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Diff/aledon8-openleukemia-ml-training-claude ↔ aledon8-openleukemia-cursor-rules-ai-service

Comparison

A · CLAUDE.md · Aledon8/OpenLeukemiaB · Cursor rules · Aledon8/OpenLeukemia
What each file covers, counted
DimensionSharedOnly in AOnly in BOverlap
Sections0110%
Commands0030%
Section tags0230%

What each file covers

Sections

0 shared · 1 only in A · 1 only in B
  • − ML Training Memory
  • + AI Service Rules

Commands

0 shared · 0 only in A · 3 only in B
  • + python -m mypy app
  • + python -m ruff check .
  • + python -m pytest

Section tags

0 shared · 2 only in A · 3 only in B
  • − code-style
  • − performance
  • + test
  • + lint-format
  • + do-not

Line diff

+13 added−6 removed3 unchanged18.8% identical
Aledon8/OpenLeukemia · ml/training/CLAUDE.md
@@ −1 @@
1# ML Training Memory
 
 
 
 
 
2 
3Apply this inside `ml/training/`.
4 
5- Prefer reproducible training scripts with explicit inputs, seeds, outputs, and metrics.
6- Keep patient-identifying data out of the repository.
7- Document label assumptions and cohort limitations near the training code.
8- Structured models support signals and trends, not diagnosis.
 
 
9 
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 
@@ −1 +1 @@
1−# ML Training Memory
1+---
2+description: Python FastAPI AI-service rules
3+globs:
4+ - "ai-service/**/*.py"
5+alwaysApply: false
6+---
27  
3−Apply this inside `ml/training/`.
8+# AI Service Rules
49  
5−- Prefer reproducible training scripts with explicit inputs, seeds, outputs, and metrics.
6−- Keep patient-identifying data out of the repository.
7−- Document label assumptions and cohort limitations near the training code.
8−- Structured models support signals and trends, not diagnosis.
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.
916  
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