feat: Phase 0 walking skeleton — monorepo, API, web, ML stub

Sets up the full Phase 0 foundation:

- pnpm workspaces + turbo build graph; native module build approval
- packages/shared-types: HTTP contracts (Tip, Auth, Integrations, User)
- services/api: Express modular monolith with better-sqlite3/drizzle
  - auth: Google OAuth2 + PKCE via openid-client v6, cookie sessions
  - integrations: Todoist OAuth2 connect/disconnect, token vault
  - recommender: RandomPolicy over Todoist tasks, feedback sink
  - user: profile, consent capture, full account deletion (GDPR)
- apps/web: Next.js 15, three pages (sign-in → connect → tip)
  - tip page: black canvas, hold-to-act gesture, action sheet
  - PWA manifest + theme
- ml/serving: FastAPI stub implementing the POST /score contract
- infra: docker-compose (core/full profiles), Dockerfiles, CI skeleton
- .env.example with all required vars documented

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

ml/serving/main.py

@@ -0,0 +1,49 @@
+"""
+oO ML Serving — Phase 0 stub.
+
+Returns a placeholder response that matches the interface the real scorer will implement.
+The recommender service calls this via RemotePolicy (not yet wired in Phase 0).
+
+Contract:
+  POST /score
+  Body: { user_id: str, candidates: [{ id: str, content: str, source: str, source_id?: str }] }
+  Response: { tip_id: str, score: float }
+"""
+
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import random
+
+app = FastAPI(title="oO ML Serving", version="0.0.0")
+
+
+class Candidate(BaseModel):
+    id: str
+    content: str
+    source: str
+    source_id: str | None = None
+
+
+class ScoreRequest(BaseModel):
+    user_id: str
+    candidates: list[Candidate]
+
+
+class ScoreResponse(BaseModel):
+    tip_id: str
+    score: float
+    policy: str
+
+
+@app.get("/health")
+def health():
+    return {"ok": True}
+
+
+@app.post("/score", response_model=ScoreResponse)
+def score(req: ScoreRequest):
+    if not req.candidates:
+        raise HTTPException(status_code=422, detail="No candidates")
+    # Stub: random uniform scoring — real model slots in here
+    chosen = random.choice(req.candidates)
+    return ScoreResponse(tip_id=chosen.id, score=1.0, policy="stub-random")

ml/serving/package.json

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+{
+  "name": "@oo/ml-serving",
+  "version": "0.0.0",
+  "private": true,
+  "scripts": {
+    "dev": "uvicorn main:app --reload --port 8000",
+    "start": "uvicorn main:app --port 8000"
+  }
+}

ml/serving/requirements.txt

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+fastapi==0.115.6
+uvicorn[standard]==0.32.1
+pydantic==2.10.4