qe-n8n-expression-testing
n8n expression syntax validation, context-aware testing, common pitfalls detection, and performance optimization. Use when validating n8n expressions and data transformations.
浏览 proffesor-for-testing 维护的已发布 AI 技能,比较用途与说明,并通过来源仓库了解安装方式。
n8n expression syntax validation, context-aware testing, common pitfalls detection, and performance optimization. Use when validating n8n expressions and data transformations.
Security auditing, vulnerability scanning, and compliance validation for OWASP, SOC2, GDPR, and other standards.
AI-powered defect prediction, pattern learning, and root cause analysis for proactive quality management.
Quality Engineering iteration loops for autonomous test improvement, coverage achievement, and quality gate compliance. Use when tests need to pass, coverage targets must be met, quality gates require complianc...
Comprehensive quality gates, metrics analysis, and deployment readiness assessment for continuous quality assurance.
Consumer-driven contract testing for APIs including REST, GraphQL, and event-driven systems with schema validation.
Parallel test execution orchestration with intelligent scheduling, retry logic, and comprehensive result aggregation.
Quality Engineering iteration loops for autonomous test improvement, coverage achievement, and quality gate compliance. Use when tests need to pass, coverage targets must be met, quality gates require complianc...
Implement continuous AI iteration loops for complex development tasks. Use when building features requiring test-driven refinement, implementing tasks with clear success criteria, or automating iterative improv...
AI-powered test generation using pattern recognition, code analysis, and intelligent test synthesis for comprehensive test coverage.
O(log n) sublinear coverage gap detection with risk-weighted analysis and intelligent test prioritization.
Security-first visual testing combining URL validation, PII detection, and visual regression with parallel viewport support. Use when testing web applications that handle sensitive data, need visual regression...
Optimized MCP server for QE with connection pooling, O(1) tool lookup, load balancing, and
Requirements traceability, acceptance criteria validation, and BDD scenario management for complete requirements coverage.
Domain-Driven Design architecture for Agentic QE v3. Implements modular, bounded context architecture with clean separation of concerns for quality engineering domains.
Chaos engineering and resilience testing including fault injection, load testing, and system recovery validation.
Transfer learning, metrics optimization, and continuous improvement for AI-powered QE agents.
Deep integration with agentic-flow@alpha for SONA learning, Flash Attention (2.49x-7.47x speedup), and 9 RL algorithms.
Knowledge graph-based code understanding with semantic search and 80% token reduction through intelligent context retrieval.
Unified memory system for QE with AgentDB, HNSW indexing (150x-12,500x faster search), and SONA learning integration.
James Bach's HTSM Product Factors (SFDIPOT) analysis for comprehensive test strategy generation. Use when analyzing requirements, epics, or user stories to generate prioritized test ideas across Structure, Func...
Migrate Agentic QE projects from v2 to v3 with zero data loss
Migrate Agentic QE projects from v2 to v3 with zero data loss
Visual regression testing, responsive design validation, and WCAG accessibility compliance testing.
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库,同一仓库的多个技能可能共享相同数值;它反映仓库关注度,不代表单个技能的安装量或质量评分。