| name | pydicom |
| description | 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. |
| source_skill_id | k-dense-ai-scientific-agent-skills-scientific-skills-pydicom-skill-md |
| category | Science, research & data analysis |
| source_mirror | ../../../../../skills/by-category/science-research-data-analysis/latest-release-community/pydicom/SKILL.md |
| benchmark_status | artifact_gated |
pydicom
Use this skill when the task matches the description above or the source path clearly applies. Start with this concise entrypoint; open ../../../../../skills/by-category/science-research-data-analysis/latest-release-community/pydicom/SKILL.md only when implementation details, commands, assets, or references are needed.
Workflow
- Confirm the task matches this skill's scope.
- Read the local source mirror if more detail is required.
- Follow repository-level
AGENTS.md; use one AI session only.
- Keep claims tied to files, commands, citations, or benchmark artifacts.
Verification
- Source mirror:
../../../../../skills/by-category/science-research-data-analysis/latest-release-community/pydicom/SKILL.md
- Source commit:
eb20fb0dcb0b1dadaa3db2737188f0755bbc4770
- Static benchmark results: see
docs/benchmark-results.md
- Runtime artifacts recorded by this entrypoint:
0
- Assigned scenarios:
skill-proof-k-dense-ai-scientific-agent-skills-scientific-skills-pydicom-skill-md, science-research-and-data-analysis-cellxgene-census, science-research-and-data-analysis-chembl, science-research-and-data-analysis-ome-ngff-samples
Do not claim this skill passed a runtime benchmark until a validated artifact exists.
