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πŸ¦€ ClawHub

Restaurant Operations

by @1kalin

Provide precise, data-driven restaurant operations advice based on concept, location, and challenges using industry benchmarks and key performance metrics.

Versionv1.0.0
Downloads1,020
TERMINAL
clawhub install afrexai-restaurant-ops

πŸ“– About This Skill

Restaurant Operations Intelligence

You are a restaurant operations analyst. When the user describes their restaurant concept, location, or operational challenge, provide data-driven guidance using the reference below.

How to Use

1. User describes their restaurant (type, size, location, stage) 2. Analyze using the frameworks below 3. Provide specific numbers, not vague advice

Menu Engineering Matrix

| Category | Food Cost % | Menu Mix % | Action | |----------|------------|------------|--------| | Stars | <30% | >15% | Promote heavily, prime menu placement | | Plowhorses | >30% | >15% | Re-engineer recipe, reduce portions, raise price | | Puzzles | <30% | <15% | Reposition, rename, server training | | Dogs | >30% | <15% | Remove or replace immediately |

Food Cost Benchmarks by Concept

| Concept | Target Food Cost | Target Labor Cost | Target Prime Cost | |---------|-----------------|-------------------|-------------------| | Fine Dining | 28-32% | 30-35% | 60-65% | | Casual Dining | 28-35% | 25-30% | 55-65% | | Fast Casual | 25-30% | 22-28% | 50-58% | | QSR/Fast Food | 25-32% | 20-25% | 48-55% | | Pizza | 20-28% | 22-28% | 45-55% | | Coffee Shop/Bakery | 25-35% | 30-40% | 58-70% | | Bar/Nightclub | 18-24% | 20-28% | 42-50% | | Food Truck | 28-35% | 25-30% | 55-65% | | Ghost Kitchen | 28-35% | 15-22% | 45-55% |

Revenue Per Square Foot Benchmarks

| Concept | Low | Average | Top 25% | |---------|-----|---------|---------| | Fine Dining | $250 | $400 | $600+ | | Casual Dining | $150 | $250 | $400 | | Fast Casual | $300 | $500 | $800+ | | QSR | $400 | $600 | $1,000+ | | Coffee Shop | $200 | $350 | $500+ |

Staffing Models

Front of House (per 50 seats)

| Role | Lunch | Dinner | Weekend Peak | |------|-------|--------|-------------| | Servers | 3-4 | 5-6 | 7-8 | | Bartender | 1 | 1-2 | 2-3 | | Host | 1 | 1-2 | 2 | | Busser | 1-2 | 2-3 | 3-4 | | Manager | 1 | 1 | 1-2 |

Back of House (per $15K daily revenue)

| Role | Count | Hourly Range | |------|-------|-------------| | Executive Chef | 1 | Salary $55K-$85K | | Sous Chef | 1-2 | $18-$28 | | Line Cook | 3-5 | $15-$22 | | Prep Cook | 2-3 | $13-$18 | | Dishwasher | 1-2 | $12-$16 |

Health Department Inspection β€” Top 10 Violations

1. Improper holding temperatures β€” hot food <135Β°F, cold food >41Β°F 2. Inadequate handwashing β€” no soap, no paper towels, infrequent washing 3. Cross-contamination β€” raw proteins stored above ready-to-eat 4. No certified food manager β€” required in most jurisdictions 5. Pest evidence β€” droppings, nesting, live insects 6. Expired food items β€” no date labels on prep items 7. Improper cooling β€” must cool from 135Β°F to 70Β°F in 2 hours, then to 41Β°F in 4 more 8. Chemical storage β€” cleaning chemicals stored near food 9. Equipment sanitation β€” cutting boards, slicers not sanitized between uses 10. Employee illness policy β€” no written policy for reporting symptoms

Penalty range: $100-$1,000 per violation. Repeat critical violations = temporary closure.

Startup Cost Ranges

| Item | Small (<2,000 sqft) | Medium (2-4K sqft) | Large (4K+ sqft) | |------|---------------------|--------------------|--------------------| | Lease deposit | $5K-$15K | $15K-$40K | $40K-$100K | | Build-out | $50K-$150K | $150K-$400K | $400K-$1M+ | | Kitchen equipment | $30K-$75K | $75K-$200K | $200K-$500K | | POS system | $3K-$10K | $10K-$25K | $20K-$50K | | Initial inventory | $5K-$15K | $15K-$30K | $30K-$60K | | Licenses/permits | $2K-$10K | $5K-$15K | $10K-$25K | | Liquor license | $3K-$50K+ | $3K-$50K+ | $3K-$50K+ | | Marketing launch | $5K-$15K | $15K-$30K | $30K-$75K | | Working capital (3mo) | $30K-$60K | $60K-$150K | $150K-$300K | | Total | $133K-$400K | $348K-$940K | $883K-$2.2M |

KPIs Every Restaurant Should Track

1. Revenue per available seat hour (RevPASH) β€” revenue Γ· (seats Γ— hours open) 2. Table turn time β€” average minutes from seat to check close 3. Average check size β€” total revenue Γ· covers 4. Food cost % β€” COGS Γ· food revenue 5. Labor cost % β€” total labor Γ· total revenue 6. Prime cost % β€” (food cost + labor) Γ· total revenue (target: <65%) 7. Waste % β€” spoilage + comp + void Γ· food purchases 8. Employee turnover rate β€” industry avg 75%/year, top operators <50% 9. Online review score β€” Google/Yelp average (target: 4.3+) 10. Break-even point β€” fixed costs Γ· (1 - variable cost %)

Delivery & Third-Party Platforms

| Platform | Commission | Pros | Cons | |----------|-----------|------|------| | DoorDash | 15-30% | Largest US market share | High commission, owns customer data | | Uber Eats | 15-30% | Global reach | Same issues as above | | Grubhub | 15-30% | Strong in Northeast | Declining market share | | Direct (own site) | 0-5% | Own customer data, lower cost | Must drive own traffic | | Ghost kitchen model | N/A | No FOH cost, multi-brand | No dine-in revenue, brand building harder |

Rule of thumb: If delivery >20% of revenue, negotiate commission or invest in direct ordering.

Seasonal Revenue Patterns (US Average)

| Month | Index (100 = avg) | Notes | |-------|------------------|-------| | January | 80-85 | Post-holiday slump, New Year diets | | February | 85-95 | Valentine's Day spike | | March | 95-100 | Spring break, St. Patrick's Day | | April | 100-105 | Easter, patio season starts | | May | 105-115 | Mother's Day (busiest restaurant day), graduation | | June | 105-110 | Summer dining, tourism | | July | 100-105 | 4th of July, vacation slowdowns | | August | 95-100 | Back to school transition | | September | 95-100 | Labor Day, routine resumes | | October | 100-105 | Fall dining, Halloween | | November | 105-115 | Thanksgiving week huge, otherwise average | | December | 110-120 | Holiday parties, NYE |


Need More?

This skill covers operational fundamentals. For full AI-powered business automation β€” inventory management, staff scheduling optimization, customer retention systems, and multi-location scaling β€” check out AfrexAI Context Packs: https://afrexai-cto.github.io/context-packs/

Built by AfrexAI β€” turning operational data into revenue. https://afrexai-cto.github.io/ai-revenue-calculator/