chore(mana): citycorners + food + wardrobe aus unified-App entfernen

Citycorners-Reste vom vorherigen Sprint mit committet. food → Nutriphi,
wardrobe → Werdrobe sind als Standalone-Apps live; die mana.how-unified-
App trägt die Modul-Surfaces nicht mehr.

Gelöscht / abgebaut:
- Module: apps/mana/.../modules/{food,wardrobe} + Routen + Locales
- Landing-Apps: apps/{food,citycorners}/ Top-Level
- Backend: apps/api/src/modules/{food,wardrobe} + MCP-Tools log_meal /
  nutrition_summary, picture-routes verifyMediaOwnership-Allowlist
- shared-branding: APP_BRANDING, APP_ICONS, MANA_APPS, Logos, Onboarding
- shared-ai, mana-tool-registry, credits, shared-types/spaces,
  shared-utils/analytics, spiral-db/MANA_APP_INDEX, website-blocks
- Cross-Module: Body-CalorieWeightChart, Comic-CharacterPicker-Wardrobe,
  website-Embed wardrobe.outfits, DaySnapshot.nutrition, FoodEventType,
  MealLogged/Meal*-Streaks/Goals/Companion/Trigger, AI-Agent-Policy,
  GoalEditor MealLogged, MyDay/RitualRunner/Rules nutrition-Refs,
  Crypto-Registry meals/wardrobeGarments/wardrobeOutfits
- Generic: PlaceCategory 'food' (places + geocoding + Locales),
  spaces.ts 'food'/'wardrobe' Modul-IDs
- Infrastruktur: cloudflared, docker-compose CORS, nginx-Landing,
  prometheus-Probe, load-tests, package.json dev-Scripts,
  generate-env, mac-mini/build-landings, dependabot

Dexie v62 Migration:
- droppt meals, goals, foodFavorites, mealTags, wardrobeGarments,
  wardrobeOutfits Tabellen
- entfernt wardrobeOutfitId / wardrobeGarmentId aus images-Index
- Upgrade-Callback strippt die toten FK-Properties aus alten image-Rows

Test/Doku:
- module-registry.test.ts: Snapshot refresht auf aktuellen Stand mit
  56 Modulen (vorher 32, statisch eingefroren pre-refactor). Plus
  LEGACY_TABLES-Exclusion für nicht-mehr-registrierte Tabellen aus
  cards/citycorners/moodlit/rituals/wishes/who.
- streaks.test.ts: MealLogged-Test in TaskCompleted-Test umgebaut
- apps/mana/CLAUDE.md: food-Refs in AI-Tool-Tabelle und
  AiProposalInbox-Liste entfernt
- validate-i18n-keys.mjs + validate-no-recursive-turbo.mjs:
  existsSync-Guard, damit die Skripte mit gestaged-aber-rm'ten Dateien
  klarkommen

mana-web svelte-check 0 errors / 7436 files, betroffene Tests grün
(streaks, dashboard, module-registry), validate:pg-schema,
validate:turbo, validate:i18n-parity grün.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

