Food Delivery

Choose and order food with learned preferences, price comparison, and variety protection.

Security Scan

The skill's requirements and instructions are internally consistent with its stated purpose (choosing and placing food orders); the main practical sensitivity is that it relies on browser automation and local files and therefore will use the user's logged-in browser sessions and stored payment methods—so the user should be aware of that privilege before enabling it.

Purpose & Capability

Name/description (choose and order food) align with what the skill does: it stores preferences locally, compares platforms, and places orders via browser automation. No unrelated credentials, binaries, or installs are requested.

Instruction Scope

SKILL.md confines data read/write to ~/food-delivery/ (memory.md, restaurants.md, orders.md, people.md) and describes explicit ordering steps. This is within scope, but the ordering instructions require controlling the user's logged-in browser session and interacting with payment methods in the browser — an implicit sensitive capability. The skill correctly requires explicit user confirmation before checkout.

Install Mechanism

Instruction-only skill with no install spec or external downloads. No code is written to disk by the skill itself beyond the user-created ~/food-delivery/ files.

Credentials

The skill requests no environment variables or external credentials (proportionate). However, it depends on the agent having browser automation capability and access to the user's logged-in browser sessions and payment methods; that implicit access is the main sensitivity to consider.

Persistence & Privilege

always is false and the skill stores only its own data under ~/food-delivery/. It does not request system-wide changes or other skills' configurations and promises not to modify SKILL.md.

Assessment

This skill appears to be what it says: it will learn preferences in files under ~/food-delivery/ and use browser automation to place orders using whatever is already configured in your delivery apps and browser. Key things to consider before installing: 1) Browser access: the agent needs to drive your logged‑in browser sessions and may trigger real purchases—ensure you trust the agent's automation capabilities and that explicit confirmation before checkout is enforced. 2) Payment privacy: the skill does not ask for card credentials but will use payment methods stored in your apps; protect your browser/profile. 3) Local storage: preference and order history are stored in ~/food-delivery/ — if that directory contains sensitive info, move or protect it and periodically prune orders.md (it already recommends 14 days). 4) Test safely: try a dry run or a low-cost order to confirm the confirmation/checkout flow behaves as you expect. 5) If you don't want the agent to be able to place orders autonomously, ensure your platform or agent can restrict browser automation or require manual approval for checkout.

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:

1. Check restaurant on all user's delivery apps
2. Compare base prices (often differ by platform)
3. Check for active coupons/promos
4. Factor in delivery fees and service charges
5. Recommend cheapest option for same food

Tell user: "Same order is $4 cheaper on [Platform] today"

6. Group Orders

When ordering for multiple people:

1. Load ~/food-delivery/people.md for known preferences
2. Collect any new restrictions
3. Find intersection cuisine (works for everyone)
4. Suggest variety restaurants (broad menus)
5. 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:

1. Open user's preferred delivery app
2. Navigate to restaurant
3. Add items to cart
4. Apply any coupons found
5. Verify delivery address
6. Confirm order total with user
7. Place order
8. 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