feat: add goose powered ai culinary innovation studio prompt to library

documentation/src/pages/prompt-library/data/prompts/ai-culinary-innovation-studio.json

@@ -0,0 +1,35 @@
+{
+  "id": "ai-culinary-innovation-studio",
+  "title": "AI Culinary Innovation Studio",
+  "description": "Revolutionary kitchen laboratory that creates novel recipes by fusing global cuisines, analyzes nutritional profiles, generates professional cooking techniques, and designs stunning food presentations for culinary innovation.",
+  "category": "Culinary & Nutrition",
+  "job": "culinary-innovation",
+  "example_prompt": "Create a fusion dish combining Japanese ramen with Mexican mole sauce. Include 4-6 servings, detailed nutritional analysis, step-by-step cooking instructions, and presentation suggestions. Ensure the recipe balances umami, spice, and texture.",
+  "example_result": "Culinary Innovation Complete!\n\n## 🌶️ Fusion Creation: Ramen-Mole Rhapsody\n\n### 1. Ingredient Fusion Research (Computer Controller Extension)\n\n**Global Ingredient Analysis:**\n- **Japanese Base**: Tonkotsu broth, fresh ramen noodles, nori, green onions\n- **Mexican Fusion**: Mole sauce components (chocolate, chili peppers, nuts, spices)\n- **Bridge Elements**: Sesame seeds, lime, cilantro for flavor harmony\n\n**Sourced Recipes:**\n- Authentic tonkotsu ramen from 15 Japanese sources\n- Traditional mole poblano from 8 Mexican culinary databases\n- Nutritional data from USDA and international food composition tables\n\n### 2. Nutritional Engineering (Developer Extension)\n\n**Macronutrient Balance Calculation:**\n```python\n# Nutritional optimization algorithm\ntarget_profile = {\n    'protein': 25-30g per serving,\n    'carbs': 45-55g per serving,\n    'fat': 15-20g per serving,\n    'fiber': 8-12g per serving\n}\n\ndef optimize_recipe(ingredients, servings=4):\n    total_nutrition = calculate_total_nutrition(ingredients)\n    per_serving = total_nutrition / servings\n    \n    # Adjust ratios for balance\n    if per_serving['protein'] < 25:\n        add_protein_sources()\n    if per_serving['fiber'] < 8:\n        add_fiber_elements()\n    \n    return optimized_recipe\n```\n\n**Per Serving Nutrition (4 servings):**\n- Calories: 485 kcal\n- Protein: 28g (23%)\n- Carbohydrates: 52g (43%)\n- Fat: 18g (34%)\n- Fiber: 9.2g\n- Sodium: 1,240mg\n- Key Micronutrients: Iron 4.2mg, Vitamin C 45mg, Calcium 180mg\n\n### 3. Culinary Visualization (Auto Visualiser Extension)\n\n**Generated Assets:**\n- Ingredient fusion diagram showing flavor profiles\n- Step-by-step cooking timeline visualization\n- Nutritional breakdown pie chart\n- Final dish presentation mockup\n- Flavor wheel analysis (umami, sweet, spicy, sour balance)\n\n**Presentation Design:**\n- Layered bowl composition with broth base\n- Mole-infused noodles draped elegantly\n- Garnish constellation of sesame, nori, and lime\n- Color palette: Deep mahogany broth, vibrant green accents\n\n### 4. Recipe Memory Bank (Memory Extension)\n\n**Stored Innovation Data:**\n- Complete recipe with variations indexed\n- Nutritional profiles cached for similar fusions\n- Cooking technique preferences saved\n- Flavor combinations cataloged for future reference\n\n**Recipe Evolution Tracking:**\n- Version 1.0: Initial fusion\n- Version 1.1: Nutritional optimization\n- Version 1.2: Presentation refinement\n\n## 📋 Complete Recipe: Ramen-Mole Rhapsody\n\n### Ingredients (4 servings)\n**Broth