Data Analysis
pydicom
89100Total Score
Core Capability
86 / 100
Functional Suitability
11 / 12
Reliability
10 / 12
Performance & Context
7 / 8
Agent Usability
14 / 16
Human Usability
7 / 8
Security
10 / 12
Maintainability
10 / 12
Agent-Specific
17 / 20
Medical Task
20 / 20 Passed
96A Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when you need to read, write, or modify DICOM format medical imaging data, extract pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymize DICOM files, process DICOM metadata and tags, convert DICOM images to other formats, handle compressed DICOM data, or work with medical imaging datasets. Suitable for tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications
4/4
92A Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when you need to read, write, or modify DICOM format medical imaging data, extract pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymize DICOM files, process DICOM metadata and tags, convert DICOM images to other formats, handle compressed DICOM data, or work with medical imaging datasets. Suitable for tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications
4/4
90A Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when you need to read, write, or modify DICOM format medical imaging data, extract pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymize DICOM files, process DICOM metadata and tags, convert DICOM images to other formats, handle compressed DICOM data, or work with medical imaging datasets. Suitable for tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications
4/4
90Packaged executable path(s): scripts/anonymize_dicom.py plus 2 additional script(s)
4/4
90End-to-end case for Scope-focused workflow aligned to: A Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when you need to read, write, or modify DICOM format medical imaging data, extract pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymize DICOM files, process DICOM metadata and tags, convert DICOM images to other formats, handle compressed DICOM data, or work with medical imaging datasets. Suitable for tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications
4/4
Veto GatesRequired pass for any deployment consideration
Skill Veto✓ All 4 gates passed
✓
Operational Stability
System remains stable across varied inputs and edge cases
PASS✓
Structural Consistency
Output structure conforms to expected skill contract format
PASS✓
Result Determinism
Equivalent inputs produce semantically equivalent outputs
PASS✓
System Security
No prompt injection, data leakage, or unsafe tool use detected
PASSResearch Veto✅ PASS — Applicable
| Dimension | Result | Detail |
|---|---|---|
| Scientific Integrity | PASS | The archived review kept this workflow anchored to supplied data fields and observable execution behavior, not fabricated results. |
| Practice Boundaries | PASS | Practice boundaries held because the package remained focused on A Python library for working with DICOM (Digital Imaging and Communications in Medicine)... rather than overclaiming what the records supported. |
| Methodological Ground | PASS | Methodological grounding was preserved through the documented inputs, transformations, and expected artifacts. |
| Code Usability | PASS | The legacy audit did not record a code-usability failure in the packaged analysis path. |
Core Capability86 / 100 — 8 Categories
Functional Suitability
Related legacy finding for pydicom: Improve stress-case output rigor. Stress and boundary scenarios show weaker consistency
11 / 12
92%
Reliability
Related legacy finding for pydicom: Improve stress-case output rigor. Stress and boundary scenarios show weaker consistency
10 / 12
83%
Performance & Context
Performance-context scoring suggests the package could handle larger or denser runs a little more gracefully.
7 / 8
88%
Agent Usability
Agent usability was strong, but the workflow could surface its entry conditions a little more directly.
14 / 16
88%
Human Usability
The archived score suggests the output contract could be a little easier for users to inspect or reuse.
7 / 8
88%
Security
Security remained strong, though the archived review still left some room for clearer execution guardrails.
10 / 12
83%
Maintainability
The archived review treated the package as maintainable, while still preserving some room for cleanup.
10 / 12
83%
Agent-Specific
The archived deduction in agent specific traces back to: Improve stress-case output rigor. Stress and boundary scenarios show weaker consistency
17 / 20
85%
Core Capability Total86 / 100
Medical TaskExecution Average: 91.6 / 100 — Assertions: 20/20 Passed
96
Canonical
A Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when you need to read, write, or modify DICOM format medical imaging data, extract pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymize DICOM files, process DICOM metadata and tags, convert DICOM images to other formats, handle compressed DICOM data, or work with medical imaging datasets. Suitable for tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications
4/4 ✓
92
Variant A
A Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when you need to read, write, or modify DICOM format medical imaging data, extract pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymize DICOM files, process DICOM metadata and tags, convert DICOM images to other formats, handle compressed DICOM data, or work with medical imaging datasets. Suitable for tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications
4/4 ✓
90
Edge
A Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when you need to read, write, or modify DICOM format medical imaging data, extract pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymize DICOM files, process DICOM metadata and tags, convert DICOM images to other formats, handle compressed DICOM data, or work with medical imaging datasets. Suitable for tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications
4/4 ✓
90
Variant B
Packaged executable path(s): scripts/anonymize_dicom.py plus 2 additional script(s)
4/4 ✓
90
Stress
End-to-end case for Scope-focused workflow aligned to: A Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when you need to read, write, or modify DICOM format medical imaging data, extract pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymize DICOM files, process DICOM metadata and tags, convert DICOM images to other formats, handle compressed DICOM data, or work with medical imaging datasets. Suitable for tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications
4/4 ✓
96
Canonical✅ Pass
A Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when you need to read, write, or modify DICOM format medical imaging data, extract pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymize DICOM files, process DICOM metadata and tags, convert DICOM images to other formats, handle compressed DICOM data, or work with medical imaging datasets. Suitable for tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications
A Python library for working with DICOM (Digital Imaging and Communications in Medicine)... remained an analysis-style extraction path whose value came from structured data capture rather than a freeform narrative response.
