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Diff/aledon8-openleukemia-ml-inference-claude ↔ aledon8-openleukemia-github-instructions-ml-instructions

Comparison

A · CLAUDE.md · Aledon8/OpenLeukemiaB · Copilot instructions · Aledon8/OpenLeukemia
What each file covers, counted
DimensionSharedOnly in AOnly in BOverlap
Sections0110%
Commands000—
Section tags0120%

What each file covers

Sections

0 shared · 1 only in A · 1 only in B
  • − ML Inference Memory
  • + ML Instructions

Commands

neither file has any

Section tags

0 shared · 1 only in A · 2 only in B
  • − performance
  • + do-not
  • + agent-behaviour

Line diff

+9 added−6 removed3 unchanged25.0% identical
Aledon8/OpenLeukemia · ml/inference/CLAUDE.md
@@ −1 @@
1# ML Inference Memory
 
 
2 
3Apply this inside `ml/inference/`.
4 
5- Keep inference interfaces typed, deterministic, and easy to validate.
6- Return explainable scores or signals with limitations, not clinical conclusions.
7- Preserve compatibility with `ai-service/` consumers when inference code is promoted.
8- Validate feature order, units, and missing-value behavior explicitly.
 
9 
Aledon8/OpenLeukemia · .github/instructions/ml.instructions.md
@@ +1 @@
1---
2applyTo: "ml/**/*"
3---
4 
5# ML Instructions
6 
7- Keep structured ML workflows separate from LLM explanation workflows.
8- Use measurable criteria: split, metric, threshold, calibration, and known failure modes.
9- Do not commit patient-identifying or sensitive medical data.
10- Model outputs support signals and trends, not diagnosis or treatment guidance.
11- Document label assumptions, cohort limitations, and evaluation caveats near the relevant code or report.
12 
@@ −1 +1 @@
1−# ML Inference Memory
1+---
2+applyTo: "ml/**/*"
3+---
24  
3−Apply this inside `ml/inference/`.
5+# ML Instructions
46  
5−- Keep inference interfaces typed, deterministic, and easy to validate.
6−- Return explainable scores or signals with limitations, not clinical conclusions.
7−- Preserve compatibility with `ai-service/` consumers when inference code is promoted.
8−- Validate feature order, units, and missing-value behavior explicitly.
7+- Keep structured ML workflows separate from LLM explanation workflows.
8+- Use measurable criteria: split, metric, threshold, calibration, and known failure modes.
9+- Do not commit patient-identifying or sensitive medical data.
10+- Model outputs support signals and trends, not diagnosis or treatment guidance.
11+- Document label assumptions, cohort limitations, and evaluation caveats near the relevant code or report.
912  
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