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Found 277 Skills
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
Resolve queries or URLs into compact, LLM-ready markdown using a low-cost cascade. Prioritizes llms.txt for structured docs, uses web fetch/search tools for extraction. Use when you need to fetch documentation, resolve web URLs to markdown, search for technical content, or build context from web sources.
AI session compression techniques for managing multi-turn conversations efficiently through summarization, embedding-based retrieval, and intelligent context management.
DSPy declarative framework for automatic prompt optimization treating prompts as code with systematic evaluation and compilers
Testing strategies for LangChain4j-powered applications. Mock LLM responses, test retrieval chains, and validate AI workflows. Use when testing AI-powered features reliably.
Build interactive chat agents for exploring and discussing academic research papers from ArXiv. Covers paper retrieval, content processing, question-answering, and research synthesis. Use when building research assistants, paper summarization tools, academic knowledge bases, or scientific literature chatbots.
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Upstash Vector DB setup, semantic search, namespaces, and embedding models (MixBread preferred). Use when building vector search features on Vercel.
Integration patterns for LangChain4j with Spring Boot. Auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications.
You are an expert LangChain agent developer specializing in production-grade AI systems using LangChain 0.1+ and LangGraph.
Expert in managing the "Memory" of AI systems. Specializes in Vector Databases (RAG), Short/Long-term memory architectures, and Context Window optimization. Use when designing AI memory systems, optimizing context usage, or implementing conversation history management.
Use this skill when building NLP pipelines, implementing text classification, semantic search, embeddings, or summarization. Triggers on text preprocessing, tokenization, embeddings, vector search, named entity recognition, sentiment analysis, text classification, summarization, and any task requiring natural language processing.