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dicomseriestovolume · nvidia | DecimalAI

scienceby nvidia

dicomseriestovolume

nvidia/dicom-series-to-volume

Source nvidia/skills ↗License Apache-2.0

Used for converting one CT DICOM series folder to a HU NIfTI volume with affine evidence. Not for multi-frame DICOM or clinical use.

SkillScore82Maintained

Automatic

Clinical / Healthcare pack

Last verified 8/12/2026

What it changes

Passed 15 of 18 test cases with the skill, and 5 of 18 without it.

Without skill27%

With skill82%

Checks it passed that the model alone missed

The response invokes python -m eval_engine.run_trusted.
The response rejects converting multi-frame DICOM files.
The response declines processing JPEG2000 compressed DICOM files.

and 13 more in the full report

12 fixed6 passed either way4 still failing0 broke

Pass-rate lift: +55 pp
Tokens: -16%
Agent turns: 0%
Response time: -67%

Model gemini-3.6-flashMeasured 8/12/2026v118 of 22 cases passed with skill · 18 comparable

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/nvidia-dicom-series-to-volume/SKILL.md https://app.decimal.ai/s/nvidia-dicom-series-to-volume/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add nvidia-dicom-series-to-volume, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/nvidia-dicom-series-to-volume/ and pins it in .decimal/skills.lock

decimalai skills add nvidia-dicom-series-to-volume

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/nvidia-dicom-series-to-volume/SKILL.md https://app.decimal.ai/s/nvidia-dicom-series-to-volume/SKILL.md

Raw file: /s/nvidia-dicom-series-to-volume/SKILL.md ↗ · machine-readable summary at /s/nvidia-dicom-series-to-volume.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 11

Prompt

When running the verifier command on `skills/dicom-series-to-volume`, which flag specifies the folder where verification runs are stored?

What was checked, without and with the skill
Checked	Without skill	With skill
The response identifies --out as the flag for specifying output directory.	Missed	Passed

case-21 · gemini-3.6-flash · 8/12/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md58 lines2 filesincludes scripts

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.

Track record

Benchmark

15 / 18

test cases passed, vs 5 / 18 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

nvidia / skills

skills/dicom-series-to-volume/SKILL.md

View on GitHub ↗

License Apache-2.0Imported 8/3/2026Version v1Bundled 1 file · scripts

More skills from the same source and nearby in the registry, with their headline benchmark lift.