Skill UI

Provides a Python client for querying the Data Commons knowledge graph so teams can resolve entities, fetch statistical observations, and explore public demographic, economic, and health datasets with optional Pandas support.

TorchDrug is a PyTorch-native toolbox for building graph neural networks tailored to molecules, proteins, and biomedical knowledge graphs, covering property prediction, generation, retrosynthesis, and knowledge graph reasoning with curated datasets and models.

Query the PrimeKG precision medicine knowledge graph to explore genes, drugs, diseases, phenotypes, and their relationships, supporting drug discovery, repurposing, and phenotype analysis through programmatic scripts.

PrimeKG is a comprehensive knowledge graph integrating over 20 primary databases to map complex biological relationships. It allows users to query multiscale data, including genes, drugs, diseases, and phenotypes. Ideal for drug discovery, identifying repurposing opportunities, and analyzing deep disease contexts in network pharmacology.

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.

This guide details how to deploy OpenCTI, a powerful, open-source threat intelligence platform, and utilize the pycti Python client. It teaches analysts how to model complex cyber threat intelligence, including threat actors, campaigns, and indicators, into a structured STIX 2.1 knowledge graph. Use this for centralized threat-intel correlation, building adversary graphs, and generating STIX bundles for detection engineering.

This tool performs pathfinder traversal across a knowledge graph starting from a specified seed entity. It expands outward through causal edges, calculates path relevance scores, and prunes low-similarity branches. It is ideal for conducting impact analyses, tracing dependency chains, or understanding complex relationships within a structured knowledge base.

This skill utilizes hyperbolic space embedding (Poincare ball model) to represent complex data structures with inherent hierarchy, such as dependency trees, ontologies, or module taxonomies. Unlike standard Euclidean embeddings, hyperbolic space preserves hierarchical distances efficiently, allowing for accurate distance calculations (geodesic distance) that grow logarithmically with tree depth. It is ideal for advanced knowledge graph analysis and code architecture mapping.

A comprehensive guide detailing the methodology for packaging, documenting, and staging research artifacts (including code, datasets, knowledge graphs, and demo systems) for CIKM conference submission. It covers best practices for licensing, provenance tracking, versioning, and transitioning artifacts from anonymous review status to citable public releases, ensuring reproducibility and adoptability.

This guide details the complex requirements for writing the 'Related Work' section for CIKM submissions. It emphasizes the need for 'three-front coverage' (Retrieval, Mining, and Knowledge Management/Databases) and provides specific instructions on writing the 'boundary-work paragraph.' It also covers critical academic hygiene, such as avoiding venue misattribution, maintaining recency baselines, and accurately stating novel contributions.

This skill automates the complex process of transforming raw research papers (PDF or URL) into structured, knowledge graph-ready resources for OpenViking. It executes a rigorous workflow: reading the entire paper, compiling an ARA-style artifact directory, validating the content against established schemas, and finally ingesting the structured data into the OpenViking knowledge base using the `ov add-resource` command. It ensures all evidence, claims, and logical relationships are captured systematically.