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Vine review program strategy — enrollment, product selection, timing, review quality maximization

Complete FBA preparation guide. Product labeling, packaging requirements, shipment planning, and compliance with Amazon's fulfillment center requirements. Avoid common rejection reasons.

Audita websites para SEO, aspectos técnicos, conteúdo, segurança, renderização de JS e prontidão para IA usando o SEOmator CLI. Retorna relatórios otimizados para LLM com pontuações de saúde em 251 regras e 20 categorias, ideal para analisar sites, depurar problemas de SEO ou verificar a saúde do site.

Provides unit test, integration test, and mock AI patterns for LangChain4j applications. Creates mock LLM responses, tests retrieval chains, validates RAG workflows, and implements Testcontainers-based integration tests for Java AI services. Use when unit testing AI services, integration testing LangChain4j components, mocking AI models, or testing LLM-based Java applications.

Provides workflows to write, debug, and optimize prompts for LLMs, including few-shot example selection, chain-of-thought structuring, system prompt design, and template composition. Use when the user asks to write or improve a prompt, wants help with few-shot examples, chain-of-thought, system prompts, prompt templates, or asks how to get better results from an LLM.

Provides and generates LangChain4j tool and function calling patterns: annotates methods as tools with @Tool, configures tool executors, registers tools with AiServices, validates tool parameters, and handles tool execution errors. Use when building AI agents that call tools, define function specifications, manage tool responses, or integrate external APIs with LLM-driven applications.

Provides integration patterns for LangChain4j with Spring Boot. Configures AI model beans, sets up chat memory with Spring context, integrates RAG pipelines with Spring Data, and handles auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications, building Java LLM applications with @Bean configuration, or setting up Spri

Provides patterns to build declarative AI Services with LangChain4j for LLM integration, chatbot development, AI agent implementation, and conversational AI in Java. Generates type-safe AI services using interface-based patterns, annotations, memory management, and tools integration. Use when creating AI-powered Java applications with minimal boilerplate, implementing conversational AI with memory

ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization.

Answer Engine Optimization (AEO): optimize content to be cited by LLMs (ChatGPT, Claude, Perplexity, Gemini, Copilot) when they answer user questions. Use when designing content strategy for the LLM-citation era, auditing existing content for LLM citability, structuring Q&A schema for AI surfaces, tracking which content gets cited and where, or measuring AEO performance against competitors. Distin

Optimize content for AI search engines including Google AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and Microsoft Copilot. Covers generative engine optimization (GEO), AI citability audits, content structuring for extraction, schema markup, bot access configuration, and monitoring. Use when optimizing for AI search, AI overviews, generative search, LLM visibility, semantic search, entity op

Strategic advisory for e-commerce founders covering unit economics, fulfillment models (3PL, DTC, dropship, retail), payment economics, returns / refunds, and channel strategy (DTC site / Amazon / wholesale / retail). Use when evaluating an ecommerce idea, modeling unit economics, picking fulfillment strategy, or when the user mentions DTC, e-commerce, Shopify, Amazon, 3PL, CAC, contribution margi