agent

查询 Microsoft 官方文档，以查找跨 Azure、.NET、Agent Framework、Aspire、VS Code、GitHub 等平台的概念、教程和代码示例……

通过 Tavily CLI 进行网页搜索、内容提取、抓取和深度研究。当用户需要搜索网页、查找文章、研究主题、在线查询、从 URL 提取内容、获取网页文本、抓取文档、下载网站页面、发现域名上的 URL 或进行带引用的深入研究时使用此技能。当用户说"获取此页面"、"拉取内容从"、"获取 https:// 页面"、"帮我找关于...的文章"或提及从外部网站提取数据时也使用。这提供 LLM 优化的网页搜索、内容提取、站点抓取、URL 发现和 AI 驱动的深度研究——超越智能体原生能力的功能。请勿在本地文件操作、git 命令、部署或代码编辑任务时触发。

here.now 让智能体能够发布网站并在云盘中存储私有文件。使用 Sites 将 HTML、文档、图像、PDF、视频和静态文件发布至 {slug}.here.now 或自定义域名的在线 URL。使用 Drives 作为私有云文件夹，智能体可在其中存储文件（文档、上下文、记忆、计划、资产、媒体、研究、代码等），与其他智能体共享文件，并能在跨会话和工具间保持连续性。当遇到要求“发布这个”、“托管这个”、“部署这个”、“在网络上分享这个”、“制作一个网站”、“把这个放到网上”、“创建一个网页”、“生成一个 URL”、“构建一个聊天机器人”、“把这个保存到我的 Drive”、“存储这个以备后用”、“把这个写入云存储”、“与另一个智能体共享文件夹”或“使用我的 here.now Drive”时，请使用此工具。

Builds AI agents on Cloudflare using the Agents SDK with state management, real-time WebSockets, scheduled tasks, tool integration, and chat capabilities. Generates production-ready agent code deployed to Workers. Use when: user wants to "build an agent", "AI agent", "chat agent", "stateful agent", mentions "Agents SDK", needs "real-time AI", "WebSocket AI", or asks about agent "state management", "scheduled tasks", or "tool calling". Biases towards retrieval from Cloudflare docs over pre-trained knowledge.

Builds remote MCP (Model Context Protocol) servers on Cloudflare Workers with tools, OAuth authentication, and production deployment. Generates server code, configures auth providers, and deploys to Workers. Use when: user wants to "build MCP server", "create MCP tools", "remote MCP", "deploy MCP", add "OAuth to MCP", or mentions Model Context Protocol on Cloudflare. Also triggers on "MCP authentication" or "MCP deployment". Biases towards retrieval from Cloudflare docs over pre-trained knowledge.

Ultimate AI agent memory system. Combines bulletproof WAL protocol, vector search, git-based knowledge graphs, cloud backup, and maintenance hygiene. Never…

与 Paperclip 控制平面 API 交互，以管理任务、与其他代理协作并遵循公司治理。在需要检查分配的任务、更新任务状态、委派工作、发表评论、设置或管理例程（定期计划任务）或调用任何 Paperclip API 端点时使用。切勿用于实际的领域工作本身（编写代码、研究等）——仅用于 Paperclip 协调。

Salesforce Data Cloud Harmonize phase. TRIGGER when: user works with DMOs, mappings, relationships, identity resolution, unified profiles, data graphs, or universal IDs. DO NOT TRIGGER when: the task is only about streams/DLOs (use sf-datacloud-prepare), segments/insights (use sf-datacloud-segment), retrieval/search (use sf-datacloud-retrieve), or STDM/session tracing (use sf-ai-agentforce-observability).

Salesforce Data Cloud Act phase. TRIGGER when: user manages activations, activation targets, data actions, or downstream delivery of Data Cloud audiences and data. DO NOT TRIGGER when: the task is segment creation (use sf-datacloud-segment), data retrieval/search work (use sf-datacloud-retrieve), or STDM/session tracing (use sf-ai-agentforce-observability).

构建生产级 LLM 应用、高级 RAG 系统和智能体。实现向量搜索、多模态 AI、智能体编排，以及…

Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.

Neural pattern training with SONA (Self-Optimizing Neural Architecture), MoE (Mixture of Experts), and EWC++ for knowledge consolidation. Use when: pattern learning, model optimization, knowledge transfer, adaptive routing. Skip when: simple tasks, no learning required, one-off operations.

Advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows

AgentDB memory system with HNSW vector search. Provides 150x-12,500x faster pattern retrieval, persistent storage, and semantic search capabilities for learning and knowledge management. Use when: need to store successful patterns, searching for similar solutions, semantic lookup of past work, learning from previous tasks, sharing knowledge between agents, building knowledge base. Skip when: no learning needed, ephemeral one-off tasks, external data sources available, read-only exploration.

Byzantine fault-tolerant consensus and distributed coordination. Queen-led hierarchical swarm management with multiple consensus strategies. Use when: distributed coordination, fault-tolerant operations, multi-agent consensus, collective decision making. Skip when: single-agent tasks, simple operations, local-only work.

Cloud computing platform for running Python on GPUs and serverless infrastructure. Use when deploying AI/ML models, running GPU-accelerated workloads, serving…

Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses…

Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. Use for pKa and macropKa prediction, conformer and…

High-performance genomic interval operations and bioinformatics file I/O on Polars DataFrames. Overlap, nearest, merge, coverage, complement, subtract for…

Submit and manage protocols on Ginkgo Bioworks Cloud Lab (cloud.ginkgo.bio), a web-based interface for autonomous lab execution on Reconfigurable Automation…