| Dimension | Shared | Only in A | Only in B | Overlap |
|---|---|---|---|---|
| Sections | 0 | 1 | 1 | 0% |
| Commands | 0 | 3 | 0 | 0% |
| Section tags | 1 | 2 | 0 | 33% |
What each file covers
Sections
0 shared · 1 only in A · 1 only in B- − AI Service Rules
- + ML Rules
Commands
0 shared · 3 only in A · 0 only in B- − python -m mypy app
- − python -m ruff check .
- − python -m pytest
Section tags
1 shared · 2 only in A · 0 only in B- − test
- − lint-format
- do-not
Line diff
Aledon8/OpenLeukemia · .cursor/rules/ai-service.mdc
@@ −1 @@
1---
2description: Python FastAPI AI-service rules
3globs:
4 - "ai-service/**/*.py"
5alwaysApply: false
6---
7
8# AI Service Rules
9
10- Keep FastAPI route handlers thin; put reusable behavior in `app/domains`.
11- Use explicit Pydantic request and response models.
12- Preserve strict typing and keep `python -m mypy app` clean.
13- Use `response_model` for route outputs.
14- Treat extraction and model outputs as explainable signals or candidate data, not clinical decisions.
15- Run `python -m ruff check .` and relevant `python -m pytest` tests for Python changes.
16
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 @@
11 ---
2−description: Python FastAPI AI-service rules
2+description: ML workspace rules
33 globs:
4− - "ai-service/**/*.py"
4+ - "ml/**/*"
55 alwaysApply: false
66 ---
77
8−# AI Service Rules
8+# ML Rules
99
10−- Keep FastAPI route handlers thin; put reusable behavior in `app/domains`.
11−- Use explicit Pydantic request and response models.
12−- Preserve strict typing and keep `python -m mypy app` clean.
13−- Use `response_model` for route outputs.
14−- Treat extraction and model outputs as explainable signals or candidate data, not clinical decisions.
15−- Run `python -m ruff check .` and relevant `python -m pytest` tests for Python changes.
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.
1615
