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Diff/aledon8-openleukemia-ml-training-claude ↔ aledon8-openleukemia-github-instructions-supabase-instructions

Comparison

A · CLAUDE.md · Aledon8/OpenLeukemiaB · Copilot instructions · Aledon8/OpenLeukemia
What each file covers, counted
DimensionSharedOnly in AOnly in BOverlap
Sections0110%
Commands000—
Section tags0210%

What each file covers

Sections

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

Commands

neither file has any

Section tags

0 shared · 2 only in A · 1 only in B
  • − code-style
  • − performance
  • + agent-behaviour

Line diff

+9 added−6 removed3 unchanged25.0% identical
Aledon8/OpenLeukemia · ml/training/CLAUDE.md
@@ −1 @@
1# ML Training Memory
 
 
2 
3Apply this inside `ml/training/`.
4 
5- Prefer reproducible training scripts with explicit inputs, seeds, outputs, and metrics.
6- Keep patient-identifying data out of the repository.
7- Document label assumptions and cohort limitations near the training code.
8- Structured models support signals and trends, not diagnosis.
 
9 
Aledon8/OpenLeukemia · .github/instructions/supabase.instructions.md
@@ +1 @@
1---
2applyTo: "supabase/**/*"
3---
4 
5# Supabase Instructions
6 
7- Use Supabase for auth, CRUD, storage, consent records, RLS, and lightweight Edge Functions.
8- Keep heavy ML, OCR, extraction, parsing, and model workflows in `ai-service`.
9- Treat RLS as a primary protection layer.
10- Pair sensitive tables with narrow, auditable policies.
11- Plan affected data flows and access paths before schema or policy changes.
12 
@@ −1 +1 @@
1−# ML Training Memory
1+---
2+applyTo: "supabase/**/*"
3+---
24  
3−Apply this inside `ml/training/`.
5+# Supabase Instructions
46  
5−- Prefer reproducible training scripts with explicit inputs, seeds, outputs, and metrics.
6−- Keep patient-identifying data out of the repository.
7−- Document label assumptions and cohort limitations near the training code.
8−- Structured models support signals and trends, not diagnosis.
7+- Use Supabase for auth, CRUD, storage, consent records, RLS, and lightweight Edge Functions.
8+- Keep heavy ML, OCR, extraction, parsing, and model workflows in `ai-service`.
9+- Treat RLS as a primary protection layer.
10+- Pair sensitive tables with narrow, auditable policies.
11+- Plan affected data flows and access paths before schema or policy changes.
912  
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