Skill UI

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.

This skill facilitates the ingestion of unstructured clinical notes and reports from FHIR R4 EHR APIs (e.g., Epic, Cerner). It handles complex tasks including paginating large search results, decoding base64 attachments (PDF, text), and extracting narrative content. The extracted text is then passed to the OpenMed pipeline for HIPAA-compliant de-identification and subsequent NLP analysis, enabling structured data insights from raw clinical records.

This skill replaces detected Protected Health Information (PHI) in clinical notes with realistic, type-matched synthetic surrogates. Unlike simple redaction, surrogates maintain the natural readability and structure of the text, ensuring the data remains parseable for downstream NLP tasks, QA, or human review. It supports consistent, reproducible fake identities and allows custom identifier generation for proprietary formats.

Automatically transforms raw CHANGELOG entries into polished, multi-format social media announcements. It generates content tailored for LinkedIn, Twitter/X, Newsletters, and Slack, supporting both English and French. Ideal for creating structured release notes, weekly summaries, and professional product announcements to boost developer visibility and user engagement.

This skill processes raw mentions of conditions and diagnoses extracted from clinical notes. It performs synonym deduplication, filters out negated or hypothetical mentions, and assigns a structured clinical status (active, resolved, or historical). The output is a clean, reconciled problem list, structured for USCDI Problem or FHIR Condition elements.

Streamlines the complex process of documenting software releases. This tool scans Git history, categorizes changes (features, fixes, performance), and generates multiple outputs—including a structured CHANGELOG, PR body, and user-friendly communication announcements—while also detecting necessary database migrations. Ideal for professional software teams preparing for production deployments.

This tool automatically generates comprehensive release notes from git commits. It analyzes version history, categorizes changes (features, fixes, etc.), and outputs three distinct formats: a technical CHANGELOG.md section, a semi-technical PR body, and a user-friendly Slack announcement. It excels at transforming complex technical jargon into accessible messaging for different audiences.

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.

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.

This skill transforms diverse raw source materials—such as articles, transcripts, and notes—into a highly structured summary suitable for a presentation or talk. It automatically detects the source type (Real-world Evidence or Conceptual Thesis), extracts the narrative arc, identifies key metrics, outlines main themes, and critically highlights potential gaps, providing a complete foundation for content creation.

This skill takes validated talk angles and titles to produce a comprehensive, professional presentation package. It generates a detailed 5-act narrative script with embedded speaker notes, a precise slide-by-slide design specification for visual implementation, and a ready-to-use AI prompt (Kimi) for automated slide generation. Ideal for transforming raw ideas into polished, actionable presentations.