全部能力

Generate comprehensive equity research snapshots combining analyst consensus estimates, company fundamentals, historical prices, and macroeconomic context. Use…

Provides configuration patterns for LangChain4J vector stores in RAG applications. Use when building semantic search, integrating vector databases…

Provides Qdrant vector database integration patterns with LangChain4j. Handles embedding storage, similarity search, and vector management for Java…

当用户想要设置、优化或扩展 Apple Search Ads (ASA) 广告系列时——包括关键词竞价、匹配类型、广告系列结构、Creative Sets……

LaTeX CV/resume templates for PhD students and researchers, covering ATS-optimized industry resumes and full academic CVs.

当用户希望设计、测试或优化其应用图标，以提升 App Store 搜索与浏览中的点击率和转化率时使用。适用于用户……

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search,…

为 NeurIPS、ICML、ICLR、ACL、AAAI、COLM 撰写达到发表标准的 ML/AI 论文。适用于基于研究仓库起草论文、组织论证结构、验证…

When the user wants to create, optimize, or structure a documentation site. Also use when the user mentions "docs," "documentation site," "docs subdomain,"…

Comprehensive Tailwind CSS v4 documentation snapshot and workflow guidance. Use when answering Tailwind v4 questions, selecting utilities/variants, configuring…

Machine learning development with JAX, functional programming patterns, and high-performance computing.

Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions. Use this skill when users need to build search functionality for text-based queries (autocomplete, fuzzy matching, faceted search), semantic similarity (embeddings, RAG applications), or combined approaches. Also use when users need text containment, substring matching ('contains', 'includes', 'appears in'), case-insensitive or multi-field text search, or filtering across many fields with variable combinations. Provides workflows for selecting the right search type, creating indexes, constructing queries, and optimizing performance using the MongoDB MCP server.

FAQ identification from support tickets, step-by-step tutorial creation, screenshot/video script guidance, search optimization, and self-service deflection…

Qdrant provides client SDKs for various programming languages, allowing easy integration with Qdrant deployments.

Industrial AI literature research with mandatory intake questions, venue-aware source prioritization, structured report outputs, and survey draft generation. Use when the user needs up-to-date research on predictive maintenance, intelligent scheduling, industrial anomaly detection, smart manufacturing, cyber-physical systems, edge AI for automation, or crossover robotics-for-industry topics. Also trigger for adjacent terms: "digital twin", "industrial IoT", "Industry 4.0", "manufacturing AI", "factory automation", "process optimization", or "survey draft" in industrial contexts.

Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector…

Design loading, skeleton, and progressive content reveal patterns.

Optimize for search engine visibility, ranking, and AI citations. Use when asked to improve SEO, optimize for search, fix meta tags, add structured data, or…

Different techniques to optimize the performance of Qdrant, including indexing strategies, query optimization, and hardware considerations. Use when you want…

优化 App Store 产品页面以提升搜索可见度和转化率。涵盖 App Store Optimization（ASO）策略、关键词研究及关键词字段优化…