Skill UI

Clarity Gate ensures documents entering RAG knowledge bases retain epistemic quality by generating Clarity-Gated Documents, validating Source of Truth tables, and running deterministic claim/document hash scripts before ingestion.

Master-level expertise in Atlassian Confluence, specializing in building robust knowledge bases and structured documentation systems. Skills include designing complex space hierarchies, configuring granular permissions, creating reusable page templates, and implementing advanced macros (e.g., Jira integration, TOC). Ideal for standardizing corporate documentation, improving content governance, and establishing scalable, collaborative workflows.

Provides elite guidance on dynamic context management, vector databases, knowledge graphs, intelligent memory, RAG, and multi-agent workflows so AI systems maintain relevant, actionable information across enterprise scenarios.

A comprehensive framework for building persistent memory systems within Large Language Models (LLMs). It supports multi-faceted memory management, including short-term context storage, long-term knowledge retrieval, and entity-based fact updates. This skill is crucial for developing sophisticated AI assistants that maintain coherence, recall user history over extended periods, and provide context-aware interactions by managing memory consolidation and retrieval relevance.

Design layered short-term, long-term, and graph-based memory systems so agents persist across sessions, keep entity consistency, and reason over accumulated knowledge during conversations.

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.

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.

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.

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.

Query the Monarch Initiative API to explore disease-gene-phenotype associations across species, enabling rare disease gene discovery, phenotype-to-gene mapping, and cross-species modeling via integrated HPO and model organism data.

Connects Apple Health, Fitbit, Oura and generic exports to WellAlly.tech, importing, normalizing, validating personal health data, and surfacing relevant knowledge-base articles for context-aware wellness recommendations.

Use Memories.ai’s LVMM to discover, summarize, and analyze TikTok, YouTube, Instagram, or Vimeo content, then build searchable knowledge bases, meeting notes, and competitive insights from video libraries.