| Dimension | Shared | Only in A | Only in B | Overlap |
|---|---|---|---|---|
| Sections | 0 | 2 | 1 | 0% |
| Commands | 0 | 0 | 0 | — |
| Section tags | 2 | 1 | 0 | 67% |
What each file covers
Sections
0 shared · 2 only in A · 1 only in B- − Supabase Instructions
- − Rules
- + ML Instructions
Commands
neither file has anySection tags
2 shared · 1 only in A · 0 only in B- − code-style
- do-not
- agent-behaviour
Line diff
Aledon8/OpenLeukemia · supabase/CLAUDE.md
@@ −1 @@
1# Supabase Instructions
2
3Supabase is the MVP platform layer for auth, Postgres, storage, Row Level Security, consent records, and lightweight Edge Functions.
4
5## Rules
6
7- Treat Row Level Security as a primary protection layer.
8- Keep consent behavior explicit, auditable, and revocable.
9- Prefer Supabase for routine product workflows and `ai-service/` for Python, ML, OCR, extraction, and compute-heavy work.
10- Keep migrations and policies readable and reversible where practical.
11- Do not weaken privacy, authorization, or consent checks without an explicit product reason.
12- For schema or policy changes, plan affected data flows and access paths before editing.
13
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 Instructions
1+---
2+applyTo: "ml/**/*"
3+---
24
3−Supabase is the MVP platform layer for auth, Postgres, storage, Row Level Security, consent records, and lightweight Edge Functions.
5+# ML Instructions
46
5−## Rules
6−
7−- Treat Row Level Security as a primary protection layer.
8−- Keep consent behavior explicit, auditable, and revocable.
9−- Prefer Supabase for routine product workflows and `ai-service/` for Python, ML, OCR, extraction, and compute-heavy work.
10−- Keep migrations and policies readable and reversible where practical.
11−- Do not weaken privacy, authorization, or consent checks without an explicit product reason.
12−- For schema or policy changes, plan affected data flows and access paths before editing.
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.
1312
