| Dimension | Shared | Only in A | Only in B | Overlap |
|---|---|---|---|---|
| Sections | 0 | 2 | 1 | 0% |
| Commands | 0 | 0 | 0 | — |
| Section tags | 0 | 3 | 1 | 0% |
What each file covers
Sections
0 shared · 2 only in A · 1 only in B- − Supabase Instructions
- − Rules
- + ML Inference Memory
Commands
neither file has anySection tags
0 shared · 3 only in A · 1 only in B- − code-style
- − do-not
- − agent-behaviour
- + performance
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 · 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
@@ −1 +1 @@
1−# Supabase Instructions
1+# ML Inference Memory
22
3−Supabase is the MVP platform layer for auth, Postgres, storage, Row Level Security, consent records, and lightweight Edge Functions.
3+Apply this inside `ml/inference/`.
44
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.
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.
139
