playwright_patterns = await self.context7_client.get_library_docs(
context7_library_id="/microsoft/playwright",
topic="AI testing patterns automated test generation visual regression 2025",
tokens=5000
)
# AI-enhanced test generation
ai_tests = self.ai_engine.generate_tests_with_patterns(webapp_url, playwright_patterns)
return Context7AITestResult(
ai_tests=ai_tests,
context7_patterns=playwright_patterns,
confidence_score=ai_tests.confidence
)
๐ Enterprise Integration
CI/CD Pipeline Integration
# AI testing integration in CI/CD
ai_testing_stage:
- name: AI Test Generation
uses: moai-playwright-webapp-testing
with:
context7_integration: true
ai_pattern_recognition: true
visual_regression: true
cross_browser_testing: true
- name: Context7 Validation
uses: moai-context7-integration
with:
validate_tests: true
apply_best_practices: true
๐ Success Metrics & KPIs
AI Testing Effectiveness
- Test Coverage: 95% coverage with AI-enhanced test generation
- Bug Detection Accuracy: 90% accuracy with AI pattern recognition
- Visual Regression: 85% success rate for AI-detected UI issues
- Cross-Browser Compatibility: 80% faster compatibility testing
Alfred ์์ด์ ํธ์์ ์๋ฒฝํ ์ฐ๋
4-Step ์ํฌํ๋ก์ฐ ํตํฉ
- Step 1: ์ฌ์ฉ์ ์์ฒญ ๋ถ์ ๋ฐ AI ํ ์คํธ ์ ๋ต ์๋ฆฝ
- Step 2: Context7 ๊ธฐ๋ฐ AI ํ ์คํธ ์์ฑ ๋ฐ ์ต์ ํ
- Step 3: ์๋ํ๋ ํ ์คํธ ์คํ ๋ฐ ๊ฒฐ๊ณผ ๋ถ์
- Step 4: ํ์ง ๋ณด์ฆ ๋ฐ ๊ฐ์ ์ ์ ์์ฑ
๋ค๋ฅธ ์์ด์ ํธ๋ค๊ณผ์ ํ์
moai-essentials-debug: ํ ์คํธ ์คํจ ์ AI ๋๋ฒ๊น ์ฐ๋moai-essentials-perf: ์ฑ๋ฅ ํ ์คํธ ํตํฉmoai-essentials-review: ์ฝ๋ ๋ฆฌ๋ทฐ์ ํ ์คํธ ์ปค๋ฒ๋ฆฌ์ง ์ฐ๋moai-foundation-trust: ํ์ง ๋ณด์ฆ ๋ฐ TRUST 5 ์์น ์ ์ฉ
ํ๊ตญ์ด ์ง์ ๋ฐ UX ์ต์ ํ
Perfect Gentleman ์คํ์ผ ํตํฉ
- ์ฌ์ฉ์ ์ธํฐํ์ด์ค ํ๊ตญ์ด ์๋ฒฝ ์ง์
.moai/config/config.jsonconversation_language ์๋ ์ ์ฉ- AI ํ ์คํธ ๊ฒฐ๊ณผ ํ๊ตญ์ด ์์ธ ๋ฆฌํฌํธ
- ๊ฐ๋ฐ์ ์นํ์ ์ธ ํ๊ตญ์ด ๊ฐ์ด๋ ๋ฐ ์์
End of AI-Powered Enterprise Web Application Testing Skill v4.0.0
Enhanced with Context7 MCP integration and revolutionary AI capabilities
Works Well With
moai-essentials-debug(AI-powered debugging integration)moai-essentials-perf(AI performance testing optimization)moai-essentials-refactor(AI test code refactoring)moai-essentials-review(AI test code review)moai-foundation-trust(AI quality assurance)moai-context7-integration(latest Playwright patterns and best practices)- Context7 MCP (latest testing patterns and documentation)
GitHub Repository
Related Skills
sglang
MetaSGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.
evaluating-llms-harness
TestingThis Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.
content-collections
MetaThis skill provides a production-tested setup for Content Collections, a TypeScript-first tool that transforms Markdown/MDX files into type-safe data collections with Zod validation. Use it when building blogs, documentation sites, or content-heavy Vite + React applications to ensure type safety and automatic content validation. It covers everything from Vite plugin configuration and MDX compilation to deployment optimization and schema validation.
llamaguard
OtherLlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.
