pydicom
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill pydicom --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Pydicom ## Overview Pydicom is a pure Python package for working with DICOM files, the standard format for medical imaging data. This skill provides guidance on reading, writing, and manipulating DICOM files, including working with pixel data, metadata, and various compression formats. ## When to Use This Skill Use this skill when working with: - Medical imaging files (CT, MRI, X-ray, ultrasound, PET, etc.) - DICOM datasets requiring metadata extraction or modification - Pixel data extraction and image processing from medical scans - DICOM anonymization for research or data sharing - Converting DICOM files to standard image formats - Compressed DICOM data requiring decompression - DICOM sequences and structured reports - Multi-slice volume reconstruction - PACS (Picture Archiving and Communication System) integration ## Installation Install pydicom and common dependencies: ```bash uv pip install pydicom uv pip install pillow # For image format conversion uv pip install numpy # For pixel array manipulation uv pip install matplotlib # For visualization ``` For handling compressed DICOM files, additional packages may be needed: ```bash uv pip install pylibjpeg pylibjpeg-libjpeg pyli
- Overview
- When to Use This Skill
- Installation
- Core Workflows
- Reading DICOM Files
- Working with Pixel Data
- Converting DICOM to Image Formats
- Modifying Metadata
- Anonymizing DICOM Files
- Writing DICOM Files
- Compression and Decompression
- Working with DICOM Sequences
- Processing DICOM Series
- Helper Scripts
uv pip install pydicom uv pip install pillow # For image format conversion uv pip install numpy # For pixel array manipulation uv pip install matplotlib # For visualization uv pip install pylibjpeg pylibjpeg-libjpeg pylibjpeg-openjpeg # JPEG compression uv pip install python-gdcm # Alternative compression handler python scripts/anonymize_dicom.py input.dcm output.dcm python scripts/dicom_to_image.py input.dcm output.png python scripts/dicom_to_image.py input.dcm output.jpg --format JPEG python scripts/extract_metadata.py file.dcm
What does the pydicom skill do?
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill pydicom --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.