| Dimension | Shared | Only in A | Only in B | Overlap |
|---|---|---|---|---|
| Sections | 0 | 1 | 6 | 0% |
| Commands | 0 | 0 | 12 | 0% |
| Section tags | 1 | 0 | 6 | 14% |
What each file covers
Sections
0 shared · 1 only in A · 6 only in B- − ML Rules
- + OpenLeukemia Claude Memory
- + Stack
- + Core Rules
- + Commands
- + Working Style
- + Claude Workflow Patterns
Commands
0 shared · 0 only in A · 12 only in B- + npm install
- + npm --workspace frontend run dev
- + npm run lint:frontend
- + npm run typecheck:frontend
- + npm test --workspace frontend
- + npm --workspace frontend run build
- + python -m venv .venv
- + python -m pip install -e ".[dev]"
- + python -m uvicorn app.main:app --reload
- + python -m ruff check .
- + python -m pytest
- + python -m mypy app
Section tags
1 shared · 0 only in A · 6 only in B- + setup
- + test
- + lint-format
- + code-style
- + performance
- + agent-behaviour
- do-not
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 · CLAUDE.md
@@ +1 @@
1# OpenLeukemia Claude Memory
2
3OpenLeukemia is a patient-centered platform for organizing leukemia-related medical data, tracking changes over time, extracting structured information from documents, and explaining validated model outputs in plain language.
4
5## Stack
6
7- Frontend: React 19, TypeScript, Vite, Vitest, ESLint
8- Platform: Supabase Auth, Postgres, Storage, RLS, Edge Functions
9- AI service: Python 3.12, FastAPI, Pydantic, Uvicorn
10- Package manager: npm workspaces for `frontend`; Python virtualenv for `ai-service`
11
12## Core Rules
13
14- Patient data belongs to the patient.
15- Registration is not blanket consent.
16- Consent must be separate, explicit, auditable, and revocable.
17- AI may summarize, extract, and explain; it must not diagnose, recommend treatment, or make urgent clinical judgments.
18- Treat LLM extraction as candidate data until validated or confirmed.
19- Prefer Supabase for routine product workflows; use `ai-service/` for Python, ML, OCR, extraction, parsing, and heavier compute.
20
21## Commands
22
23Frontend, from repo root:
24
25```sh
26npm install
27npm --workspace frontend run dev
28npm run lint:frontend
29npm run typecheck:frontend
30npm test --workspace frontend
31npm --workspace frontend run build
32```
33
34AI service, from `ai-service/`:
35
36```sh
37python -m venv .venv
38python -m pip install -e ".[dev]"
39python -m uvicorn app.main:app --reload
40python -m ruff check .
41python -m pytest
42python -m mypy app
43```
44
45## Working Style
46
47- Read nearby code and docs before editing.
48- Make minimal, focused changes and preserve existing architecture.
49- Do not rewrite unrelated files or add dependencies without clear need.
50- Use typed models and structured parsing instead of ad hoc string handling.
51- Keep patient-facing text calm, plain, and non-diagnostic.
52- Run the most relevant checks after edits when practical; otherwise name the skipped check.
53- Directory-specific `CLAUDE.md` files add local rules when working in those subtrees.
54
55## Claude Workflow Patterns
56
57- Optimize for the user's intended outcome, not a literal step list; find the relevant files yourself when the request is behavioral.
58- For unfamiliar code, first explain architecture, data flow, and dependencies before editing.
59- Before deleting or broadly refactoring, identify callers, downstream effects, and the files likely to change.
60- For multi-file or risky changes, plan the touched files and verification path before editing.
61- Match existing patterns by reading a nearby implementation the project already accepts.
62- When the user provides an error, log, screenshot, plan output, issue, or file, treat that artifact as primary evidence.
63- Build self-check loops into implementation work: write/run tests, build, lint, typecheck, render, compare, or otherwise verify.
64- Use measurable targets when available: coverage threshold, latency goal, bundle size, visual delta, or exact API behavior.
65- If the user corrects the same mistake or asks to preserve a convention, turn that correction into a durable `CLAUDE.md` rule.
66
@@ −1 +1 @@
1−---
2−description: ML workspace rules
3−globs:
4− - "ml/**/*"
5−alwaysApply: false
6−---
1+# OpenLeukemia Claude Memory
72
8−# ML Rules
3+OpenLeukemia is a patient-centered platform for organizing leukemia-related medical data, tracking changes over time, extracting structured information from documents, and explaining validated model outputs in plain language.
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+## Stack
6+
7+- Frontend: React 19, TypeScript, Vite, Vitest, ESLint
8+- Platform: Supabase Auth, Postgres, Storage, RLS, Edge Functions
9+- AI service: Python 3.12, FastAPI, Pydantic, Uvicorn
10+- Package manager: npm workspaces for `frontend`; Python virtualenv for `ai-service`
11+
12+## Core Rules
13+
14+- Patient data belongs to the patient.
15+- Registration is not blanket consent.
16+- Consent must be separate, explicit, auditable, and revocable.
17+- AI may summarize, extract, and explain; it must not diagnose, recommend treatment, or make urgent clinical judgments.
18+- Treat LLM extraction as candidate data until validated or confirmed.
19+- Prefer Supabase for routine product workflows; use `ai-service/` for Python, ML, OCR, extraction, parsing, and heavier compute.
20+
21+## Commands
22+
23+Frontend, from repo root:
24+
25+```sh
26+npm install
27+npm --workspace frontend run dev
28+npm run lint:frontend
29+npm run typecheck:frontend
30+npm test --workspace frontend
31+npm --workspace frontend run build
32+```
33+
34+AI service, from `ai-service/`:
35+
36+```sh
37+python -m venv .venv
38+python -m pip install -e ".[dev]"
39+python -m uvicorn app.main:app --reload
40+python -m ruff check .
41+python -m pytest
42+python -m mypy app
43+```
44+
45+## Working Style
46+
47+- Read nearby code and docs before editing.
48+- Make minimal, focused changes and preserve existing architecture.
49+- Do not rewrite unrelated files or add dependencies without clear need.
50+- Use typed models and structured parsing instead of ad hoc string handling.
51+- Keep patient-facing text calm, plain, and non-diagnostic.
52+- Run the most relevant checks after edits when practical; otherwise name the skipped check.
53+- Directory-specific `CLAUDE.md` files add local rules when working in those subtrees.
54+
55+## Claude Workflow Patterns
56+
57+- Optimize for the user's intended outcome, not a literal step list; find the relevant files yourself when the request is behavioral.
58+- For unfamiliar code, first explain architecture, data flow, and dependencies before editing.
59+- Before deleting or broadly refactoring, identify callers, downstream effects, and the files likely to change.
60+- For multi-file or risky changes, plan the touched files and verification path before editing.
61+- Match existing patterns by reading a nearby implementation the project already accepts.
62+- When the user provides an error, log, screenshot, plan output, issue, or file, treat that artifact as primary evidence.
63+- Build self-check loops into implementation work: write/run tests, build, lint, typecheck, render, compare, or otherwise verify.
64+- Use measurable targets when available: coverage threshold, latency goal, bundle size, visual delta, or exact API behavior.
65+- If the user corrects the same mistake or asks to preserve a convention, turn that correction into a durable `CLAUDE.md` rule.
1566
