Skill UI

Query NCBI ClinVar for variant clinical significance, mixing gene/condition searches, API/FTP downloads, and VCF annotations to support genomic medicine interpretation workflows.

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.

Access HMDB to search over 220K human metabolites by name, ID, structure, disease or specimen, retrieve chemical/clinical/pathway data, and download spectra for metabolomics workflows.

NeuroKit2 is a Python biosignal processing toolkit for ECG, EEG, EDA, RSP, EMG, and EOG data that automates cleaning, feature extraction, and HRV/complexity analysis for psychophysiology, clinical, and HCI studies.

Use the OpenAlex client to query 240M+ scholarly works for literature searches, citation tracking, open access discovery, and bibliometric studies across papers, authors, and institutions.

PyHealth delivers a full-stack Python toolkit for building, evaluating, and deploying clinical machine learning models, covering EHR datasets, medical coding systems, physiological signals, and healthcare-tailored architectures such as RETAIN, SafeDrug, and Transformer-based pipelines.

Query the Reactome Content and Analysis Services (or the reactome2py client) to fetch pathway hierarchies, map genes, run overrepresentation/expression analyses, and explore disease interactions for systems biology research.

Generates concise 3-4 page LaTeX/PDF treatment plans across medical specialties, embedding SMART goals, evidence-based interventions, HIPAA-compliant formatting, and mandated AI-generated schematics for quick clinical actionability.

A robust Python client wrapper for interacting with the Zotero Web API v3. This skill allows users to programmatically manage academic references, including retrieving, creating, updating, and deleting items, collections, tags, and attachments. Ideal for academic researchers building automated workflows, bulk exporting citations (e.g., BibTeX), or integrating Zotero data into larger research applications.

This guide provides a comprehensive walkthrough for developing and deploying Apify Actors. Actors are serverless, containerized applications designed for powerful tasks like web scraping, data processing, and complex automation. Learn the mandatory setup, template selection (Python, JS, TS), step-by-step development workflow, and crucial security best practices for building robust, production-ready data pipelines.

Apify Actorization is a process that transforms existing, standalone software or scripts (like CLI tools) into standardized, reusable serverless applications called Actors. Actors are packaged as Docker images, accepting structured JSON inputs and producing structured JSON outputs. This skill guides developers on migrating projects to the Apify platform, ensuring compatibility, and enabling scalable, event-driven data processing pipelines.