{"slug":"aperivue-preprocess-imaging","source_name":"aperivue/preprocess-imaging","name":"Aperivue/Preprocess Imaging","description":"Design or audit the data-preparation stage of a medical-imaging model — DICOM/NIfTI intake, resampling and intensity normalisation, and the augmentation plan — so the pipeline is leakage-safe before model-scaffold builds the training repo. Emits a declarative preprocessing manifest and a deterministic data-stage leakage gate that catches the leaks a split table cannot see: a dataset-level normaliser fit on non-train data, any data-fitted transform run before the split, and the same patient's sli","version":1,"lift":{"pass_rate_delta_pts":47.83,"pass_rate_pct":78.3,"total_cases":23,"passed_cases":18,"tokens_delta_pct":50.7,"turns_delta_pct":0,"verdict":"mixed","benchmark_model":"gemini-3.6-flash","grading_method":"judged","completed_at":"2026-08-24T22:30:58.183751+00:00"},"skill_score":0.7826,"benchmark_models":[{"model":"gemini-3.6-flash","headline":true,"delta_pts":47.83,"with_pass_pct":78.3,"without_pass_pct":30.4,"tokens_delta_pct":50.7,"turns_delta_pct":0,"total_cases":23,"cases_aggregated":17,"verdict":"mixed","never_hurt":true,"completed_at":"2026-08-24T22:30:58.183751+00:00","run_id":"9fb25e96-0d32-45cc-993a-4732009e2f98","version_number":1,"is_latest_version":true,"gate":null}],"trust":{"skill_safety":"passed","safety_status":"clean","intent_verdict":"safe","content_status":"clean","indexable":true},"license":"MIT","install_count":0,"manifest_hash":"35f2714849451b338694440f57efa80aadab36b2d75e9e696b445cd3d1efc30d","raw_url":"https://app.decimal.ai/s/aperivue-preprocess-imaging/SKILL.md","scorecard_url":"https://app.decimal.ai/skills/aperivue-preprocess-imaging"}