Loading...
Loading...
Found 26 Skills
PocketFlow framework for building LLM applications with graph-based abstractions, design patterns, and agentic coding workflows
Design AI architectures, write Prompts, build RAG systems and LangChain applications
Analyze AI/ML technical content (papers, articles, blog posts) and extract actionable insights filtered through enterprise AI engineering lens. Use when user provides URL/document for AI/ML content analysis, asks to "review this paper", or mentions technical content in domains like RAG, embeddings, fine-tuning, prompt engineering, LLM deployment.
Implement AI Coaching best practices on AnalyticDB for PostgreSQL (ADBPG): Leverage Supabase projects (training data management) + ADBPG instances with vector optimization to build RAG-driven coaching systems that guide users through domain-specific workflows, decision-making, or skill development. Use when: User wants to create Supabase projects (spb-xxx), ADBPG instances (gp-xxx), vector knowledge bases, or RAG-driven coaching systems on ADBPG. Triggers: "Supabase", "ADBPG", "vector database", "knowledge base", "RAG", "AI coaching", "coaching system", "spb-xxx", "gp-xxx"
Provides expertise on Chroma vector database integration for semantic search applications. Use when the user asks about vector search, embeddings, Chroma, semantic search, RAG systems, nearest neighbor search, or adding search functionality to their application.
Evidence-based Drug-Drug Interaction (DDI) assessment skill modeled after the Micromedex Drug-Reax methodology. Trigger this skill whenever the user types /drug-drug, mentions "drug interaction", "DDI", "drug-drug", "can I take X with Y", "interaction between", "交互作用", "併用", or asks whether two medications can be used together. This skill performs systematic literature retrieval via PubMed, CrossRef, and WebSearch, then produces a structured assessment report with Severity, Documentation, Onset, Mechanism, Clinical Effects, and Management — mirroring the Micromedex Drug-Reax classification framework. Even casual questions like "is it safe to combine A and B" should trigger this skill.
Expert prompt engineering for LLM applications including prompt design, optimization, RAG systems, agent architectures, and AI product development.
Vector database implementation for AI/ML applications, semantic search, and RAG systems. Use when building chatbots, search engines, recommendation systems, or similarity-based retrieval. Covers Qdrant (primary), Pinecone, Milvus, pgvector, Chroma, embedding generation (OpenAI, Voyage, Cohere), chunking strategies, and hybrid search patterns.
Engineer effective LLM prompts using zero-shot, few-shot, chain-of-thought, and structured output techniques. Use when building LLM applications requiring reliable outputs, implementing RAG systems, creating AI agents, or optimizing prompt quality and cost. Covers OpenAI, Anthropic, and open-source models with multi-language examples (Python/TypeScript).
Comprehensive skill for Microsoft GraphRAG - modular graph-based RAG system for reasoning over private datasets
Configure pgvector extension for vector search in Supabase - includes embedding storage, HNSW/IVFFlat indexes, hybrid search setup, and AI-optimized query patterns. Use when setting up vector search, building RAG systems, configuring semantic search, creating embedding storage, or when user mentions pgvector, vector database, embeddings, semantic search, or hybrid search.
Build autonomous RAG agents that reason, plan, and use tools for complex retrieval tasks. Use this skill when simple retrieve-and-generate isn't enough. Activate when: agentic RAG, RAG agent, multi-step retrieval, tool-using RAG, autonomous retrieval, query decomposition.