| Dimension | Shared | Only in A | Only in B | Overlap |
|---|---|---|---|---|
| Sections | 0 | 2 | 1 | 0% |
| Commands | 0 | 0 | 3 | 0% |
| Section tags | 1 | 3 | 3 | 14% |
What each file covers
Sections
0 shared · 2 only in A · 1 only in B- − ML Workspace Instructions
- − Rules
- + AI Service Instructions
Commands
0 shared · 0 only in A · 3 only in B- + python -m mypy app
- + python -m ruff check .
- + python -m pytest
Section tags
1 shared · 3 only in A · 3 only in B- − code-style
- − monorepo
- − do-not
- + test
- + lint-format
- + api
- 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/ai-service.instructions.md
@@ +1 @@
1---
2applyTo: "ai-service/**/*.py"
3---
4
5# AI Service Instructions
6
7- Keep FastAPI route handlers thin; put reusable behavior in `app/domains`.
8- Use explicit Pydantic request and response models.
9- Preserve strict typing and keep `python -m mypy app` clean.
10- Use `response_model` for API route outputs.
11- Treat extraction and model outputs as explainable signals or candidate data, not clinical decisions.
12- Run `python -m ruff check .` and the relevant `python -m pytest` tests for Python changes.
13
@@ −1 +1 @@
1−# ML Workspace Instructions
1+---
2+applyTo: "ai-service/**/*.py"
3+---
24
3−This directory is for training pipelines, inference experiments, evaluations, and notebooks.
5+# AI Service 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+- Keep FastAPI route handlers thin; put reusable behavior in `app/domains`.
8+- Use explicit Pydantic request and response models.
9+- Preserve strict typing and keep `python -m mypy app` clean.
10+- Use `response_model` for API route outputs.
11+- Treat extraction and model outputs as explainable signals or candidate data, not clinical decisions.
12+- Run `python -m ruff check .` and the relevant `python -m pytest` tests for Python changes.
1313
