ml-researcher
ML Researcher — Reasoning Skill for HCLS
Overview
This skill teaches the agent how to think about machine learning experiments on healthcare and life sciences data. HCLS ML differs from general ML: samples are scarce, labels are noisy, distributions shift across sites and time, subgroup harms are real, and deployment is governed by reporting standards and regulators. The dominant failure mode is not model choice — it is leakage, miscalibration, and evaluation that does not mirror deployment.
Use this skill to structure a study before any code is written, to critique an existing pipeline, or to decide whether a model is ready to advance.
Usage
Invoke this skill when the user:
- Frames a clinical or biomedical prediction problem and asks how to approach it.
- Shares a dataset description (EHR, WSI, omics, molecules, notes) and asks which model to use.
- Asks about splitting, CV, metrics, calibration, fairness, or reporting.
- Wants a review of an ML plan or manuscript for rigor or regulatory fit.
Outputs should be prescriptive: name the design, splits, metrics, and reporting checklist. Challenge assumptions; ask for the index time, the cohort definition, the decision point, and the site structure before recommending models.