Skill UI

A comprehensive tool designed to reorganize and optimize your professional networks on X and LinkedIn. Instead of treating outbound connections as mere lead lists, it analyzes your social graph by identifying pruning opportunities, recommending high-value connections to keep, and generating warm outreach drafts. Use it to enhance relationship quality and strategic growth.

A conversational onboarding skill designed to deeply understand the user and define the core identity of an AI partner. Through adaptive, multi-phase dialogue, it extracts key behavioral traits, motivations, and relationship dynamics, culminating in the generation of a comprehensive SOUL.md file that governs the AI's personality and interaction style. Use this skill when setting up, personalizing, or updating your AI agent.

This skill analyzes Azure resource groups in depth, identifying all resources, their configurations, and complex interdependencies. It maps relationships—including network connections, data flows, and managed identities—to generate comprehensive, detailed Mermaid architecture diagrams. Use this when you need a clear, structured visualization and full documentation of an Azure environment's topology.

Generates Excalidraw diagrams from natural language prompts, covering flowcharts, relationship maps, mind maps, architecture, data flow, swimlane, class, sequence, and ER diagrams for communicating processes and systems.

A comprehensive expert prompt for evaluating Power BI data models. It guides the assessment of model architecture (Star Schema, relationships), storage optimization (Import, DirectQuery, Dual), data quality, performance scalability, and governance best practices. Use this when reviewing an existing BI model to ensure it is robust, maintainable, and highly performant.

Helps implement optimized Power BI semantic models by analyzing the active model with MCP tools, then advising on star schemas, DAX, relationships, security, documentation, and performance best practices for reporting scenarios.

A persistent, structured knowledge base designed to accumulate and connect all research artifacts—including papers, ideas, experiments, and claims—across the entire research lifecycle. Inspired by LLM Wiki patterns, it builds a comprehensive, evolving graph of knowledge by tracking typed relationships (e.g., supports, contradicts, extends), ensuring that findings are compounded and never re-derived. Ideal for complex, multi-stage scientific projects.

This technique is used when the required answer in a case query is not directly stated but must be deduced by following a sequence of connected roles, people, or events. It involves mapping complex relationships, such as Victim -> Defendant -> Lawyer -> Witness, to trace indirect connections and reach the final piece of information. Ideal for advanced investigative research and deep knowledge graph traversal.

This skill enhances research query formulation by translating vague, colloquial, or generalized descriptions into precise, standard academic terminology. When initial search terms are broad (e.g., "conjugal happiness"), this technique suggests multiple plausible academic synonyms (e.g., "marital satisfaction," "relationship quality"). By improving the precision of the search query and incorporating structured constraints (like keywords, years, or sample sizes), it significantly boosts the relevance and effectiveness of academic paper retrieval.

This skill is designed for understanding complex database structures. It allows users to list all available tables, intelligently filter relevant tables based on natural language queries, retrieve detailed schemas (columns, types, primary/foreign keys), and map inter-table relationships. It culminates in generating the M-Schema format, which is essential for accurately preparing the context needed for advanced SQL query generation.

This technique guides the identification of musicians by prioritizing unique personal life events over discography details (albums or songs). It instructs users to use highly distinctive biographical information—such as career turning points, specific personal relationships, or unusual life incidents—as the primary search anchor. This method significantly improves search accuracy when typical musical clues are insufficient.

Transforms the AI into a highly personalized virtual tutor, adopting a defined persona, teaching style, and emotional relationship. This skill uses the Socratic method and advanced psychological principles (like Self-Determination Theory and memory scaffolding) to guide users through discovery learning. It doesn't just deliver answers; it fosters genuine engagement, maintains continuity across sessions, and motivates the user to learn by simulating the experience of a caring, challenging human mentor.