Skill UI

Converts raw OpenMed NER outputs (text spans) into structured, standards-compliant FHIR R4 resources (e.g., Condition, Observation, MedicationStatement). It provides mechanical helpers for building CodeableConcepts using major coding systems like SNOMED, LOINC, and ICD-10, ensuring high interoperability with EHR systems and registries.

This utility separates clinical entity extraction from clinical coding. It processes de-identified text, utilizing OpenMed to identify key clinical spans. These spans are then programmatically mapped into structured FHIR R4 resources (like Condition, Observation, and MedicationStatement) and assembled into a Bundle. This process ensures deterministic, standardized data output suitable for interoperability and medical record structuring.

Utilizes OpenMed's analyze_text to perform sophisticated Named-Entity Recognition (NER) on clinical and biomedical text. This tool extracts structured entities such as diseases, drugs, anatomy, genes, and proteins. It provides precise character spans and model confidence scores, making it essential for building robust, privacy-first clinical Natural Language Processing (NLP) pipelines.

Use openmed.extract_pii to detect and retrieve specific types of Protected Health Information (PHI) or Personally Identifiable Information (PII) spans (names, dates, MRNs, etc.) from clinical text. Unlike deidentification, this function returns the original text and the detected spans, allowing users to audit, preview redactions, normalize labels, or route spans to a custom anonymizer without altering the source data. It is ideal for pre-processing and policy enforcement.

This skill extracts crucial Social Determinants of Health (SDOH) — such as housing instability, food insecurity, and transportation barriers — from unstructured clinical notes. It maps the identified text spans to standardized ICD-10-CM Z-codes (Z55–Z65). It is essential for health-equity analytics, building risk stratification tools, and identifying coding gaps by converting narrative text into queryable, discrete data points.

A comprehensive skill for managing KubeSphere's external access gateway using ingress-nginx. It supports cluster, workspace, and project-level ingress setup, covering installation planning, status monitoring, upgrade management, and graceful uninstallation across multiple Kubernetes clusters.

This extension provides comprehensive management for external access using the standard Kubernetes Gateway API and Traefik as the underlying proxy. It supports multi-tenant scoping across cluster, workspace, and namespace levels, enabling robust, modern API gateway configuration within the KubeSphere ecosystem.

This skill maps clinical concept spans (disorders, anatomy, procedures, etc.) extracted by OpenMed to SNOMED CT concept IDs (SCTID). It performs complex, out-of-process terminology resolution using standard FHIR R4 endpoints ($lookup, $translate, ValueSet/$expand) against a user-supplied, licensed terminology server (e.g., Ontoserver, UTS). Ideal for generating rich, hierarchy-aware clinical codes, performing subsumption queries (ECL), or translating codes between clinical standards (like ICD-10 to SNOMED CT).

This skill facilitates the crucial grounding step for clinical coding by interacting with external FHIR terminology servers. It supports core operations such as `$validate-code`, `$expand` (for ValueSets), `$lookup`, and `$translate` (ConceptMaps). It ensures that raw text spans are converted into validated, coded `CodeableConcept` resources using industry standards like SNOMED CT, LOINC, and ICD-10, maintaining interoperability.

Generates highly accurate, structured summaries from complex clinical notes, such as discharge summaries, SBAR reports, or problem lists. The core function is to anchor every generated claim to a specific source span within the original text, guaranteeing verifiability and eliminating hallucination. Essential for clinical documentation drafting and chart abstraction.

A specialized tool designed to manage and deploy cloud infrastructure using Terraform commands. It activates only when explicit Terraform CLI operations (such as plan, apply, or destroy) are detected. Users must specify the target workspace path and the desired action to ensure accurate and controlled infrastructure management.

This skill guides decision-making when resources (time, context, tools) are finite and the search space is infinite. It instructs the user to first establish a clear, explicit 'good enough' aspiration level. Once set, the process involves stopping at the first option that meets this threshold, preventing costly over-optimization and wasted effort.