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Hardware Embedded Interface Security Analysis

hardware-security

zhaoxuya520/reverse-skill

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.

Clinical Document Ingestion and Preprocessing

ingesting-clinical-documents

maziyarpanahi/openmed

This module converts diverse clinical inputs—including scanned images, faxes, CSV/TSV exports, and C-CDA XML—into a normalized, clean text format (`ExtractedDocument`). It supports on-device OCR (Tesseract/PaddleOCR) and structured redaction for tables. It is the crucial first stage for feeding de-identification and Named Entity Recognition (NER) pipelines.

Load And Cache OpenMed Biomedical Models

loading-openmed-models

maziyarpanahi/openmed

This skill manages the loading and efficient caching of OpenMed clinical/biomedical Named Entity Recognition (NER) models. Models are downloaded once from the Hugging Face Hub, enabling full offline operation afterward. It supports model reuse across multiple calls, configuring cache directories, specifying devices (CPU/GPU), and handling various model identifiers (registry keys, full HF IDs, or local paths).

Selecting On-Device PII Models

pick-a-pii-model

maziyarpanahi/openmed

Guides users to select a local, on-device Personally Identifiable Information (PII) detection model from a committed registry. It filters models based on language, required runtime environment (e.g., PyTorch, MLX), and size constraints. The process emphasizes building an offline shortlist and mandates rigorous pre-deployment recall validation and performance benchmarking to ensure secure, privacy-preserving deployment.

Running OpenMed On-Device with Local Backends

running-openmed-ondevice

maziyarpanahi/openmed

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.

Zero-Shot Biomedical Entity Extraction

running-zeroshot-ner

maziyarpanahi/openmed

Utilize zero-shot NER to extract custom entity types (e.g., Drug, Symptom, Device) from complex clinical or biomedical text without requiring any labeled training data or fine-tuning. OpenMed wraps GLiNER/GLiNER2, allowing users to define labels on the fly. The workflow involves building a model index and then running inference via CLI or Python API, making it ideal for rapid prototyping or unstable schemas.

Scaffolding SMART on FHIR for Clinical AI

scaffolding-smart-on-fhir

maziyarpanahi/openmed

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).

OpenMed REST API for Clinical NLP Services

serving-openmed-rest-api

maziyarpanahi/openmed

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.

Add Native Device Capabilities

microsoft/power-platform-skills

Entry point for adding native device capabilities to Power Apps mobile apps—camera, image and document pickers, barcode/QR scanning, secure storage, file system, sharing, haptics, PDF generation and viewing, pen capture, and background GPS tracking—through typed wrappers under src/native/.

Assign Offline Profile To Users

assign-offline-profile

microsoft/power-platform-skills

This skill binds users or teams to a Mobile Offline Profile in Dataverse to enable offline sync on devices. It resolves profile membership issues where profiles exist but aren't used. The workflow involves verifying the project, selecting users, checking existing memberships, and posting new memberships via the Dataverse API.

Build iOS Plugin Framework

build-ios-binary

microsoft/power-platform-skills

Compiles an iOS Objective-C/Swift module into a flat device-slice .framework for a .ppmplugin bundle. Mac-only Xcode build, references React-Core headers only and never embeds React or uses CocoaPods. Leaves canonical ios/ sources untouched. Run after manifest generation and before plugin assembly.

Debug Extension Failures

debug-extension

microsoft/power-platform-skills

Diagnose and fix failures in built .ppmplugin controls: crashes, silent no-ops, PCF errors, or incorrect behavior. Investigates symptoms through error codes, manifests, native modules, and dispatch to locate root causes, then applies approved surgical fixes. Use when a control breaks on device.

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