Skill UI

Expert in designing and implementing advanced Retrieval-Augmented Generation (RAG) pipelines. This role focuses on optimizing the entire information retrieval process—from document chunking and embedding model selection to vector database architectures and hybrid search strategies. The goal is to bridge the gap between raw knowledge documents and accurate LLM understanding, drastically reducing hallucination and enhancing context relevance for enterprise applications.

This comprehensive workflow guides the end-to-end implementation of Retrieval-Augmented Generation (RAG) systems. It covers critical stages, including selecting optimal embedding models, setting up vector databases, designing chunking strategies, implementing hybrid retrieval, integrating Large Language Models (LLMs), and performing rigorous evaluation. Use this when building knowledge-grounded AI applications or advanced semantic search systems.

Automates Trello boards, cards, lists, assignments, searches, comments, and attachments through Composio’s Rube MCP, so teams can script workflows like card creation, movement, member assignment, and data retrieval without manual clicks.

Expert capability for designing and implementing complex semantic search systems using vector databases. Proficient in embedding strategies, index optimization (HNSW, IVF), and advanced retrieval methods like RAG and hybrid search. Masters major platforms including Pinecone, Weaviate, Qdrant, Milvus, and pgvector for building sophisticated recommendation and similarity search applications.

Direct Python helpers for KEGG's REST API, covering database metadata, listing, search, retrieval, ID conversion, linkage, and drug interactions to power pathway and gene analysis.

Direct REST/API access to PubMed for constructing complex Boolean or MeSH queries, filtering by publication attributes, and automating literature retrieval or citation management via NCBI’s E-utilities.

This skill integrates multiple digital health sources (such as Apple Health, Fitbit, Oura, and CSV/JSON files) into a unified system. It processes, standardizes, and validates diverse health data formats, and connects this data to a dedicated knowledge base (WellAlly.tech). It provides intelligent recommendations and article retrieval based on the user's current health context and needs.

Comprehensive guide to LLM Operations (LLMOps). Covers advanced topics like Retrieval-Augmented Generation (RAG), vector database management (Pinecone, Chroma), embeddings, fine-tuning, prompt engineering, and cost optimization. Essential for bridging the gap between AI prototypes and reliable, scalable, production-grade AI systems.

Trackio is a comprehensive library for logging, visualizing, and managing ML experiment metrics and alerts. It allows developers to use a Python API to log metrics and set diagnostic alerts (INFO/WARN/ERROR) during training. Furthermore, a dedicated CLI enables metric retrieval and dashboard viewing, syncing data to Hugging Face Spaces for persistent, remote monitoring, making it ideal for autonomous ML agent workflows.

This skill provides programmatic access to Alpha Vantage's extensive global financial dataset. It enables the retrieval of historical and real-time data across diverse asset classes, including equities, options, forex, cryptocurrencies, commodities, and key economic indicators. Ideal for quantitative research, market trend analysis, and building financial dashboards.

A powerful toolset for automating core PostHog product analytics tasks via Rube MCP. It enables developers and analysts to capture user events, list and filter event data, manage feature flags (creation, retrieval, listing), and inspect user profiles. Ideal for integrating advanced product tracking and A/B testing workflows directly into development pipelines.

This skill teaches users how to optimize search queries for specific, structural information found within academic theses or dissertations. It emphasizes the critical need to include qualifying terms like "acknowledgment," "thesis," or "dissertation" in searches to successfully retrieve details about committee members, dedicating recipients, or other section-specific data, rather than just topic abstracts. This dramatically improves the relevance and accuracy of information retrieval.