商業

Configure LangChain4J vector stores for RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, Neo4j), implementing embedding storage/retrieval, setting up hybrid search, or optimizing vector database performance for production AI applications.

Unit tests for service layer with Mockito. Test business logic in isolation by mocking dependencies. Use when validating service behaviors and business logic without database or external services.

Comprehensive React 19 patterns expert covering Server Components, Actions, use() hook, useOptimistic, useFormStatus, useFormState, React Compiler, concurrent features, Suspense, and modern TypeScript development. Proactively use for any React development, component architecture, state management, performance optimization, or when implementing React 19's latest features.

Implement Retrieval-Augmented Generation (RAG) systems with LangChain4j. Build document ingestion pipelines, embedding stores, vector search strategies, and knowledge-enhanced AI applications. Use when creating question-answering systems over document collections or AI assistants with external knowledge bases.

Implement optimal chunking strategies in RAG systems and document processing pipelines. Use when building retrieval-augmented generation systems, vector databases, or processing large documents that require breaking into semantically meaningful segments for embeddings and search.

Qdrant vector database integration patterns with LangChain4j. Store embeddings, similarity search, and vector management for Java applications. Use when implementing vector-based retrieval for RAG systems, semantic search, or recommendation engines.

Comprehensive guide for developing on EspoCRM - metadata-driven CRM with service layer architecture

Build comprehensive, mobile-compatible Obsidian study vaults from academic course materials with checkpoint-based workflow, error pattern recognition, and quality assurance. Battle-tested patterns from 828KB/37-file projects. Works across all subjects - CS, medicine, business, self-study.

Automatically discover frontend development skills when working with React, Next.js, UI components, state management, data fetching, forms, accessibility, performance optimization, or SEO. Activates for frontend web development tasks.

Build mobile-compatible Obsidian study vaults from academic materials with checkpoint workflow and QA. Battle-tested on 800KB+ projects. Works across CS, medicine, business.

Guidance for scaling systems from startup to enterprise scale. Use when planning for growth, diagnosing bottlenecks, or designing systems that need to handle 10x-1000x current load.

Perform RFM (Recency, Frequency, Monetary) customer segmentation analysis on e-commerce data. Use when you need to analyze customer value, identify VIP customers, or create marketing segments. Automatically cleans data, calculates RFM metrics, applies K-means clustering, and generates visualization reports with Chinese language support.

Perform multi-touch attribution analysis using Markov chains, Shapley values, and custom attribution models. Use when you need to analyze marketing channel effectiveness, calculate conversion attribution, optimize marketing budgets, or understand customer journey paths. Supports channel transition analysis, ROI calculation, and marketing optimization insights with Chinese language support.

Use to craft executive-ready value narratives and ROI/TCO stories for enterprise pursuits.

Tagging schema for classifying community signals by persona, journey, and business impact.

Narrative and artifact system for preparing SaaS board/investor updates.

Use when planning channel distribution and measuring video performance.

Use to package concise executive updates, decks, and decision logs for enterprise pursuits.

Use when defining or adjusting marketing-to-sales assignment rules.

Use when designing filters, suppression logic, and personalization cohorts for campaigns or automations.