Skill UI

Query and download the NCI Imaging Data Commons public cancer imaging datasets via idc-index, filter by metadata, fetch DICOM files, visualize series, and respect licensing details for AI training or research.

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.

Pydicom is a Python toolkit for reading, writing, and manipulating DICOM medical imaging files, helping teams extract pixel data, manage metadata, anonymize records, convert to standard formats, and integrate with PACS workflows in healthcare research and radiology applications.

A comprehensive guide for creating forensically sound, bit-for-bit disk images of suspect drives using standard tools like dd and specialized forensic tools like dcfldd. It emphasizes best practices including hardware write-blocking, pre-acquisition hashing, and post-acquisition integrity verification to maintain the chain of custody for legal evidence.

A detailed guide for conducting professional disk forensics investigations, covering the entire lifecycle from evidence acquisition to artifact analysis. This includes establishing a strict chain of custody, performing bit-for-bit forensic imaging using write blockers, identifying file system structures (MFT, inodes), recovering deleted files, analyzing key artifacts (Prefetch, Amcache), and reconstructing precise timelines of activity for incident response cases.

This skill guides comprehensive digital forensics investigations on compromised endpoints. It covers evidence collection (memory acquisition, disk imaging) and deep artifact analysis, including volatile data, MFT records, and prefetch files. Use it for incident response, legal evidence collection, and determining the scope of a breach by reconstructing attacker timelines.

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.

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.

This skill provides comprehensive tools and methodologies for working with the Brain Imaging Data Structure (BIDS) standard. It is essential for organizing, validating, querying, and converting complex multi-modal neuroscience and biomedical datasets (including MRI, EEG, MEG, PET, etc.). Use this when setting up BIDS-compliant directory structures, ensuring data integrity before publication, or converting raw DICOM scans into standardized formats.