Skill UI

Creates a comprehensive onboarding guide for new teammates by extracting project context from the knowledge graph, layering architecture descriptions, guiding tours, and highlighting complex files so newcomers can ramp up quickly.

Extracts business domain knowledge from a codebase, optionally reusing an existing knowledge graph, and generates an interactive horizontal flow graph for dashboard visualization, with lightweight scanning and validation steps.

A persistent, structured knowledge base designed to accumulate and connect all facets of research—including papers, ideas, experiments, and claims. Inspired by modern LLM patterns, it moves beyond simple literature reviews by building a dynamic, interconnected graph of knowledge. Use it to track how ideas evolve, how experiments validate claims, and how new papers build upon prior work, ensuring knowledge compounding across the entire research lifecycle.

This skill manages a persistent, structured personal knowledge base (Wiki). It provides core operations for ingesting external articles, synthesizing insights from conversations, and querying stored knowledge. By maintaining structured files, index lists, and cross-references, it helps build a comprehensive knowledge graph, ensuring all insights are captured and easily retrievable.

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 provides programmatic access to the PrimeKG, a comprehensive knowledge graph integrating over 20 primary databases. It facilitates multiscale biological research by mapping complex relationships between genes, drugs, diseases, and phenotypes. Use it for drug repurposing, identifying novel disease targets, and analyzing molecular mechanisms across various biological scales.

TorchDrug is a comprehensive PyTorch-based ML toolbox designed for molecular and protein science. It leverages Graph Neural Networks (GNNs) to tackle complex tasks including molecular property prediction, protein structure modeling, drug-target binding prediction, knowledge graph reasoning, and de novo molecular generation. It provides robust capabilities for accelerating drug discovery research.

This skill transforms an LLM into a disciplined wiki maintainer for Obsidian. It moves beyond simple RAG by incrementally ingesting sources (papers, notes, articles) to build a persistent, interlinked "Second Brain." The LLM manages cross-references, updates core concepts, and maintains an evolving knowledge graph, ensuring that knowledge compounds over time rather than being re-derived on every query. Ideal for deep research, personal knowledge management (PKM), and building comprehensive internal wikis.

A comprehensive guide and implementation pattern for querying, searching, and paginating OneNote content using the Microsoft Graph API. This skill addresses the limitations of the standard OneNote search endpoint, providing robust methods for cross-notebook querying, advanced OData filtering on metadata, and client-side full-text indexing for body content. Ideal for building complex knowledge retrieval systems that handle large, distributed result sets.

This advanced skill is an end-to-end tool for transforming complex research topics into professional, visualized knowledge graph PDFs. It automatically conducts multi-round web searches, extracts key entities, structures related knowledge, and designs the optimal visualization (supporting Graphviz, PlantUML, etc.). Ideal for deep academic analysis, mapping character relationships, or visualizing complex technical architectures where existing structured data is unavailable.

A comprehensive workflow for scraping and extracting structured data from Google search results using the SerpApi. It captures all key components, including organic listings, knowledge graphs, answer boxes, People Also Ask (PAA), local pack results, and related searches. Ideal for SEO monitoring, market research, competitive analysis, or building search-powered features that require deep, structured data insights.

Utilize this tool to programmatically access and parse search engine result pages (SERP) data from multiple sources, including Google, Bing, YouTube, and Google News. It returns comprehensive, structured JSON data, allowing developers and marketers to extract organic results, knowledge graph entries, ads, and local information for advanced SEO analysis, market research, and data science projects. Supports both Python and Node.js implementations.