| Dimension | Shared | Only in A | Only in B | Overlap |
|---|---|---|---|---|
| Sections | 0 | 1 | 1 | 0% |
| Commands | 0 | 0 | 0 | — |
| Section tags | 0 | 2 | 2 | 0% |
What each file covers
Sections
0 shared · 1 only in A · 1 only in B- − Supabase Migrations Memory
- + ML Instructions
Commands
neither file has anySection tags
0 shared · 2 only in A · 2 only in B- − code-style
- − performance
- + do-not
- + agent-behaviour
Line diff
Aledon8/OpenLeukemia · supabase/migrations/CLAUDE.md
@@ −1 @@
1# Supabase Migrations Memory
2
3Apply this inside `supabase/migrations/`.
4
5- Model identity, medical data, consent records, documents, and audit events deliberately.
6- Prefer explicit constraints, timestamps, and readable names.
7- Pair sensitive tables with RLS policies.
8- Do not weaken privacy or authorization behavior in a migration without a clear reason.
9- Verify migrations against expected schema shape and name any rollback or data-risk assumptions.
10
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−# Supabase Migrations Memory
1+---
2+applyTo: "ml/**/*"
3+---
24
3−Apply this inside `supabase/migrations/`.
5+# ML Instructions
46
5−- Model identity, medical data, consent records, documents, and audit events deliberately.
6−- Prefer explicit constraints, timestamps, and readable names.
7−- Pair sensitive tables with RLS policies.
8−- Do not weaken privacy or authorization behavior in a migration without a clear reason.
9−- Verify migrations against expected schema shape and name any rollback or data-risk assumptions.
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.
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