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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.

This skill automatically detects and extracts tabular laboratory panels from diverse sources, including scanned images, photos, and delimited files (CSV/TSV). It converts complex visual data into clean, structured rows, capturing the test name, measured value, unit, reference range, and abnormal flag. It is essential for preparing medical data for subsequent standardization using LOINC and UCUM, and for mapping into FHIR resources.

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.

This skill implements a critical CI gate to ensure the privacy and performance integrity of de-identification models. It automatically fails the build if the model's recall on held-out PHI data drops below the defined threshold, or if any critical Personally Identifiable Information (PHI) is leaked (leakage must be zero). It enforces strict privacy standards in continuous integration pipelines, blocking unsafe merges.

This tool analyzes specified source code files or modules to automatically generate comprehensive and structured test suites. It identifies testable units (functions, classes, methods), adheres to project conventions (like AAA pattern), and ensures high coverage for critical scenarios, including happy paths, boundary edge cases, and error handling. It supports popular frameworks such as Jest, Vitest, and pytest.

Synthea generates statistically realistic, fully synthetic patient records in multiple standards (FHIR R4, C-CDA, CSV). This tool is essential for developers and researchers needing safe, shareable test data for OpenMed pipelines, CI fixtures, or specialized de-identification leakage gate test sets. It ensures zero risk of exposing real Protected Health Information (PHI).

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.

A comprehensive guide for performing reverse engineering using Ghidra. This skill is essential for scenarios where commercial tools like IDA are unavailable, or when performing large-scale, automated headless analysis. It covers the entire workflow, from project setup and function decompilation to advanced headless scripting and integration with other exploit tools.

This tool monitors the health of a Git worktree by checking background verification tasks (type checking, unit testing, and build compilation). It analyzes dedicated log files to provide a non-blocking, actionable summary of whether the branch is stable, helping developers quickly identify failures before committing.

This skill generates a comprehensive, structured handoff document from the current conversation session. It captures all critical project details, including scope, relevant files with specific line numbers, key discoveries, achieved work, current status, and actionable next steps. This is essential for knowledge transfer, ensuring that work can be seamlessly resumed by another agent or team member.

This guide outlines the methodology for conducting hardware security research on embedded systems. It covers physical interface analysis (UART, JTAG, SWD), debug pad triage, secure boot evaluation, and offline firmware extraction. It emphasizes systematic, non-destructive testing protocols and responsible handling of physical devices.