Skill UI

An expert system for designing and implementing advanced AI context architectures. It specializes in integrating intelligent memory systems, vector databases, and knowledge graphs to ensure AI applications maintain coherent, relevant, and scalable state across complex, multi-agent workflows. Essential for enterprise RAG implementation and stateful AI development.

This skill provides expert guidance for modern, multi-cloud database administration. It covers comprehensive knowledge across AWS, Azure, and GCP platforms, spanning relational, NoSQL, and graph databases. Key areas include Infrastructure as Code (Terraform, Flyway), achieving high availability, disaster recovery, advanced performance optimization, and ensuring strict regulatory compliance (HIPAA, GDPR). It is essential for managing complex, highly reliable cloud data architectures.

This skill provides methodologies for designing sophisticated, layered memory architectures for AI agents. It moves beyond simple context windows and basic vector stores by incorporating short-term, long-term, and graph-based memory systems. Implementing this is crucial for agents that require persistence across sessions, maintaining complex entity consistency, and performing advanced, relationship-driven reasoning over accumulated knowledge bases, including time-aware fact tracking.

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.

An expert guide detailing the SuperScalar protocol and Bitcoin Lightning channel factories. It explains scalable onboarding mechanisms using shared UTXOs, advanced cryptographic structures like Decker-Wattenhofer trees, and MuSig2. This knowledge is vital for understanding how Bitcoin can scale significantly without requiring disruptive soft forks.

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.

This skill implements advanced cryptographic techniques, including the Schnorr identification protocol and a simplified Zero-Knowledge Password Proof (ZKPP). It enables a prover to authenticate by demonstrating knowledge of a secret without ever revealing the underlying password or private key to the server. Ideal for building secure, password-less systems or environments where server compliance mandates that user credentials are never stored or transmitted in plain form.

Analyze a codebase to assemble an interactive knowledge graph showing architecture, components, and relationships, enabling full rebuilds, incremental updates, or audits with optional auto-update control.

Helps developers answer codebase questions by leveraging the `.understand-anything/knowledge-graph.json` graph; walks through project metadata, nodes, edges, and layers to explain dependencies and contexts.

Launches an interactive web dashboard designed to visualize the knowledge graph derived from a target codebase. This tool helps developers gain a high-level, structural understanding of complex projects by mapping out relationships, module dependencies, and the overall knowledge network of the code, making maintenance and onboarding significantly easier.

Use the understand-diff skill to analyze git changes against the stored knowledge graph so you can quickly summarize modified files, affected components, and risk areas before reviewing or merging.