Skill UI

This comprehensive tool automates the process of translating non-English official documents (images) for visa applications. It handles image preprocessing (conversion and rotation), performs robust OCR using multiple frameworks, translates the extracted text into professional English while maintaining document structure, and generates a certified bilingual PDF containing both the original image and the formatted translation.

PathML is a Python toolkit for computational pathology workflows, loading 160+ slide formats, preprocessing H&E images, constructing spatial graphs, training HoVer-Net/HACTNet models, and analyzing multiplex imaging for ML-ready pathology data.

This toolset guides users through advanced digital forensic techniques using tools like Foremost and Scalpel. It demonstrates how to perform file carving—recovering specific file types (images, documents, etc.) from raw disk images or unallocated space—by analyzing file headers and footers (magic bytes). This is critical for evidence extraction when traditional file system metadata is unavailable or corrupted.

This guide demonstrates how to build comprehensive forensic super-timelines using Plaso (log2timeline) to correlate events across diverse sources. It covers the entire workflow, from processing disk images and various logs (MFT, registry, browser history) to generating standardized CSV, JSONL exports, and visualizing the results in tools like Timesketch for deep investigative analysis.

Histolab is a specialized Python library for digital pathology research, enabling robust processing of Whole Slide Images (WSI). It automates complex workflows including tissue detection, sophisticated segmentation, and flexible tile extraction from various formats (SVS, TIFF). This tool is crucial for preparing structured, high-quality datasets required for deep learning models and advanced computational pathology analysis.

This skill provides comprehensive integration with the LabArchives Electronic Lab Notebook (ELN) platform via its REST API. It allows users to programmatically manage notebooks, retrieve user data, automate entry creation, upload diverse attachments (PDF, CSV, images), perform full notebook backups, and generate detailed usage reports. Ideal for automating scientific workflows and integrating ELN data with tools like Jupyter or REDCap.

MarkItDown is a powerful tool designed to convert a wide array of file types—including PDF, DOCX, images, audio, and structured data (JSON/XML)—into clean, token-efficient Markdown format. It supports advanced features like OCR for scanned documents, transcription for audio, and AI-enhanced descriptions, making diverse content readily consumable and structured for modern Language Models and AI pipelines.

This skill provides a comprehensive Python API integration for the OMERO platform, an open-source solution for managing and analyzing large-scale microscopy datasets. Users can programmatically access images, retrieve structured data, analyze raw pixel data, manage complex annotations (ROIs), and execute batch processing workflows essential for high-content screening and advanced biological research.

PathML is a comprehensive Python framework designed for computational pathology workflows. It supports end-to-end analysis of Whole Slide Images (WSI), including loading 160+ proprietary formats, performing advanced stain normalization and tissue segmentation, constructing spatial cell graphs, and training deep learning models (e.g., HoVer-Net) for multiplex immunofluorescence and spatial proteomics. Ideal for large-scale biomedical image analysis.

A comprehensive Python library for handling DICOM (Digital Imaging and Communications in Medicine) files. Use this skill for reading, writing, and manipulating complex medical images such as CT, MRI, and X-ray. Features include metadata extraction, pixel data manipulation, image anonymization (PHI removal), multi-format conversion, and processing multi-slice volumes, making it essential for medical data analysis and PACS integration.

PyHealth is a comprehensive Python toolkit designed for building deep-learning pipelines in the clinical and healthcare domains. It supports diverse data modalities, including EHR records, physiological signals, and medical images. The framework follows a stable 5-stage pipeline (Dataset → Task → Model → Trainer → Metrics), enabling users to perform complex tasks like predicting mortality, optimizing drug recommendations, staging sleep, or cross-mapping medical codes (ICD/ATC/RxNorm) on datasets like MIMIC-III/IV and OMOP.

Utilize the idc-index Python package to access and query massive public datasets from the NCI Imaging Data Commons (IDC). This skill allows users to search, download, and visualize large-scale radiology (CT, MR, PET) and pathology (slide) images. It is essential for AI model training, medical research, and complex medical data analysis.