Restaurant Operations

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

Security Scan

This is an instruction-only restaurant-operations advisory skill whose requested artifacts (none) and runtime instructions align with its stated purpose; it does not request credentials, install software, or access system files.

Purpose & Capability

The name and description (restaurant operations advice using benchmarks and KPIs) match the SKILL.md and README content. All tables and frameworks are directly relevant to providing operational guidance; there are no unrelated requirements (no cloud creds, no system binaries).

Instruction Scope

The SKILL.md tells the agent to analyze user-provided restaurant information using the included frameworks and to provide numeric, data-driven guidance. It does not instruct the agent to read local files, environment variables, or other system state, nor to transmit data to external endpoints from within the skill. The README contains links to external AfrexAI resources, but the skill itself is instruction-only and does not include automation steps that would access those services.

Install Mechanism

No install specification and no code files are present. Being instruction-only means nothing is written to disk and no external packages are pulled in by the skill itself.

Credentials

The skill requires no environment variables, credentials, or config paths. There are no requests for SECRET/TOKEN/PASSWORD variables, which is proportionate to a guidance-only skill.

Persistence & Privilege

always is false and there is no install logic that modifies agent configuration or other skills. The skill can be invoked by the agent (default behavior) — this is normal for skills and not a concern here because the skill has no external hooks or elevated privileges.

Assessment

This skill is structurally coherent and low-risk as shipped: it only provides frameworks and benchmarks. Before installing, consider: (1) provenance — the package source and homepage are missing (verify the author or test on non-sensitive data); (2) data accuracy — cross-check critical benchmarks against trusted industry sources and local regulations (health code fines and labor rules vary by jurisdiction); (3) privacy — do not feed personally identifying or financial credentials (employee SSNs, payroll account numbers, bank details) into the skill output or prompts; (4) scope — the README links to external AfrexAI automation resources, but this package does not perform automation itself — if you later integrate with automation/context packs, review those components for installs, credentials, and network endpoints. If you need higher assurance, ask the publisher for source provenance or a changelog before deploying in production.

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 — Top 10 Violations

1. Improper holding temperatures — hot food <135°F, cold food >41°F
2. Inadequate handwashing — no soap, no paper towels, infrequent washing
3. Cross-contamination — raw proteins stored above ready-to-eat
4. No certified food manager — required in most jurisdictions
5. Pest evidence — droppings, nesting, live insects
6. Expired food items — no date labels on prep items
7. Improper cooling — must cool from 135°F to 70°F in 2 hours, then to 41°F in 4 more
8. Chemical storage — cleaning chemicals stored near food
9. Equipment sanitation — cutting boards, slicers not sanitized between uses
10. Employee illness policy — 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) — revenue ÷ (seats × hours open)
2. Table turn time — average minutes from seat to check close
3. Average check size — total revenue ÷ covers
4. Food cost % — COGS ÷ food revenue
5. Labor cost % — total labor ÷ total revenue
6. Prime cost % — (food cost + labor) ÷ total revenue (target: <65%)
7. Waste % — spoilage + comp + void ÷ food purchases
8. Employee turnover rate — industry avg 75%/year, top operators <50%
9. Online review score — Google/Yelp average (target: 4.3+)
10. Break-even point — fixed costs ÷ (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

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Need More?

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

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