Skill UI

Utilize zero-shot NER to extract custom entity types (e.g., Drug, Symptom, Device) from complex clinical or biomedical text without requiring any labeled training data or fine-tuning. OpenMed wraps GLiNER/GLiNER2, allowing users to define labels on the fly. The workflow involves building a model index and then running inference via CLI or Python API, making it ideal for rapid prototyping or unstable schemas.

This skill interfaces with the modern v2 REST API of ClinicalTrials.gov, allowing users to search for clinical studies matching specific conditions, interventions, and recruitment statuses. It is essential for building patient-to-trial matching features, generating a corpus of trial records for biomedical NER, or finding open studies for a given diagnosis or drug. It uses cursor pagination and requires no API key.

This skill queries the NCATS Translator ARAX API for biomedical knowledge-graph relationships. It supports one-hop and two-hop lookups with Biolink constraints, entity normalization, and provider federation. Users can inspect provenance and edge bindings. It is designed for public research queries only, requiring Python 3.10+ and HTTPS access. Do not use for clinical guidance or sensitive data.

Search and read biomedical papers, regulatory documents, clinical trials, and protein databases via the Paperclip CLI, with semantic search, SQL, map/reduce, and line-pinned citations.