Skill UI

Leverages 78 public scientific, biomedical, materials, and economic REST APIs to find compounds, genes, pathways, clinical trials, patents, economic indicators, weather, and other structured data, returning raw JSON plus the databases/endpoints used.

A comprehensive toolkit for developing and executing genomics pipelines on the DNAnexus cloud platform. It provides capabilities for app/applet building, managing large biomedical datasets (FASTQ, BAM, VCF), orchestrating complex workflows, and interacting programmatically using the dxpy Python SDK.

A comprehensive Python library for handling DICOM (Digital Imaging and Communications in Medicine) files. Use this skill for reading, writing, and manipulating complex medical images such as CT, MRI, and X-ray. Features include metadata extraction, pixel data manipulation, image anonymization (PHI removal), multi-format conversion, and processing multi-slice volumes, making it essential for medical data analysis and PACS integration.

PyHealth is a comprehensive Python toolkit designed for building deep-learning pipelines in the clinical and healthcare domains. It supports diverse data modalities, including EHR records, physiological signals, and medical images. The framework follows a stable 5-stage pipeline (Dataset → Task → Model → Trainer → Metrics), enabling users to perform complex tasks like predicting mortality, optimizing drug recommendations, staging sleep, or cross-mapping medical codes (ICD/ATC/RxNorm) on datasets like MIMIC-III/IV and OMOP.

PyTDC (Therapeutics Data Commons) is an open-science platform providing comprehensive, AI-ready datasets and benchmarks for the entire drug discovery and development pipeline. It supports single-instance prediction (e.g., ADME, toxicity), multi-instance prediction (DTI, DDI, PPI), and novel molecule generation (MolGen). Use this skill for benchmarking machine learning models and predicting complex biomedical properties.

Access a comprehensive network of 78 public databases across scientific, biomedical, environmental, and physical domains. This skill enables deterministic and reproducible data retrieval through documented REST APIs. It manages complex workflows including exhaustive pagination, cross-referencing identifiers across sources (e.g., PubChem, UniProt), and generating full provenance logs, ensuring results are auditable for high-stakes scientific research.

This workflow implements Abridge's end-to-end clinical documentation pipeline. It captures ambient audio from patient encounters, performs real-time transcription, utilizes generative AI to structure the medical notes (e.g., SOAP, H&P), generates required medical codes (ICD-10, CPT), and finally inserts the structured data directly into the EHR system. Designed for primary clinical encounters, minimizing manual charting time.

This minimal working example demonstrates Abridge's ambient AI clinical documentation capabilities. It simulates a complete patient encounter session, allowing users to submit transcripts and receive a highly structured clinical note. Ideal for testing EHR connectivity, verifying AI workflow, or learning how structured medical data (SOAP notes, ICD/CPT codes) is automatically generated from free-text conversation.

Utilize the idc-index Python package to access and query massive public datasets from the NCI Imaging Data Commons (IDC). This skill allows users to search, download, and visualize large-scale radiology (CT, MR, PET) and pathology (slide) images. It is essential for AI model training, medical research, and complex medical data analysis.

This skill guides the systematic process of conducting comprehensive academic literature reviews. It allows users to search multiple biomedical, scientific, and technical databases (e.g., PubMed, arXiv, Semantic Scholar) to synthesize current knowledge. The output generates professionally formatted reports (Markdown/PDF) with verified citations, and mandatorily integrates AI-generated scientific schematics to enhance visualization and depth, suitable for research papers, meta-analyses, and state-of-the-art reports.

This guide provides a comprehensive workflow for searching and retrieving biomedical literature from PubMed and NCBI. It covers advanced query construction using MeSH terms and specific field tags, advanced filtering (date, publication type), and a repeatable programmatic workflow using NCBI E-utilities (esearch, efetch). Essential for researchers conducting systematic reviews and literature monitoring.

This skill provides comprehensive tools and methodologies for working with the Brain Imaging Data Structure (BIDS) standard. It is essential for organizing, validating, querying, and converting complex multi-modal neuroscience and biomedical datasets (including MRI, EEG, MEG, PET, etc.). Use this when setting up BIDS-compliant directory structures, ensuring data integrity before publication, or converting raw DICOM scans into standardized formats.