ai-test-failure-analyzer 是做什么的？
8阶段证据流水线，用于Playwright、pytest、Jest、Cypress、Newman、k6和JUnit测试失败分析。扫描Git历史、应用日志和配置，生成带有置信度评分的根本原因假设。以MCP服务器、CLI和流式Web UI形式提供。
8阶段证据流水线，用于Playwright、pytest、Jest、Cypress、Newman、k6和JUnit测试失败分析。扫描Git历史、应用日志和配置，生成带有置信度评分的根本原因假设。以MCP服务器、CLI和流式Web UI形式提供。
该技能是一个专门用于多框架（Playwright、pytest、Jest、Cypress、Newman、k6和JUnit）测试失败分析的8阶段证据流水线。它通过扫描Git历史记录、应用程序日志和配置文件，生成带有置信度评分的根本原因假设，帮助开发者快速定位问题。其核心优势在于系统化地收集和评估证据，而不是依赖单一信息源。该工具支持多种交互方式：作为MCP（Model Context Protocol）服务器集成到AI工作流中，作为独立CLI命令行工具使用，或者通过流式Web UI获得实时分析结果。对于追求高效开发流程的团队来说，它能显著减少手动调试时间，尤其适用于持续集成环境中的自动化失败分析。
npx skills add aks-builds/ai-test-failure-analyzerInstalled? Explore more 研究与数据分析 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
8阶段证据流水线，用于Playwright、pytest、Jest、Cypress、Newman、k6和JUnit测试失败分析。扫描Git历史、应用日志和配置，生成带有置信度评分的根本原因假设。以MCP服务器、CLI和流式Web UI形式提供。
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.