| name | Food Delivery |
| slug | food-delivery |
| version | 1.0.0 |
| description | Choose and order food with learned preferences, price comparison, and variety protection. |
| metadata | {"clawdbot":{"emoji":"🍕","requires":{"bins":[]},"os":["linux","darwin","win32"]}} |
When to Use
User wants their agent to handle the entire food ordering process — from deciding what to eat, through comparing options, to placing the actual order. Agent learns preferences over time and makes increasingly better choices.
Architecture
Memory lives in ~/food-delivery/. See memory-template.md for setup.
~/food-delivery/
├── memory.md # Core preferences, restrictions, defaults
├── restaurants.md # Restaurant ratings, dishes, notes
├── orders.md # Recent orders for variety tracking
└── people.md # Household/group member preferences
User creates these files. Templates in memory-template.md.
Quick Reference
| Topic | File |
|---|
| Memory setup | memory-template.md |
| Decision framework | decisions.md |
| Ordering workflow | ordering.md |
| Common traps | traps.md |
Data Storage
All data stored in ~/food-delivery/. Create on first use:
mkdir -p ~/food-delivery
Scope
This skill handles:
- Learning cuisine and taste preferences
- Storing restaurant ratings and dish notes
- Comparing prices across delivery platforms
- Finding active promotions and coupons
- Placing orders via browser automation
- Tracking recent orders for variety
- Managing household member preferences
- Coordinating group orders
User provides:
- Delivery app credentials (stored in their browser/app)
- Delivery address (configured in their apps)
- Payment methods (configured in their apps)
Self-Modification
This skill NEVER modifies its own SKILL.md.
All learned data stored in ~/food-delivery/ files.
Core Rules
1. Learn Preferences Explicitly
| User says | Store in memory.md |
|---|
| "I'm vegetarian" | restriction: vegetarian |
| "I love spicy food" | preference: spice_level=high |
| "Allergic to shellfish" | CRITICAL: shellfish (always filter) |
| "I don't like olives" | avoid: olives |
| "Budget around $20" | default_budget: $20 |
| "Usually order dinner around 7pm" | default_time: 19:00 |
2. Restriction Hierarchy
CRITICAL (allergies, medical) → ALWAYS filter, never suggest
FIRM (religious, ethical, diet) → filter unless user overrides
PREFERENCE (taste) → consider but flexible
For CRITICAL restrictions:
- Add note to EVERY order specifying the allergy
- Verify restaurant can accommodate
- Never suggest "you could try it anyway"
3. The Decision Flow
When user asks to order food:
Step 1: Context
- What time is it? (breakfast/lunch/dinner)
- What day? (weekday functional vs weekend exploratory)
- Any stated mood or occasion?
- How many people?
Step 2: Filter
- Remove anything violating CRITICAL restrictions
- Remove recently repeated (variety protection)
- Remove closed restaurants
- Apply budget constraints
Step 3: Compare
- Check same restaurant across platforms
- Find active promos/coupons
- Calculate total cost (food + delivery + fees)
Step 4: Present
- Show 2-3 options maximum
- Include reasoning for each
- Show price comparison if relevant
- Recommend one based on user history
Step 5: Confirm & Order
- Get explicit confirmation
- Place order via browser
- Confirm order placed with ETA
4. Variety Protection
Track in orders.md:
- Last 14 days of orders (restaurant + cuisine type)
Triggers:
- Same restaurant 3x in 7 days → "You've ordered from [X] a lot. Want to try something similar?"
- Same cuisine 4x in 7 days → suggest different category
- Haven't tried category user likes in 2+ weeks → suggest it
5. Price Optimization
Before ordering:
- Check restaurant on all user's delivery apps
- Compare base prices (often differ by platform)
- Check for active coupons/promos
- Factor in delivery fees and service charges
- Recommend cheapest option for same food
Tell user: "Same order is $4 cheaper on [Platform] today"
6. Group Orders
When ordering for multiple people:
- Load ~/food-delivery/people.md for known preferences
- Collect any new restrictions
- Find intersection cuisine (works for everyone)
- Suggest variety restaurants (broad menus)
- Calculate fair split if needed
Default crowd-pleasers when no consensus:
- Pizza (customizable)
- Burgers (something for everyone)
- Tacos (variety of fillings)
- Chinese (range of dishes)
- Indian (vegetarian options)
7. Context Adaptation
| Context | Behavior |
|---|
| "I'm tired" | Comfort food, familiar favorites |
| "Celebrating" | Higher-end, special occasion spots |
| "In a hurry" | Fastest delivery, simple orders |
| "Working lunch" | Quick, not messy, productive-friendly |
| "Date night" | Quality over speed, ambiance matters |
| "Hungover" | Greasy comfort, hydrating, gentle |
| "Post-workout" | Protein-heavy, healthier options |
| Rainy day | Warn about longer delivery times |
| Friday night | Can wait for quality |
| Sunday morning | Brunch options, recovery mode |
8. Proactive Suggestions
When appropriate (not spammy):
- Notify of flash sales on favorite restaurants
- Remind of unused loyalty points
- Suggest reordering past successes
- Mention new restaurants matching preferences
9. Order Execution
Via browser automation:
- Open user's preferred delivery app
- Navigate to restaurant
- Add items to cart
- Apply any coupons found
- Verify delivery address
- Confirm order total with user
- Place order
- Report confirmation and ETA
Always confirm before final checkout.
10. Problem Handling
If order has issues:
- Missing items → help file complaint
- Wrong items → help request refund
- Late delivery → track and communicate
- Quality issues → record in restaurant notes
Boundaries
Stored Locally (in ~/food-delivery/)
- Cuisine preferences and restrictions
- Restaurant ratings and dish notes
- Recent order log (variety tracking)
- Household member preferences
- Budget defaults
User Manages (in their apps)
- Delivery addresses
- Payment methods
- Account credentials
Agent Does NOT Store
- Credit card numbers
- Exact addresses
- Account passwords
- Order receipts with payment details
