Individual skill
imaging-data
Use when preparing a medical-imaging dataset (DICOM/NIfTI) for modelling. Profiles spacing, orientation, intensity, label integrity, foreground fraction and target volume, gates them against the plan, then plans and audits preprocessing and augmentation for leakage.
skills/imaging-data/SKILL.md
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- biostatistics Repository topic
- claude-code Repository topic
- claude-skills Repository topic
- clinical-research Repository topic
- diagnostic-accuracy Repository topic
- literature-review Repository topic
- manuscript Repository topic
- medical-ai Repository topic
- medical-research Repository topic
- meta-analysis Repository topic
- data Derived category
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SKILL.mdcore · 11,723 bytesSKILL.mdcore · 11,723 bytesSKILL.mdcore · 11,723 bytesSKILL.mdcore · 11,723 bytesSKILL.mdcore · 11,723 bytesSKILL.mdcore · 11,723 bytesSKILL.mdcore · 11,723 bytesSKILL.mdcore · 11,723 bytesSKILL.mdcore · 11,723 bytesSKILL.mdcore · 11,723 bytesSKILL.mdcore · 11,723 bytesreferences/preprocessing_guide.mddoc · 5,110 bytesscripts/check_dataset_profile.pyruns · 17,179 bytesscripts/check_dataset_profile_challenge/expected/clean.txtdoc · 357 bytesscripts/check_dataset_profile_challenge/expected/defect.txtdoc · 1,597 bytesscripts/check_dataset_profile_challenge/fixture/profile_clean.jsonasset · 2,887 bytesscripts/check_dataset_profile_challenge/fixture/profile_defect.jsonasset · 4,536 bytesscripts/check_dataset_profile_challenge/problem.mddoc · 1,404 bytesscripts/check_dataset_profile_challenge/verify.shruns · 2,463 bytesscripts/check_normalizer_domain.pyruns · 10,285 bytesscripts/check_normalizer_domain_challenge/expected/arbitrary_mismatch.txtdoc · 825 bytesscripts/check_normalizer_domain_challenge/expected/hu_clean.txtdoc · 358 bytesscripts/check_normalizer_domain_challenge/fixture/plan_ct.jsonasset · 486 bytesscripts/check_normalizer_domain_challenge/fixture/profile_arbitrary.jsonasset · 4,497 bytesscripts/check_normalizer_domain_challenge/fixture/profile_hu.jsonasset · 2,740 bytesscripts/check_normalizer_domain_challenge/problem.mddoc · 2,404 bytesscripts/check_normalizer_domain_challenge/verify.shruns · 3,075 bytesscripts/check_preprocessing_leakage.pyruns · 17,521 bytesscripts/check_preprocessing_leakage_challenge/expected/clean.txtdoc · 364 bytesscripts/check_preprocessing_leakage_challenge/expected/leak.txtdoc · 1,024 bytesscripts/check_preprocessing_leakage_challenge/fixture/manifest_clean.jsonasset · 800 bytesscripts/check_preprocessing_leakage_challenge/fixture/manifest_leak.jsonasset · 881 bytesscripts/check_preprocessing_leakage_challenge/problem.mddoc · 903 bytesscripts/check_preprocessing_leakage_challenge/verify.shruns · 2,188 bytesscripts/profile_imaging_dataset.pyruns · 7,238 bytesskill.ymlasset · 6,292 bytestests/test_dataset_profile.shruns · 9,604 bytestests/test_preprocessing_leakage.shruns · 10,301 bytes
Read excerpt
# Imaging-Data Skill
The dataset decides more of a study than the architecture does, and it decides it first. Phases 1–3
establish what the data is and what it will not support, while that is still cheap; Phases 4–7 design
and audit the preparation pipeline so it is leakage-safe before `/model-scaffold` builds the repo.
Describe-and-audit only: never modify, resample, reorient, split or write image data, never run
preprocessing on real patient data, and wire MONAI / TorchIO transforms by reference rather than
writing a new normalisation or resampling implementation.
Elsewhere: tabular/clinical variables → `/generate-codebook`, `/clean-data`; auditing the split
table, held-out metrics, calibration, subgroup results → `/model-assessment`; choosing an
architecture → `/model-selection`; building the repo → `/model-scaffold`.
## Workflow