How to Build a Recipe App

<context>
App: Recipe App
Difficulty: Beginner
Estimated build time: 3–7 days

Data model:
  Recipe: id, user_id, title, description, servings, prep_time, cook_time, difficulty, cuisine, diet_tags[], cover_image, published
  Ingredient: id, recipe_id, name, quantity, unit, preparation_note
  Step: id, recipe_id, position, instruction, image_url (optional)
  MealPlan: id, user_id, week_start_date
  MealPlanEntry: id, meal_plan_id, recipe_id, day (0–6), meal_type (breakfast/lunch/dinner)

Recommended build order:
  1. Recipe card and detail page — Build a recipe card (image, title, time, difficulty, diet tags) and a detail page with ingredients list, step-by-step instructions, and serving size scaler. Hardcode 3 recipes to get the layout right.
  2. Search and filters — Add a search input that filters by title. Add filter pills for cuisine (Italian, Asian, Mexican), diet (vegan, gluten-free, keto), and time range (under 30 min, under 1 hour). Combine filters with AND logic.
  3. User accounts and favourites — Add Clerk auth. Add a favourite button on each recipe card — toggle a user_favorites join table. Add a /favourites page that shows saved recipes. Serve this as the logged-in homepage.
  4. Weekly meal planner — Build a 7-day grid (Mon–Sun). Allow dragging or clicking a + button to assign a recipe to a meal slot. Save the plan per week to the meal_plan_entries table.
  5. Grocery list generation — From the meal plan, collect all ingredient rows from all planned recipes. Group by ingredient name (merge quantities with the same unit). Generate a consolidated list sorted by aisle category. Add a check-off UI.
  6. Serving size scaler — Multiply all ingredient quantities by (desired servings / recipe servings). Update the ingredient list in real time as the user adjusts the serving count with a +/- control. Format quantities cleanly (1.5 tsp not 1.499999 tsp).
  7. AI recipe generation — Add a "Generate a recipe from what I have" feature. User enters ingredients (tomatoes, chicken, garlic). POST to /api/generate-recipe, call Claude with a prompt to return a full recipe as structured JSON. Parse and display it.

Known pitfalls to avoid:
  - Storing ingredient quantities as strings — "1 cup" and "250ml" are the same thing but string comparison won't merge them. Store quantity as a float and unit separately. Use a unit normalisation table for conversions.
  - Fetching full recipes to build a search index — for fast search, store a denormalised search_text field on the recipe (title + tags + ingredients joined). Run ILIKE on that field instead of JOINing the ingredients table on every search.
  - Rounding ingredient quantities badly — 1/3 cup × 3 = 0.999... cups which displays as 0.99. Use Fraction.js to handle rational arithmetic for quantities, or round to the nearest common fraction (1/4, 1/3, 1/2, 2/3, 3/4).

Tech stack (intermediate): Next.js + Supabase + Spoonacular API + Clerk — accounts with saved recipes and meal plans
</context>

<role>
You are a Senior Full-Stack Engineer and product architect who has shipped production Recipe Apps before. You know exactly where developers get stuck and how to structure the project to avoid rewrites.
</role>

<task id="step-1-clarify">
Before writing any code or spec, ask me 3–5 clarifying questions that will meaningfully change the architecture. Focus on: scale expectations, auth requirements, platform (web / mobile / both), must-have vs nice-to-have features for the MVP, and any hard constraints (budget, deadline, existing infrastructure).

<example>
BAD: "What tech stack do you want to use?" — too broad, doesn't change architecture decisions.
GOOD: "Do you need real-time sync across devices, or is single-device with periodic refresh acceptable? This decides whether we use WebSockets or simple REST polling and significantly affects infrastructure complexity."
</example>
</task>

<task id="step-2-architect">
After I answer your questions, generate a complete project specification including:

1. Final tech stack with version numbers
2. Full file and folder structure
3. Complete database schema — every table, column, type, index, and foreign key
4. All API routes with request/response shapes and auth requirements
5. Component tree with TypeScript props interfaces
6. Auth and authorisation flow (who can do what)
7. Step-by-step implementation plan in the exact build order from <context> above
8. All environment variables with descriptions

<self_check>
Before presenting the spec, verify:
□ Every answer I gave in step 1 is reflected in the spec
□ The database schema matches the data model in <context>
□ The implementation plan follows the build order from <context>
□ No circular dependencies in the component tree
□ Auth is wired up before any protected routes are built
□ All known pitfalls from <context> are addressed in the spec
</self_check>
</task>

<task id="step-2.5-agents-md">
Generate an AGENTS.md file at the project root. This file is read automatically by Claude Code, Cursor, Windsurf, Sourcegraph Cody, and all major AI coding tools at the start of every session.

Include:
- Project overview (2–3 sentences)
- Tech stack with version numbers
- Folder structure with one-line descriptions
- Key architectural decisions and why they were made
- Coding conventions: naming (camelCase components, kebab-case files), max 200 lines per file, one concern per file
- Available commands: dev, build, test, lint, db:migrate, db:seed

Note in the file: "Cursor users can symlink .cursorrules → AGENTS.md. Claude Code users can symlink CLAUDE.md → AGENTS.md."
</task>

<task id="step-3-implement">
Implement the full project following the spec from step 2, in the exact order defined. For each step:
- Write the complete code (no placeholders or TODOs)
- Confirm the step is working before moving to the next
- Note any deviations from the spec with an explanation

<constraints>
- Max 200 lines per file. WHY: every file must fit in one AI context window for easy review and editing.
- One concern per file. WHY: mixing auth logic into API routes makes it impossible to reuse — extract to lib/auth.ts.
- TypeScript strict mode, no `any` types. WHY: catches data shape mismatches at compile time, not in production at 2am.
- All database queries in server components or API routes only. WHY: keeps credentials server-side, prevents accidental client-side exposure.
- All environment variables documented in .env.example. WHY: the next developer (or your future self) should be able to set up the project in under 5 minutes.
- Comment every non-obvious decision. WHY: AI tools read your comments to understand intent — without them, the next edit will break the pattern you established.
</constraints>
</task>