apps/api/src/modules/food/routes.ts

@@ -1,220 +0,0 @@
-/**
- * Food module — Meal analysis (Gemini Vision via mana-llm) + recommendations.
- *
- * CRUD for meals, goals, favorites is handled by mana-sync. This module
- * owns the server-only operations: photo upload to mana-media, structured
- * AI analysis using the Vercel AI SDK (`generateObject`) against the
- * shared Zod schema in @mana/shared-types, and a small rule-based
- * recommendation engine.
- *
- * Why generateObject + Zod instead of raw fetch?
- *   - Runtime validation of the AI response — if Gemini drifts on a
- *     field, we throw at the boundary instead of corrupting downstream
- *     state. The frontend never sees malformed data.
- *   - Provider-portable structured outputs: the AI SDK translates one
- *     Zod schema into OpenAI strict json_schema / Anthropic tool-use /
- *     Gemini response_schema depending on which backend mana-llm routes
- *     to. We don't have to know which.
- *   - Single source of truth: the same MealAnalysisSchema is consumed
- *     by the unified web app via `z.infer<typeof MealAnalysisSchema>`,
- *     so changes here propagate end-to-end without manual sync.
- */
-
-import { Hono } from 'hono';
-import { generateObject } from 'ai';
-import { createOpenAICompatible } from '@ai-sdk/openai-compatible';
-import {
-	AI_SCHEMA_VERSION,
-	MealAnalysisSchema,
-	type AiResponseEnvelope,
-	type MealAnalysis,
-} from '@mana/shared-types';
-import { logger, type AuthVariables } from '@mana/shared-hono';
-import { MANA_LLM } from '@mana/shared-ai';
-
-const LLM_URL = process.env.MANA_LLM_URL || 'http://localhost:3025';
-// mana-llm resolves this alias to a healthy vision model (chain in
-// services/mana-llm/aliases.yaml). To swap the chain, edit the YAML
-// and SIGHUP — no service redeploy here.
-const VISION_MODEL = MANA_LLM.VISION;
-
-const llm = createOpenAICompatible({
-	name: 'mana-llm',
-	// mana-llm exposes /v1/chat/completions (see services/mana-llm/CLAUDE.md +
-	// src/main.py:125). The AI SDK's openai-compatible adapter appends
-	// /chat/completions to baseURL, so baseURL ends in /v1.
-	baseURL: `${LLM_URL}/v1`,
-	// Tell the AI SDK that mana-llm honours OpenAI-style strict
-	// json_schema response_format. Without this, generateObject() falls
-	// back to a tool-call mode that Ollama-backed models don't support
-	// reliably and the response fails to validate against the Zod schema.
-	// mana-llm's Ollama provider translates response_format → Ollama's
-	// native `format` field (services/mana-llm/src/providers/ollama.py)
-	// so this is honoured end-to-end.
-	supportsStructuredOutputs: true,
-});
-
-const ANALYSIS_PROMPT = `Du bist ein Ernährungsexperte. Analysiere die Mahlzeit und gib strukturierte Nährwertdaten zurück. Schätze realistische Portionsgrößen und Kalorien. Antworte auf Deutsch.`;
-
-/**
- * Provider hints attached to the system message. Forward-compat:
- *
- *   - anthropic.cacheControl: ephemeral system-prompt caching. NO-OP today
- *     because (a) we route to Gemini via mana-llm and (b) the prompt is
- *     ~50 tokens — well under Anthropic's 1024-token cache minimum. Becomes
- *     active automatically when mana-llm routes to Claude AND the prompt
- *     grows (e.g. once we attach per-user dietary preferences as system
- *     context, which would push us past the threshold).
- *
- * Kept here so the day we flip the backend, we don't have to revisit
- * every route to enable caching — it just starts working.
- */
-const SYSTEM_CACHE_HINT = {
-	anthropic: { cacheControl: { type: 'ephemeral' as const } },
-};
-
-/** Wrap a validated AI object in the standard wire-format envelope. */
-function envelope(data: MealAnalysis): AiResponseEnvelope<MealAnalysis> {
-	return { schemaVersion: AI_SCHEMA_VERSION, data };
-}
-
-const routes = new Hono<{ Variables: AuthVariables }>();
-
-// ─── Photo Upload (server-only: S3 storage via mana-media) ───
-
-routes.post('/photos/upload', async (c) => {
-	const userId = c.get('userId');
-	const formData = await c.req.formData();
-	const file = formData.get('file') as File | null;
-
-	if (!file) return c.json({ error: 'No file provided' }, 400);
-	if (file.size > 10 * 1024 * 1024) return c.json({ error: 'File too large (max 10MB)' }, 400);
-
-	try {
-		const { uploadImageToMedia } = await import('../../lib/media');
-		const buffer = await file.arrayBuffer();
-		const result = await uploadImageToMedia(buffer, file.name, { app: 'food', userId });
-
-		return c.json(
-			{
-				mediaId: result.id,
-				publicUrl: result.urls.original,
-				thumbnailUrl: result.urls.thumbnail || result.urls.original,
-				storagePath: result.id,
-			},
-			201
-		);
-	} catch (err) {
-		logger.error('food.upload_failed', {
-			error: err instanceof Error ? err.message : String(err),
-		});
-		return c.json({ error: 'Upload failed' }, 500);
-	}
-});
-
-// ─── Photo Analysis (Gemini Vision on uploaded URL) ──────────
-
-routes.post('/analysis/photo', async (c) => {
-	const { photoUrl } = await c.req.json();
-	if (!photoUrl) return c.json({ error: 'photoUrl required' }, 400);
-
-	try {
-		const { object } = await generateObject({
-			model: llm(VISION_MODEL),
-			schema: MealAnalysisSchema,
-			messages: [
-				{
-					role: 'system',
-					content: ANALYSIS_PROMPT,
-					providerOptions: SYSTEM_CACHE_HINT,
-				},
-				{
-					role: 'user',
-					content: [
-						{ type: 'text', text: 'Analysiere diese Mahlzeit.' },
-						{ type: 'image', image: new URL(photoUrl) },
-					],
-				},
-			],
-			temperature: 0.3,
-		});
-		return c.json(envelope(object));
-	} catch (err) {
-		logger.error('food.photo_analysis_failed', {
-			error: err instanceof Error ? err.message : String(err),
-		});
-		return c.json({ error: 'Analysis failed' }, 500);
-	}
-});
-
-// ─── Text Analysis (Gemini on a free-text meal description) ──
-
-routes.post('/analysis/text', async (c) => {
-	const { description } = await c.req.json();
-	if (!description) return c.json({ error: 'description required' }, 400);
-
-	try {
-		const { object } = await generateObject({
-			model: llm(VISION_MODEL),
-			schema: MealAnalysisSchema,
-			messages: [
-				{
-					role: 'system',
-					content: ANALYSIS_PROMPT,
-					providerOptions: SYSTEM_CACHE_HINT,
-				},
-				{
-					role: 'user',
-					content: `Analysiere diese Mahlzeit: ${description}`,
-				},
-			],
-			temperature: 0.3,
-		});
-		return c.json(envelope(object));
-	} catch (err) {
-		logger.error('food.text_analysis_failed', {
-			error: err instanceof Error ? err.message : String(err),
-		});
-		return c.json({ error: 'Analysis failed' }, 500);
-	}
-});
-
-// ─── Recommendations (server-only: rule engine) ──────────────
-
-routes.post('/recommendations/generate', async (c) => {
-	const { dailyNutrition } = await c.req.json();
-	const hints: Array<{ type: string; priority: string; message: string; nutrient?: string }> = [];
-
-	if (dailyNutrition) {
-		if (dailyNutrition.protein < 25) {
-			hints.push({
-				type: 'hint',
-				priority: 'medium',
-				message:
-					'Deine Proteinzufuhr ist niedrig. Versuche Hülsenfrüchte, Eier oder Joghurt einzubauen.',
-				nutrient: 'protein',
-			});
-		}
-		if (dailyNutrition.fiber < 10) {
-			hints.push({
-				type: 'hint',
-				priority: 'medium',
-				message: 'Mehr Ballaststoffe! Vollkornprodukte, Gemüse und Obst helfen.',
-				nutrient: 'fiber',
-			});
-		}
-		if (dailyNutrition.sugar > 50) {
-			hints.push({
-				type: 'hint',
-				priority: 'high',
-				message:
-					'Dein Zuckerkonsum ist hoch. Achte auf versteckten Zucker in Getränken und Fertigprodukten.',
-				nutrient: 'sugar',
-			});
-		}
-	}
-
-	return c.json({ recommendations: hints });
-});
-
-export { routes as foodRoutes };

