| Dimension | Shared | Only in A | Only in B | Overlap |
|---|---|---|---|---|
| Sections | 0 | 2 | 1 | 0% |
| Commands | 0 | 0 | 0 | — |
| Section tags | 1 | 2 | 0 | 33% |
What each file covers
Sections
0 shared · 2 only in A · 1 only in B- − Supabase Instructions
- − Rules
- + ML Rules
Commands
neither file has anySection tags
1 shared · 2 only in A · 0 only in B- − code-style
- − agent-behaviour
- do-not
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 · .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−# Supabase Instructions
1+---
2+description: ML workspace rules
3+globs:
4+ - "ml/**/*"
5+alwaysApply: false
6+---
27
3−Supabase is the MVP platform layer for auth, Postgres, storage, Row Level Security, consent records, and lightweight Edge Functions.
8+# ML Rules
49
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.
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.
1315
