doc-fetcher
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Fetch library and framework documentation via context7-mcp and fetch-mcp
3008 results
(29.4ms)
page 30 / 151
backend-patterns
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后端架构模式、API 设计、数据库优化，以及 Node.js、Express 和 Next.js API 路由的服务端最佳实践。
python-backend
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elevenlabs-agents
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elevenlabs-agents
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performance-testing
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Performance testing specialist for load testing, stress testing, and performance optimization across applications and infrastructure
explanation-docs
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Explanation documentation patterns for understanding-oriented content - conceptual guides that explain why things work the way they do
network-engineer
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Use when user needs network architecture design, security implementation, performance optimization, and troubleshooting for cloud and hybrid environments. Builds reliable, secure networks with...
clarify
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Resolve [NEEDS CLARIFICATION] markers in specs with structured options
Python
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Expert in Model Context Protocol server/client implementation, tool registration, transport layers, and secure MCP integrations
i18n-best-practices
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Use when building internationalization features, managing translation keys, setting up localization workflows, integrating AI translation, connecting GitHub repositories, delivering translations...
Use when implementing OCI GenAI inference APIs, troubleshooting rate limits or token errors, optimizing GenAI costs, or handling sensitive data (PHI/PII) in prompts. Covers model selection, cost...
backend-dev
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Comprehensive backend development workflow that orchestrates expert analysis, architecture design, implementation, and deployment using the integrated toolset. Handles everything from API design...
backend-dev
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Comprehensive backend development workflow that orchestrates expert analysis, architecture design, implementation, and deployment using the integrated toolset. Handles everything from API design...
backend-dev
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Comprehensive backend development workflow that orchestrates expert analysis, architecture design, implementation, and deployment using the integrated toolset. Handles everything from API design...
Autonomously deep-scan entire codebase line-by-line, understand architecture and patterns, then systematically transform it to production-grade, corporate-level professional quality with optimizations
Autonomously deep-scan entire codebase line-by-line, understand architecture and patterns, then systematically transform it to production-grade, corporate-level professional quality with optimizations
Autonomously deep-scan entire codebase line-by-line, understand architecture and patterns, then systematically transform it to production-grade, corporate-level professional quality with optimizations
Autonomously deep-scan entire codebase line-by-line, understand architecture and patterns, then systematically transform it to production-grade, corporate-level professional quality with optimizations
Autonomously deep-scan entire codebase line-by-line, understand architecture and patterns, then systematically transform it to production-grade, corporate-level professional quality with optimizations