Base:**\n- 2 cups tonkotsu-style pork broth\n- 1 cup mole sauce (store-bought or homemade)\n- 2 tbsp miso paste\n- 1 tbsp sesame oil\n\n**Proteins:**\n- 12 oz fresh ramen noodles\n- 8 oz pork belly, thinly sliced\n- 4 soft-boiled eggs\n\n**Vegetables & Aromatics:**\n- 4 green onions, finely sliced\n- 2 sheets nori, cut into strips\n- 1 lime, quartered\n- Fresh cilantro, chopped\n\n**Fusion Spices:**\n- 1 tsp cumin\n- 1 tsp coriander\n- ½ tsp cinnamon\n- 2 dried ancho chiles\n\n### Cooking Instructions\n\n**Phase 1: Broth Fusion (25 minutes)**\n1. Heat tonkotsu broth to simmer\n2. Whisk in mole sauce gradually\n3. Add miso paste and sesame oil\n4. Infuse with fusion spices\n5. Simmer 20 minutes for flavor marriage\n\n**Phase 2: Protein Preparation (15 minutes)**\n1. Cook pork belly until crispy\n2. Soft-boil eggs (6 minutes)\n3. Prepare ramen noodles al dente\n\n**Phase 3: Assembly (5 minutes)**\n1. Divide noodles into bowls\n2. Pour hot fusion broth over noodles\n3. Top with pork belly and halved eggs\n4. Garnish with green onions, nori, and cilantro\n5. Serve with lime wedges\n\n### Flavor Profile Analysis\n- **Umami Depth**: Mole's chocolate + miso + pork = 9/10\n- **Spice Balance**: Ancho chiles + cumin provide gentle heat\n- **Texture Harmony**: Crispy pork, tender noodles, rich broth\n- **Cultural Fusion**: Japanese precision meets Mexican passion\n\n### Presentation Tips\n- Serve in deep bowls for broth immersion\n- Create height with egg positioning\n- Use contrasting colors for visual appeal\n- Add edible flowers for elegance\n\n## Innovation Metrics\n\n✅ **Novelty Score**: 8.5/10 (unique flavor combination)\n✅ **Nutritional Balance**: Optimized macronutrients\n✅ **Cultural Fusion**: Japanese-Mexican synthesis\n✅ **Scalability**: Adaptable for restaurant or home\n✅ **Sustainability**: Uses accessible, seasonal ingredients\n\n## Next Culinary Experiments\n\n1. **Variation Ideas**:\n   - Vegan version with mushroom broth\n   - Seafood fusion with mole-marinated shrimp\n   - Spicy level adjustments\n\n2. **Scaling Options**:\n   - Restaurant portion (increase by 3x)\n   - Meal prep version (batch cooking)\n   - Dietary adaptations (keto, gluten-free)\n\n3. **Advanced Techniques**:\n   - Molecular gastronomy elements\n   - Fermentation integration\n   - International spice substitutions\n\nWould you like me to:\n- Create another fusion recipe?\n- Analyze nutritional variations?\n- Generate a restaurant menu?\n- Design plating photography?",
+  "extensions": [
+    {
+      "name": "Computer Controller",
+      "description": "Researches global recipes, nutritional databases, and culinary techniques from international sources.",
+      "is_builtin": true,
+      "environmentVariables": []
+    },
+    {
+      "name": "Developer",
+      "description": "Performs nutritional calculations, recipe scaling algorithms, and flavor profile optimization.",
+      "is_builtin": true,
+      "environmentVariables": []
+    },
+    {
+      "name": "Auto Visualiser",
+      "description": "Creates recipe visualizations, nutritional charts, and food presentation designs.",
+      "is_builtin": true,
+      "environmentVariables": []
+    },
+    {
+      "name": "Memory",
+      "description": "Stores innovative recipes, nutritional data, and culinary experiments for future reference and iteration.",
+      "is_builtin": true,
+      "environmentVariables": []
+    }
+  ]
+}