Skill UI

A comprehensive guide for embedding OpenMed-powered tools into Electronic Health Records (EHRs) like Epic or Cerner. It covers the entire SMART on FHIR workflow, including OAuth2 PKCE authorization, granular scoping (e.g., patient/DocumentReference.rs), secure token handling, and processing clinical notes locally on-device for de-identification and Named Entity Recognition (NER).

This technique segments unstructured clinical documents (such as HPI, PMH, and A&P sections) into contextually rich, canonical chunks. This is crucial for improving the precision of downstream NLP tasks like Named Entity Recognition (NER) and de-identification. By identifying and normalizing headers and mapping them to LOINC codes, the system ensures that the source context (e.g., historical vs. active) is maintained, leading to significantly more accurate results.

Provides a hardened FastAPI endpoint for OpenMed's core functionalities: clinical Named Entity Recognition (NER), Personally Identifiable Information (PII) extraction, and de-identification. Designed for self-hosted microservices, it handles inference requests, model lifecycle management, and supports dynamic batching, ensuring secure, on-device processing of sensitive clinical data.

This skill applies consistent date shifting to clinical text, ensuring that absolute dates are obscured while critically preserving the temporal intervals between medical events. It is essential for longitudinal research or survival analysis where time-to-event data must be preserved under HIPAA Safe Harbor rules. It maintains the integrity of time differences (e.g., length of stay, visit gaps) by applying a stable, per-patient offset.

A comprehensive, structured guide for reverse engineering and analyzing managed .NET/C# binaries. This skill covers the full workflow, including initial identification (CLR check), deobfuscation (using tools like de4dot), static analysis via IL view (dnSpyEx), and dynamic debugging. It is essential for analyzing malware, red team tools, and patching compiled C# applications.

A rigorous methodology for systematic problem diagnosis and consequential decision-making. It operates in two modes: Problem Analysis (for root cause identification using IS/IS-NOT contrast) and Decision Analysis (for prioritizing options by screening MUSTs, weighting WANTs, and assessing adverse consequences). Ideal for complex engineering and business scenarios requiring structured evidence and risk assessment.

Methodology for configuring legal matter collaboration platforms: site architecture, workflow identification, dashboard design, data quality governance and user adoption for SharePoint, Teams and equivalent platforms.

An advanced, automated pipeline designed for comprehensive, deep-dive Open Source Intelligence (OSINT) gathering and reconnaissance against authorized domains. It executes the full methodology—from passive data harvesting and subdomain discovery to multi-agent content fan-out, vulnerability identification (BOLA/IDOR), and automated findings generation. The output is a single, consolidated, multi-tab deliverable (XLSX), eliminating the need for manual follow-up or superficial reporting.

This skill guides the identification and mapping of complex legal authority sources within the Swiss jurisdiction. It is crucial for AI systems handling questions related to federal, cantonal, communal, regulatory, or private law matters in Switzerland. It does not provide legal advice but instead structures the research workflow by identifying the necessary source layers, governing laws, and jurisdictional boundaries to ensure accuracy and compliance.