apps/api/src/modules/moodlit/routes.ts

@@ -1,41 +0,0 @@
-/**
- * Moodlit module — Preset moods library
- * Ported from apps/moodlit/apps/server
- *
- * Local-first for user moods/sequences.
- * This module serves the default preset library.
- */
-
-import { Hono } from 'hono';
-
-const DEFAULT_MOODS = [
-	{ id: 'fire', name: 'Fire', colors: ['#ff6b35', '#f72585', '#ff006e'], animation: 'flicker' },
-	{ id: 'breath', name: 'Breath', colors: ['#4361ee', '#3a0ca3', '#7209b7'], animation: 'pulse' },
-	{
-		id: 'northern-lights',
-		name: 'Northern Lights',
-		colors: ['#06d6a0', '#118ab2', '#073b4c'],
-		animation: 'aurora',
-	},
-	{ id: 'thunder', name: 'Thunder', colors: ['#14213d', '#fca311', '#e5e5e5'], animation: 'flash' },
-	{
-		id: 'sunset',
-		name: 'Sunset',
-		colors: ['#ff6b6b', '#feca57', '#ff9ff3'],
-		animation: 'gradient',
-	},
-	{ id: 'ocean', name: 'Ocean', colors: ['#0077b6', '#00b4d8', '#90e0ef'], animation: 'wave' },
-	{ id: 'forest', name: 'Forest', colors: ['#2d6a4f', '#40916c', '#52b788'], animation: 'sway' },
-	{
-		id: 'lavender',
-		name: 'Lavender',
-		colors: ['#7b2cbf', '#9d4edd', '#c77dff'],
-		animation: 'pulse',
-	},
-];
-
-const routes = new Hono();
-
-routes.get('/presets', (c) => c.json(DEFAULT_MOODS));
-
-export { routes as moodlitRoutes };

apps/api/src/modules/picture/routes.ts

@@ -254,11 +254,8 @@ routes.post('/generate', async (c) => {
 // image input natively. Replicate/local fallback is a later milestone.
 
