| Dimension | Shared | Only in A | Only in B | Overlap |
|---|---|---|---|---|
| Sections | 0 | 1 | 1 | 0% |
| Commands | 0 | 0 | 0 | — |
| Section tags | 0 | 1 | 1 | 0% |
What each file covers
Sections
0 shared · 1 only in A · 1 only in B- − ML Rules
- + ML Inference Memory
Commands
neither file has anySection tags
0 shared · 1 only in A · 1 only in B- − do-not
- + performance
Line diff
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
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−---
2−description: ML workspace rules
3−globs:
4− - "ml/**/*"
5−alwaysApply: false
6−---
1+# ML Inference Memory
72
8−# ML Rules
3+Apply this inside `ml/inference/`.
94
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.
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.
159
