---

name: recipe-generator description: Generate and manage AI-powered recipes for NutriProfile. Use this skill when working with recipe generation, ingredients management, cooking instructions, or the Recipes page. Handles multi-model consensus (Mistral, Llama, Mixtral) and user dietary preferences. allowed-tools: Read,Write,Edit,Grep,Glob,Bash

NutriProfile Recipe Generator Skill

You are a recipe generation expert for the NutriProfile application. This skill helps you work with AI-powered recipe generation that considers user profiles, allergies, and nutritional goals.

Context

NutriProfile uses multi-agent AI (Mistral, Llama, Mixtral) with consensus validation for recipe generation. The system considers:

• User dietary preferences (vegetarian, vegan, omnivore, etc.)
• Allergies and food restrictions
• Nutritional goals (weight loss, muscle gain, maintenance)
• Available ingredients

Architecture

Backend Files

• backend/app/agents/recipe.py - Recipe generation agent
• backend/app/models/recipe.py - Recipe, FavoriteRecipe, RecipeHistory models
• backend/app/api/v1/recipes.py - Recipe API endpoints
• backend/app/schemas/recipe.py - Pydantic schemas

Frontend Files

• frontend/src/pages/RecipesPage.tsx - Main recipes page
• frontend/src/components/recipes/RecipeGenerator.tsx - Generation form
• frontend/src/components/recipes/RecipeCard.tsx - Recipe display card
• frontend/src/services/recipesApi.ts - API service

Data Models

Recipe Model

Copyclass Recipe(Base):
    id: int
    user_id: int
    name: str
    description: str
    ingredients: List[dict]  # [{name, quantity, unit}]
    instructions: List[str]
    prep_time: int  # minutes
    cook_time: int  # minutes
    servings: int
    calories_per_serving: float
    protein_per_serving: float
    carbs_per_serving: float
    fat_per_serving: float
    difficulty: str  # easy, medium, hard
    cuisine_type: str
    tags: List[str]
    image_url: Optional[str]
    confidence_score: float
    created_at: datetime

RecipeHistory Model

Copyclass RecipeHistory(Base):
    id: int
    user_id: int
    recipe_id: int
    generated_at: datetime
    ingredients_used: List[str]
    preferences_applied: dict

API Endpoints

Recipe Generation

CopyPOST /api/v1/recipes/generate
{
  "ingredients": ["poulet", "riz", "brocoli"],
  "preferences": {
    "cuisine": "asian",
    "max_time": 30,
    "difficulty": "easy"
  }
}

Response:
{
  "id": 1,
  "name": "Bowl Asiatique au Poulet",
  "description": "...",
  "ingredients": [...],
  "instructions": [...],
  "nutrition_per_serving": {...},
  "confidence": 0.85
}

Other Endpoints

• GET /api/v1/recipes - List user's recipes
• GET /api/v1/recipes/{id} - Get specific recipe
• POST /api/v1/recipes/{id}/favorite - Add to favorites
• DELETE /api/v1/recipes/{id}/favorite - Remove from favorites
• GET /api/v1/recipes/favorites - Get favorites

Multi-Agent Consensus

Recipe Agent Flow

Copyasync def generate_recipe(self, ingredients: List[str], profile: UserProfile):
    # 1. Build context with user profile
    context = self._build_context(ingredients, profile)

    # 2. Query multiple models in parallel
    results = await asyncio.gather(
        self.query_mistral(context),
        self.query_llama(context),
        self.query_mixtral(context)
    )

    # 3. Consensus validation
    merged_recipe = self.consensus.merge_recipes(results)

    # 4. Calculate nutrition
    merged_recipe.nutrition = self.calculate_nutrition(merged_recipe.ingredients)

    return merged_recipe

Consensus Rules

• Recipe name: Best rated by coherence
• Prep/cook time: Average of all models
• Ingredients: Union with quantity averaging
• Instructions: Merge and order by step logic
• Confidence: Minimum of individual confidences

Freemium Limits

Check limits in backend/app/services/subscription.py:

Copylimits = {
    "free": {"recipe": 2},
    "premium": {"recipe": 10},
    "pro": {"recipe": -1}  # unlimited
}

Frontend Integration

React Query Hooks

Copy// Generate recipe
const generateMutation = useMutation({
  mutationFn: (data: RecipeRequest) => recipesApi.generate(data),
  onSuccess: (recipe) => {
    queryClient.invalidateQueries(['recipes'])
    toast.success(t('recipeGenerated'))
  }
})

// Fetch recipes
const { data: recipes } = useQuery({
  queryKey: ['recipes'],
  queryFn: () => recipesApi.getAll()
})

i18n Namespace

Use recipes namespace for translations:

• recipes.title - Page title
• recipes.generate - Generate button
• recipes.ingredients - Ingredients label
• recipes.instructions - Instructions label
• recipes.nutrition - Nutrition info

Best Practices

1. Respect dietary restrictions - Always filter recipes based on user allergies
2. Calculate accurate nutrition - Use per-ingredient values and sum
3. Handle missing ingredients - Suggest substitutions
4. Support multiple cuisines - French, Italian, Asian, Mediterranean, etc.
5. Cache generated recipes - Save to RecipeHistory for analytics

Example Tasks

Add New Cuisine Type

1. Update CUISINE_TYPES in recipe agent
2. Add prompt template for cuisine
3. Update frontend dropdown options
4. Add translations for all 7 languages

Improve Recipe Quality

1. Review agent prompts in recipe.py
2. Adjust consensus weights
3. Add more detailed instructions generation
4. Test with various ingredient combinations

Fix Nutrition Calculation

1. Check calculate_nutrition() in recipe agent
2. Verify ingredient quantities are parsed correctly
3. Cross-reference with nutritionReference database
4. Run backend tests: pytest tests/test_recipes.py