Skill UI

Automates capturing chat insights into structured Notion pages by extracting key concepts, organizing content by type, and linking newly created entries into the right wikis and databases for easy discovery.

Implements advanced Role-Based Access Control (RBAC) for collaborative Obsidian workspaces. This system allows administrators to define granular permissions (read, write, delete, manage) for different user roles (Admin, Editor, Viewer) across specific folders or paths within a shared vault. It is achieved via a custom plugin that intercepts file operations, ensuring data integrity and proper collaboration control in enterprise team environments. Ideal for managing large, secure knowledge bases.

This runbook provides systematic procedures for diagnosing and resolving critical failures within the Obsidian knowledge base. It guides users through recovering from plugin crashes, vault corruption, sync conflicts, and performance degradation using advanced command-line tools and developer console access. Essential for advanced users and technical support.

This deep-dive tool automates the complex process of migrating notes and knowledge bases from various platforms (such as Notion, Evernote, Roam, etc.) into Obsidian. It handles critical conversions like stripping platform-specific UUIDs, converting internal links to optimized Obsidian wikilinks ([[...]]), and correctly formatting attachments and YAML frontmatter, ensuring a seamless transition and preserving the structural integrity of your knowledge base.

A comprehensive guide for optimizing Obsidian plugin performance, especially when dealing with large knowledge vaults (10,000+ files). Learn advanced techniques such as profiling bottlenecks with DevTools, implementing lazy initialization to speed up startup, processing files in manageable batches with UI yielding, and utilizing LRU caches for bounded memory management. Essential for creating stable, high-performance plugins.

A comprehensive workflow for systematically reviewing medical evidence. It allows users to search databases for clinical studies, build curated citation collections, grade evidence using established frameworks like GRADE, and generate professionally formatted, structured reports in academic citation styles (e.g., AMA). Ideal for conducting literature reviews and supporting clinical research. This tool helps transform raw medical data into actionable, structured knowledge.

This guide provides a comprehensive roadmap for migrating and upgrading integrations with the OpenEvidence clinical AI platform. It addresses critical API changes, such as schema restructuring (e.g., flat answers to structured evidence), version detection, and updated citation formats. Developers can use this knowledge to implement robust migration logic, ensuring that downstream clinical applications maintain high levels of accuracy and safety when accessing evolving medical evidence.

This skill provides comprehensive capabilities for working with PowerPoint files (.pptx). It supports end-to-end workflows, including creating new presentations from scratch based on design principles, sophisticated content editing, and deep structural analysis. Users can extract plain text, access raw underlying XML (for notes, comments, and layouts), and apply expert design knowledge—such as selecting appropriate color palettes and establishing visual hierarchy—to ensure professional and effective presentations.

An expert assistant providing deep knowledge and best practices for Prisma ORM. It covers the entire lifecycle of database interaction, including optimal schema design, relation modeling, safe migration workflows, and advanced query optimization techniques (addressing N+1 issues and implementing raw queries) across PostgreSQL, MySQL, and SQLite.

Qdrant is a high-performance, Rust-powered vector database designed for production-grade RAG (Retrieval-Augmented Generation) and semantic search applications. It provides fast nearest neighbor search, supports complex hybrid search using metadata filtering, and offers horizontal scalability through sharding and replication. Ideal for building real-time recommendation systems and enterprise-level knowledge retrieval.

Designs and implements production-grade RAG systems with document chunking, embeddings, vector stores, hybrid search, reranking, and evaluation, ideal for knowledge-grounded AI apps that need semantic retrieval, similarity search, and context augmentation.

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.