Skill UI

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.

This skill maps free-text laboratory and clinical observation names to standardized LOINC codes. It resolves ambiguous terms by considering the full six-axis model: Component, Property, Time, System (specimen), Scale, and Method. It integrates with FHIR terminology services to ensure accuracy, supporting US Core Laboratory Results and UCUM units.

This skill standardizes free-text medication mentions extracted by OpenMed to RxNorm RxCUIs using the public RxNav API. It resolves complex relationships between brand names, generic ingredients, strengths, and dose forms, providing a stable, canonical code. Ideal for clinical interoperability, data analytics, de-duplication, and building US Core Medication resources.

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.

This skill guides users on deploying OpenMed models completely on-device for maximum privacy and edge computing. It details running workflows using three backends: MLX (Apple Silicon), CoreML (iOS/macOS), and ONNX/WebGPU (cross-platform/browser). The process involves converting, optionally quantizing, and running models locally, ensuring PHI never leaves the host device.

This tool simulates a senior application security engineer, performing a comprehensive, multi-phase audit. It checks for exposed secrets, prompt injection vectors, dependency vulnerabilities (supporting multiple languages), and critical configuration misconfigurations. It provides a scored, prioritized remediation report, ensuring a robust security posture before deployment.

This skill generates a comprehensive, structured catalogue of all key concepts found within a talk's summary and timeline. It categorizes each concept and scores its potential for presentation (HIGH/MEDIUM/LOW) to help users identify the strongest narrative angles. The output is designed to serve as a structured inventory, guiding the subsequent positioning and content development stages.

This skill manages the complex, multi-step workflow for updating the company's threat intelligence database. It delegates the core research and content updates to the dedicated AgentSec system. The process involves reading AgentSec's source material, executing rigorous testing in an isolated workspace, synchronizing the validated security feed, and finally ensuring data integrity by validating the updates across the guide and landing mirrors.

This skill validates FHIR R4 resources and Bundles against mandatory US Core profiles (USCDI). It goes beyond mere syntactic checks to ensure that data prepared for submission to major Electronic Health Records (EHRs) like Epic or Cerner meets the required US-specific conformance standards. It detects common data gaps, checks 'must-support' elements, and converts the comprehensive validation report into a standard FHIR OperationOutcome for seamless pipeline integration.

This skill processes verbose, stream-of-consciousness voice dictations (from tools like macOS Dictation or Wispr Flow) and transforms them into highly structured, token-efficient prompts. It removes filler words, repetitions, and tangents, organizing the core intent into standard sections (Context, Objective, Constraints) suitable for advanced LLMs like Claude Code.

This skill is designed to read data records from a specified source file, performing necessary transformations and cleaning to prepare the data for downstream use. It handles the core Extract, Transform, Load (ETL) process, making it ideal for data pipelines, data validation, and preparing raw data into a structured format.

This robust methodology guides users to strip away assumed constraints, industry conventions, or historical precedents. By isolating only the fundamental, independently supported primitives (like physics, core math, or measured needs), it enables the rebuilding of the simplest, most robust solution that genuinely satisfies real-world constraints, bypassing artificial or arbitrary limitations.