How do I install the Dicom Series To Volume skill?

Run npx skillmds@latest add nvidia/dicom-series-to-volume in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Dicom Series To Volume skill do?

Converts a single CT DICOM series folder into a Hounsfield Unit NIfTI volume with affine metadata. It is listed under Data & Analytics, Coding & Dev Tools, Data Analysis on SkillMD.

Is Dicom Series To Volume safe to use?

SkillMD's automated safety review verdict for this skill is PASS. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: executes scripts, makes network calls, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Dicom Series To Volume?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Dicom Series To Volume free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under Apache-2.

Who published Dicom Series To Volume?

NVIDIA (@nvidia) published this skill as a verified publisher. Their other Agent Skills are listed on their SkillMD profile.

Dicom Series To Volume by nvidia — Agent Skill

Name: Dicom Series To Volume
Rating: 4.2 (5 reviews)
Author: NVIDIA

File contents

dicom_series_to_volume

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 dicom_dir; outputs are nifti_volume and result_json.

Instructions

Read skill_manifest.yaml before changing arguments, side effects, or validation gates.
Run scripts/series_to_volume.py through the documented command below; keep outputs under a caller-provided run directory.
If a host agent exposes run_script, use run_script("scripts/series_to_volume.py", args=[...]); otherwise run the Bash/Python command shown below.
Check the emitted JSON and the paired dicom_volume_quality_v1 verifier before treating the run as evidence.

Available Scripts

Script	Purpose	Arguments
`scripts/series_to_volume.py`	Primary entrypoint declared by skill_manifest.yaml.	`PATH_TO_DICOM_DIR [--output OUT.nii.gz]`

Prerequisites

Runtime requirements: Python packages listed in runtime.side_effects.pip_packages.
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. expected_axcodes) 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 `skill_manifest.yaml`.	Install the packages declared in the manifest or use the documented setup command.
Empty or schema-invalid output	Wrong input path, unsupported modality, or upstream failure.	Re-run with a known fixture and inspect the wrapper JSON plus stderr.
Validation gate failure	Output violated a declared engineering invariant.	Keep the failed evidence pack and use the gate message to repair inputs or wrapper code.

Reads one DICOM series, sorts slices by ImagePositionPatient, applies RescaleSlope and RescaleIntercept, builds an affine from orientation and spacing tags, and writes a .nii.gz plus JSON summary.

python scripts/series_to_volume.py PATH_TO_DICOM_DIR --output PATH_TO_OUT.nii.gz

For a trusted run with the paired verifier:

python -m eval_engine.run_trusted skills/dicom-series-to-volume \
  --fixture PATH_TO_DICOM_DIR \
  --out runs/dicom_series_to_volume_trusted

Key output fields: n_slices, series_instance_uid, output.path, output.shape, output.spacing, output.axcodes, output.affine, hu_range, and runtime.conversion_seconds.

Scope limits: single-series CT only; no multi-frame DICOM, compressed transfer syntax handling, RT structure sets, auto-reorientation, or clinical use.

---name: dicom-series-to-volumedescription: Converts a single CT DICOM series folder into a Hounsfield Unit NIfTI volume with affine metadata.license: Apache-2.0---# dicom_series_to_volume## 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 `dicom_dir`; outputs are `nifti_volume` and `result_json`.## Instructions- Read `skill_manifest.yaml` before changing arguments, side effects, or validation gates.- Run `scripts/series_to_volume.py` through the documented command below; keep outputs under a caller-provided run directory.- If a host agent exposes `run_script`, use `run_script("scripts/series_to_volume.py", args=[...])`; otherwise run the Bash/Python command shown below.- Check the emitted JSON and the paired `dicom_volume_quality_v1` verifier before treating the run as evidence.## Available Scripts| Script | Purpose | Arguments ||---|---|---|| `scripts/series_to_volume.py` | Primary entrypoint declared by skill_manifest.yaml. | `PATH_TO_DICOM_DIR [--output OUT.nii.gz]` |## Prerequisites- Runtime requirements: Python packages listed in `runtime.side_effects.pip_packages`.- 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. expected_axcodes) 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 `skill_manifest.yaml`. | Install the packages declared in the manifest or use the documented setup command. || Empty or schema-invalid output | Wrong input path, unsupported modality, or upstream failure. | Re-run with a known fixture and inspect the wrapper JSON plus stderr. || Validation gate failure | Output violated a declared engineering invariant. | Keep the failed evidence pack and use the gate message to repair inputs or wrapper code. |Reads one DICOM series, sorts slices by `ImagePositionPatient`, applies`RescaleSlope` and `RescaleIntercept`, builds an affine from orientation andspacing tags, and writes a `.nii.gz` plus JSON summary.```bashpython scripts/series_to_volume.py PATH_TO_DICOM_DIR --output PATH_TO_OUT.nii.gz```For a trusted run with the paired verifier:```bashpython -m eval_engine.run_trusted skills/dicom-series-to-volume \ --fixture PATH_TO_DICOM_DIR \ --out runs/dicom_series_to_volume_trusted```Key output fields: `n_slices`, `series_instance_uid`, `output.path`,`output.shape`, `output.spacing`, `output.axcodes`, `output.affine`,`hu_range`, and `runtime.conversion_seconds`.Scope limits: single-series CT only; no multi-frame DICOM, compressed transfersyntax handling, RT structure sets, auto-reorientation, or clinical use.

Frequently asked questions

Dicom Series To Volume