 // OpenAI gpt-image-1 / gpt-image-2 accept up to 16 reference images per
-// edit call. We clamp at 8 to cover the Wardrobe try-on workflow — one
-// face-ref + one body-ref + up to six garment photos (top/bottom/shoes/
-// outerwear + two accessories) — while keeping credit exposure and
-// upload payload size predictable. Pre-wardrobe the cap was 4; bumped
-// in docs/plans/wardrobe-module.md M1.
+// edit call. We clamp at 8 to keep credit exposure and upload payload
+// size predictable.
 const MAX_REFERENCE_IMAGES = 8;
 
 routes.post('/generate-with-reference', async (c) => {
@@ -318,18 +315,14 @@ routes.post('/generate-with-reference', async (c) => {
 	}
 
 	// Ownership check before we spend credits or burn OpenAI quota.
-	// References span three upload tags today:
-	//   - `me`       — face/body portraits from the profile module
-	//   - `wardrobe` — garment photos (M4 try-on flow)
-	//   - `comic`    — comic-specific anchor / backdrop uploads
-	//                  (slot reserved for M6+; no writer lands in
-	//                  this app today, M1 character refs come from
-	//                  me + wardrobe only).
+	// References span two upload tags today:
+	//   - `me`    — face/body portraits from the profile module
+	//   - `comic` — comic-specific anchor / backdrop uploads
 	// Anything outside these apps is treated as not-owned regardless of
 	// mana-media's own view.
 	try {
 		const { verifyMediaOwnership } = await import('../../lib/media');
-		await verifyMediaOwnership(userId, refIds, ['me', 'wardrobe', 'comic']);
+		await verifyMediaOwnership(userId, refIds, ['me', 'comic']);
 	} catch (err) {
 		const e = err as Error & { status?: number; missing?: string[] };
 		if (e.status === 404) {

apps/api/src/modules/wardrobe/routes.ts

@@ -1,55 +0,0 @@
-/**
- * Wardrobe module — server endpoints.
- *
- * Thin wrapper around mana-media for garment photo uploads. Plan:
- * docs/plans/wardrobe-module.md M1. No logic beyond tagging uploads
- * as `app='wardrobe'` so a later `GET /api/v1/media?app=wardrobe&...`
- * query can enumerate a user's garment pool without scanning every
- * media reference.
- *
- * Try-on generation does NOT live here — it reuses the Picture
- * module's POST /api/v1/picture/generate-with-reference endpoint
- * with MAX_REFERENCE_IMAGES bumped to 8 so face + body + garments
- * fit into one call.
- */
-
-import { Hono } from 'hono';
-import type { AuthVariables } from '@mana/shared-hono';
-
-const routes = new Hono<{ Variables: AuthVariables }>();
-
-// Same 10MB cap as the other photo-upload endpoints (profile me-images,
-// picture uploads). Phone-camera PNG/HEIC routinely comes in under 6MB.
-const MAX_UPLOAD_BYTES = 10 * 1024 * 1024;
-
-routes.post('/garments/upload', async (c) => {
-	const userId = c.get('userId');
-	const formData = await c.req.formData();
-	const file = formData.get('file') as File | null;
-
-	if (!file) return c.json({ error: 'No file' }, 400);
-	if (file.size > MAX_UPLOAD_BYTES) return c.json({ error: 'Max 10MB' }, 400);
-
-	try {
-		const { uploadImageToMedia } = await import('../../lib/media');
-		const buffer = await file.arrayBuffer();
-		const result = await uploadImageToMedia(buffer, file.name, {
-			app: 'wardrobe',
-			userId,
-		});
-
-		return c.json(
-			{
-				mediaId: result.id,
-				storagePath: result.id,
-				publicUrl: result.urls.original,
-				thumbnailUrl: result.urls.thumbnail,
-			},
-			201
-		);
-	} catch (_err) {
-		return c.json({ error: 'Upload failed' }, 500);
-	}
-});
-
-export { routes as wardrobeRoutes };