DICOM Series Preflight

SkillAI & models

Lets your agent check a DICOM medical image folder's headers before running conversion or AI inference.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the DICOM Series Preflight skill

About this capability

Used for header-only preflight of one DICOM series folder before conversion or inference. Not for de-identification or clinical clearance.

What this skill tells your AI

The instructions your AI receives, as published by nvidia/skills in skills/dicom-series-preflight/SKILL.md and read by ahel’s review.

Purpose

  • Used for header-only preflight of one DICOM series folder before conversion or inference. Not for de-identification or clinical clearance.
  • 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 preflight_json.

Instructions

  • Read skill_manifest.yaml before changing arguments, side effects, or validation gates.
  • Run scripts/preflight_series.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/preflight_series.py", args=[...]); otherwise run the Bash/Python command shown below.
  • Check the emitted JSON and paired verifier guidance before treating the run as evidence.

Available Scripts

ScriptPurposeArguments
scripts/preflight_series.pyPrimary entrypoint declared by skill_manifest.yaml.PATH_TO_DICOM_DIR

Prerequisites

  • Runtime requirements: Python packages listed in runtime.side_effects.pip_packages.
  • 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

  • Header-only; does not decode pixel data or detect burnt-in PHI.
  • Canonical orientation gate assumes LPS-derived CT axcodes L,P,S.
  • Compressed transfer syntax and multi-frame instances are warned, not decoded.
  • Single-directory scan; does not reconcile multiple studies in one tree.
  • Not for clinical deployment, regulatory de-identification, autonomous diagnosis, production ingestion without a vetted converter.

Troubleshooting

ErrorCauseFix
Missing dependency or import errorRuntime package drift from skill_manifest.yaml.Install the packages declared in the manifest or use the documented setup command.
Empty or schema-invalid outputWrong input path, unsupported modality, or upstream failure.Re-run with a known fixture and inspect the wrapper JSON plus stderr.
Validation gate failureOutput violated a declared engineering invariant.Keep the failed evidence pack and use the gate message to repair inputs or wrapper code.

Scans a DICOM directory (one series per folder) without decoding pixels. Emits JSON with inventory, orientation axcodes, PHI flags, findings, and a preflight.verdict of pass, warn, or fail.

python scripts/preflight_series.py PATH_TO_DICOM_DIR

Pair with verifiers/dicom_preflight_quality_v1 for a trusted preflight pack:

make run-trusted SKILL=dicom_series_preflight \
  FIXTURE=skills/dicom-series-preflight/fixtures/clean_no_phi \
  OUT=runs/dicom_preflight_demo

Flagship workflow:

make run-workflow \
  WORKFLOW=examples/workflows/dicom_preflight_gate.yaml \
  WORKFLOW_INPUT=skills/dicom-series-preflight/fixtures/clean_no_phi \
  WORKFLOW_OUT=runs/dicom_preflight_gate

Not for de-identification, private-tag review, or clinical clearance.

Signals

GitHub stars
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Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
dicom-series-preflight
Source
github.com/nvidia/skills