| Dimension | Shared | Only in A | Only in B | Overlap |
|---|---|---|---|---|
| Sections | 0 | 2 | 1 | 0% |
| Commands | 0 | 0 | 0 | — |
| Section tags | 1 | 3 | 0 | 25% |
What each file covers
Sections
0 shared · 2 only in A · 1 only in B- − ML Workspace Instructions
- − Rules
- + Supabase Instructions
Commands
neither file has anySection tags
1 shared · 3 only in A · 0 only in B- − code-style
- − monorepo
- − do-not
- agent-behaviour
Line diff
Aledon8/OpenLeukemia · ml/CLAUDE.md
@@ −1 @@
1# ML Workspace Instructions
2
3This directory is for training pipelines, inference experiments, evaluations, and notebooks.
4
5## Rules
6
7- Keep structured ML workflows separate from LLM explanation workflows.
8- Prefer reproducible scripts and documented evaluation outputs over one-off notebook state.
9- Do not treat model output as diagnosis, urgent clinical judgment, or treatment recommendation.
10- Track assumptions about input data, labels, validation, and limitations close to the code or report that uses them.
11- Avoid committing patient-identifying or sensitive medical data.
12- Prefer measurable success criteria: metric, split, threshold, calibration, and known failure modes.
13
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 Workspace Instructions
1+---
2+applyTo: "supabase/**/*"
3+---
24
3−This directory is for training pipelines, inference experiments, evaluations, and notebooks.
5+# Supabase Instructions
46
5−## Rules
6−
7−- Keep structured ML workflows separate from LLM explanation workflows.
8−- Prefer reproducible scripts and documented evaluation outputs over one-off notebook state.
9−- Do not treat model output as diagnosis, urgent clinical judgment, or treatment recommendation.
10−- Track assumptions about input data, labels, validation, and limitations close to the code or report that uses them.
11−- Avoid committing patient-identifying or sensitive medical data.
12−- Prefer measurable success criteria: metric, split, threshold, calibration, and known failure modes.
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.
1312