Basic 36/40|Specialized 60/60|Total 96/100
✅A1The pydicom output structure matches the documented deliverable
✅A2The instruction path remains actionable for the documented case
✅A3The output stays fully within the documented skill boundary
✅A4The response quality is acceptable for the documented path
Pass rate: 4 / 4
92
Variant A✅ Pass
A Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when you need to read, write, or modify DICOM format medical imaging data, extract pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymize DICOM files, process DICOM metadata and tags, convert DICOM images to other formats, handle compressed DICOM data, or work with medical imaging datasets. Suitable for tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications
The archived run treated A Python library for working with DICOM (Digital Imaging and Communications in Medicine)... as a bounded extraction workflow, keeping attention on source fields, fallback logic, and output shape.
Basic 34/40|Specialized 58/60|Total 92/100
✅A1The pydicom output structure matches the documented deliverable
✅A2The instruction path remains actionable for the documented case
✅A3The output stays fully within the documented skill boundary
✅A4The response quality is acceptable for the documented path
Pass rate: 4 / 4
90
Edge✅ Pass
A Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when you need to read, write, or modify DICOM format medical imaging data, extract pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymize DICOM files, process DICOM metadata and tags, convert DICOM images to other formats, handle compressed DICOM data, or work with medical imaging datasets. Suitable for tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications
A Python library for working with DICOM (Digital Imaging and Communications in Medicine)... remained an analysis-style extraction path whose value came from structured data capture rather than a freeform narrative response.
Basic 33/40|Specialized 57/60|Total 90/100
✅A1The pydicom output structure matches the documented deliverable
✅A2The instruction path remains actionable for the documented case
✅A3The output stays fully within the documented skill boundary
✅A4The response quality is acceptable for the documented path
Pass rate: 4 / 4
90
Variant B✅ Pass
Packaged executable path(s): scripts/anonymize_dicom.py plus 2 additional script(s)
Packaged executable path(s): scripts/anonymize_dicom.py plus 2... remained tied to the documented analysis contract even when the preserved evidence centered on instructions instead of a full rerun.
Basic 32/40|Specialized 58/60|Total 90/100
✅A1The pydicom output structure matches the documented deliverable
✅A2The instruction path remains actionable for the documented case
✅A3The output stays fully within the documented skill boundary
✅A4The response quality is acceptable for the documented path
Pass rate: 4 / 4
90
Stress✅ Pass
End-to-end case for Scope-focused workflow aligned to: A Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when you need to read, write, or modify DICOM format medical imaging data, extract pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymize DICOM files, process DICOM metadata and tags, convert DICOM images to other formats, handle compressed DICOM data, or work with medical imaging datasets. Suitable for tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications
This stress case stayed focused on extracting and normalizing evidence from the provided records instead of drifting into unsupported interpretation.
Basic 29/40|Specialized 60/60|Total 90/100
✅A1The pydicom output structure matches the documented deliverable
✅A2The instruction path remains actionable for the documented case
✅A3The output stays fully within the documented skill boundary
✅A4The response quality is acceptable for the documented path
Pass rate: 4 / 4
Medical Task Total91.6 / 100
Key Strengths
- Primary routing is Data Analysis with execution mode B
- Static quality score is 86/100 and dynamic average is 78.6/100
- Assertions and command execution outcomes are recorded per input for human review
- Execution verification summary: Script verification 3/3; adjustment=5. anonymize_dicom.py: OK; dicom_to_image.py: OK; extract_metadata.py: OK