Convert a single CT DICOM series into a NIfTI volume with Hounsfield units, the CT intensity scale. A JSON summary records image dimensions, spacing, orientation, and the coordinate transformation matrix for checking the conversion.
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Used for converting one CT DICOM series folder to a HU NIfTI volume with affine evidence. Not for multi-frame DICOM or clinical use.
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dicom_dir; outputs are nifti_volume and result_json.skill_manifest.yaml before changing arguments, side effects, or validation gates.scripts/series_to_volume.py through the documented command below; keep outputs under a caller-provided run directory.run_script, use run_script("scripts/series_to_volume.py", args=[...]); otherwise run the Bash/Python command shown below.dicom_volume_quality_v1 verifier before treating the run as evidence.| Script | Purpose | Arguments |
|---|---|---|
scripts/series_to_volume.py | Primary entrypoint declared by skill_manifest.yaml. | PATH_TO_DICOM_DIR [--output OUT.nii.gz] |
runtime.side_effects.pip_packages.| 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.
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Move pandas-style table processing to NVIDIA GPUs using cuDF. Choose minimal-change acceleration or explicit GPU code, handle joins and aggregations, compare results with pandas, and use dask-cuDF when data exceeds GPU memory.
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Customize NVIDIA Nemotron Voice Agent’s Generic Pipecat example for appointment scheduling, five-field patient intake, or a custom healthcare conversation. Build prompts and tool handlers without a separate backend, then validate conversation behavior, services, and voice interaction.
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Calibrate live cameras by recording their RTSP video streams through VIOS and processing the clips with AutoMagicCalib. The workflow imports settings and alignment files, checks readiness, tracks calibration progress, and retrieves results.
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Test a running AMC service with the bundled four-camera warehouse dataset. Run camera calibration from upload to completion, then check distance and image projection errors against ground-truth data to verify the installation.
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Calibrate multiple cameras from recorded MP4 files through the AutoMagicCalib REST API. The skill handles project creation, uploads, input verification, progress checks, and evaluation when reference data is supplied, with optional VGGT refinement.
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Set up AutoMagicCalib from NGC release containers using Docker Compose. The skill handles registry authentication, port and path configuration, optional VGGT model download, service startup, and readiness checks for the backend and web interface.
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