Agent skill · software engineering · nvidia
dicom-series-to-volume
Used for converting one CT DICOM series folder to a HU NIfTI volume with affine evidence. Not for multi-frame DICOM or clinical use.
Why this skill is useful
Provides specific commands and domain knowledge for converting DICOM series to NIfTI volumes, which the AI wouldn't reliably generate on its own.
What it needs
Requires python installed locally. About 2k tokens when loaded. Last updated 2026-08-07. 2,816 stars on the source repository.
What this skill does
dicomseriestovolume Purpose Used for converting one CT DICOM series folder to a HU NIfTI volume with affine evidence. Not for multi-frame DICOM or clinical use. Use the wrapper exactly as documented; do not replace the upstream entrypoint with a handwritten implementation. Manifest I/O: inputs are dicomdir; outputs are niftivolume and resultjson. Instructions Read skillmanifest.yaml before changing arguments, side effects, or validation gates. Run scripts/seriestovolume.py through the documented command below; keep outputs under a caller-provided run directory. If a host agent exposes runscript, use runscript("scripts/seriestovolume.py", args=[...]); otherwise run the Bash/Python command shown below. Check the emitted JSON and the paired dicomvolumequalityv1 verifier before treating the run as evidence. Available Scripts Script Purpose Arguments --- --- --- scripts/seriestovolume.py Primary entrypoint declared by skillmanifest.yaml. PATHTODICOMDIR [--output OUT.nii.gz] Prerequisites Runtime requirements: Python packages listed in runtime.sideeffects.pippackages. NiBabel 5.4 or newer is required so extreme-oblique axes remain labeled consistently across reorientation. Run commands from the repository root unless an existing section below says otherwise. Limitations Single-series only; multi-series input is rejected at preflight. Multi-frame DICOM (NumberOfFrames > 1 per file) not supported. Compressed transfer syntaxes (JPEG / JPEG2000 / RLE) not supported. No voxel reorientation. The affine is derived from DICOM headers and represented in NIfTI/RAS coordinates; a downstream gate (e.g. expectedaxcodes) is expected to assert orientation before this volume is fed to a segmentation model. Not for clinical deployment, autonomous diagnosis, regulatory submission, production inference (use a vetted converter such as dcm2niix for that). Troubleshooting Error Cause Fix --- --- --- Missing dependency or import error Runtime package drift from skillmanifest.yaml. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/dicom-series-to-volume