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Diff/aledon8-openleukemia-ml-inference-claude ↔ aledon8-openleukemia-cursor-rules-ml

Comparison

A · CLAUDE.md · Aledon8/OpenLeukemiaB · Cursor rules · Aledon8/OpenLeukemia
What each file covers, counted
DimensionSharedOnly in AOnly in BOverlap
Sections0110%
Commands000—
Section tags0110%

What each file covers

Sections

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

Commands

neither file has any

Section tags

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

Line diff

+12 added−6 removed3 unchanged20.0% 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 · .cursor/rules/ml.mdc
@@ +1 @@
1---
2description: ML workspace rules
3globs:
4 - "ml/**/*"
5alwaysApply: false
6---
7 
8# ML Rules
9 
10- Keep structured ML workflows separate from LLM explanation workflows.
11- Use measurable criteria: split, metric, threshold, calibration, and known failure modes.
12- Do not commit patient-identifying or sensitive medical data.
13- Model outputs support signals and trends, not diagnosis or treatment guidance.
14- Document label assumptions, cohort limitations, and evaluation caveats near relevant code or reports.
15 
@@ −1 +1 @@
1−# ML Inference Memory
1+---
2+description: ML workspace rules
3+globs:
4+ - "ml/**/*"
5+alwaysApply: false
6+---
27  
3−Apply this inside `ml/inference/`.
8+# ML Rules
49  
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.
10+- Keep structured ML workflows separate from LLM explanation workflows.
11+- Use measurable criteria: split, metric, threshold, calibration, and known failure modes.
12+- Do not commit patient-identifying or sensitive medical data.
13+- Model outputs support signals and trends, not diagnosis or treatment guidance.
14+- Document label assumptions, cohort limitations, and evaluation caveats near relevant code or reports.
915  
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