Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Content Creation
Food Delivery
Choose and order food with learned preferences, price comparison, and variety protection.
skill openclawclawhub Free
Choose and order food with learned preferences, price comparison, and variety protection.
โฌ 0 downloads โ
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, decisions.md, memory-template.md, ordering.md, traps.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- ivangdavila
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
When to Use
User wants their agent to handle the entire food ordering process โ 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/ โโโ memory.md # Core preferences, restrictions, defaults โโโ restaurants.md # Restaurant ratings, dishes, notes โโโ orders.md # Recent orders for variety tracking โโโ people.md # Household/group member preferences User creates these files. Templates in memory-template.md.
Quick Reference
TopicFileMemory setupmemory-template.mdDecision frameworkdecisions.mdOrdering workflowordering.mdCommon trapstraps.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.
1. Learn Preferences Explicitly
User saysStore 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) โ ALWAYS filter, never suggest FIRM (religious, ethical, diet) โ filter unless user overrides PREFERENCE (taste) โ 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 โ "You've ordered from [X] a lot. Want to try something similar?" Same cuisine 4x in 7 days โ suggest different category Haven't tried category user likes in 2+ weeks โ 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
ContextBehavior"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 optionsRainy dayWarn about longer delivery timesFriday nightCan wait for qualitySunday morningBrunch 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 โ help file complaint Wrong items โ help request refund Late delivery โ track and communicate Quality issues โ record in restaurant notes
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
Category context
Writing, remixing, publishing, visual generation, and marketing content production.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
5 Docs
- SKILL.md
- decisions.md
- memory-template.md
- ordering.md
- traps.md