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Get Started Free →Used for extracting selected metadata from one DICOM file and flagging standard-tag PHI presence. Not for anonymization or clinical use.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 24% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 78% | 0% |
| case-06 | ✗→✓ | ▲ Improved | -28% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -23% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -49% | 0% |
dicom_path; outputs are metadata_json.skill_manifest.yaml before changing arguments, side effects, or validation gates.scripts/extract_metadata.py through the documented command below; keep outputs under a caller-provided run directory.run_script, use run_script("scripts/extract_metadata.py", args=[...]); otherwise run the Bash/Python command shown below.medagent.verifiers.dicom_metadata_quality_v1 on evidence packs before treating the run as reviewed evidence.| Script | Purpose | Arguments | |---|---|---| | scripts/extract_metadata.py | Primary entrypoint declared by skill_manifest.yaml. | PATH_TO_DICOM [--output OUT.json] |
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 file with pydicom and emits JSON on stdout.
bashpython scripts/extract_metadata.py PATH_TO_DICOM python scripts/extract_metadata.py PATH_TO_DICOM --output result.json
Output includes transfer_syntax, modality, grouped study/series/image metadata, phi_present, and phi_tags_found.
Use this as the smallest end-to-end example of a Medical AI Skills skill. Do not use it for anonymization, private-tag review, pixel PHI detection, or clinical interpretation.
For second-pass evidence review, generate a trusted run:
bashpython -m eval_engine.run_trusted skills/dicom-metadata-extract \ --fixture skills/dicom-metadata-extract/fixtures/sample_ct.dcm \ --out runs/dicom_metadata_trusted
Other measured skills in the registry, with their headline benchmark lift.