Skill UI

Enables efficient querying of the bioRxiv preprint server with keywords, authors, dates, and categories, returning structured JSON metadata and optional PDF downloads for literature reviews or tracking research trends.

Provides programmatic access to the CZ CELLxGENE Census (61M+ cells) for querying standardized single-cell expression, metadata, embeddings, and stats across tissues and diseases, supporting scanpy and ML workflows.

Provide REST API access to ClinPGx, the successor to PharmGKB, for querying gene-drug interactions, CPIC/DPWG/FDA guidelines, allele functions, and evidence that supports precision dosing and pharmacogenomics-driven clinical decisions.

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.

gget is a CLI/Python client that unifies querying over 20+ bioinformatics databases, letting researchers fetch gene info, sequences, BLAST results, structures, and enrichment data interactively or within scripts.

LaminDB is a Python-driven open-source framework for managing biological datasets, ensuring traceable, reproducible, FAIR data through lineage tracking, schema validation, ontology annotation, workflow integration, and lakehouse-style querying.

This expert assists developers in building type-safe, high-performance database layers using Drizzle ORM with TypeScript. It provides deep knowledge of schema design, complex relational querying (joins, aggregations), managing migrations via Drizzle Kit, and seamless integration with modern stacks like Next.js, tRPC, and serverless environments. Ideal for developers needing a zero-overhead, type-safe ORM alternative.

This skill provides comprehensive functionality for querying, comparing, and calculating nutritional data from a structured food database. Users can perform precise searches by food name or nutritional criteria (e.g., high protein, low GI). It supports calculating total nutrient intake based on specific portions, suggesting foods for various health conditions (like diabetes or high blood pressure), and performing detailed comparative analysis between multiple food items.

A comprehensive skill for managing the entire lifecycle of datasets on the Hugging Face Hub. It allows users to initialize, configure, and stream updates for datasets of various types (Chat, Text Classification, Tabular). Crucially, it extends data handling capabilities by enabling powerful SQL-based querying and transformation directly on Hub datasets using the DuckDB engine, facilitating advanced data exploration and pipeline creation.

A comprehensive collection of six expert-level, structured workflows designed to elevate AI assistance from ad-hoc querying to repeatable, production-grade engineering practices. Users can evaluate prompts, design RAG architectures, plan context budgets, run security audits, build eval harnesses, and coach product strategy.

This skill provides a programmatic method for analyzing Advanced Persistent Threat (APT) groups using the MITRE ATT&CK framework. By querying TTPs and generating multi-layer JSON files compatible with ATT&CK Navigator, analysts can visualize threat actor coverage, map detection capabilities, and identify critical security gaps to build highly targeted, threat-informed defenses.

This skill guides users on querying Azure Monitor activity and sign-in logs using KQL to detect sophisticated security threats. It covers identifying suspicious administrative activities, impossible travel scenarios, privilege escalation attempts, and unauthorized resource modifications. Ideal for Security Operations Center (SOC) analysts building advanced threat hunting rules or developing cloud SIEM detections.