搜索

PythonReactSEOPDF
OpenClaw

Afrexai Restaurant Ops

Restaurant Operations Intelligence

1kalin0 来源安装数☆ 0 仓库 Stars

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。

SkillHub CLI

skillhub install 1kalin/afrexai-restaurant-ops

打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent,让它先读取说明、核对依赖,并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

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 | |-...

来源信息

Source
github
Repository
openclaw/skills
Path
skills/1kalin/afrexai-restaurant-ops
Version
—
Commit
407f7507a25d883d5f5d52be906bbfa2344793cd
分类来源
rules
状态
active
可见性
public

这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。

README

Restaurant Operations Intelligence 🍽️

Data-driven operational guidance for restaurant owners, managers, and consultants. Covers every concept from food trucks to fine dining.

What's Inside

  • Menu Engineering Matrix — classify every item as Star, Plowhorse, Puzzle, or Dog
  • Food Cost Benchmarks — target percentages by concept (QSR, casual, fine dining, ghost kitchen)
  • Revenue Per Square Foot — know if your space is underperforming
  • Staffing Models — FOH and BOH headcount by volume
  • Health Inspection Prep — top 10 violations and penalty ranges
  • Startup Cost Calculator — realistic ranges by restaurant size
  • 10 KPIs — RevPASH, prime cost, table turns, waste %, and more
  • Delivery Platform Economics — commission rates and when to go direct
  • Seasonal Revenue Patterns — monthly index with planning notes

Who It's For

  • Restaurant owners planning expansion or new concepts
  • Operators benchmarking against industry standards
  • Consultants advising food service clients
  • Anyone writing a restaurant business plan

Usage

Install in your AI agent and ask questions like:

  • "What should my food cost be for a fast casual concept?"
  • "How many servers do I need for 80 seats at dinner?"
  • "What's a realistic startup budget for a 3,000 sqft casual restaurant?"
  • "Analyze my menu items using the engineering matrix"

Want Full Automation?

This skill gives you the data. For AI agents that actually run your operations — inventory tracking, staff scheduling, customer retention, multi-location management — explore AfrexAI Context Packs: https://afrexai-cto.github.io/context-packs/

AI Revenue Leak Calculator: https://afrexai-cto.github.io/ai-revenue-calculator/

Built by AfrexAI 🖤💛

来源内容

SKILL.md
# 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 |
|-...
README
# Restaurant Operations Intelligence 🍽️

Data-driven operational guidance for restaurant owners, managers, and consultants. Covers every concept from food trucks to fine dining.

## What's Inside

- **Menu Engineering Matrix** — classify every item as Star, Plowhorse, Puzzle, or Dog
- **Food Cost Benchmarks** — target percentages by concept (QSR, casual, fine dining, ghost kitchen)
- **Revenue Per Square Foot** — know if your space is underperforming
- **Staffing Models** — FOH and BOH headcount by volume
- **Health Inspection Prep** — top 10 violations and penalty ranges
- **Startup Cost Calculator** — realistic ranges by restaurant size
- **10 KPIs** — RevPASH, prime cost, table turns, waste %, and more
- **Delivery Platform Economics** — commission rates and when to go direct
- **Seasonal Revenue Patterns** — monthly index with planning notes

## Who It's For

- Restaurant owners planning expansion or new concepts
- Operators benchmarking against industry standards
- Consultants advising food service clients
- Anyone writing a restaurant business plan

## Usage

Install in your AI agent and ask questions like:
- "What should my food cost be for a fast casual concept?"
- "How many servers do I need for 80 seats at dinner?"
- "What's a realistic startup budget for a 3,000 sqft casual restaurant?"
- "Analyze my menu items using the engineering matrix"

## Want Full Automation?

This skill gives you the data. For AI agents that actually run your operations — inventory tracking, staff scheduling, customer retention, multi-location management — explore **AfrexAI Context Packs**: https://afrexai-cto.github.io/context-packs/

**AI Revenue Leak Calculator**: https://afrexai-cto.github.io/ai-revenue-calculator/

Built by [AfrexAI](https://afrexai-cto.github.io/context-packs/) 🖤💛

相关技能

OpenClaw

bambu-studio-ai

heyixuan2

Bambu Lab 3D printer control and automation. Activate when user mentions: printer status, 3D printing, slice, analyze model, generate 3D, AMS filament, print monitor, Bambu Lab, or any 3D printing task. Full pi...

OpenClaw

geo-optimization

capt-marbles

Generative Engine Optimization (GEO) for AI search visibility. Optimize content to appear in ChatGPT, Perplexity, Claude, and Google AI Overviews. Use when optimizing websites, pages, or content for LLM discove...

OpenClaw

0protocol

0isone

Agents can sign plugins, rotate credentials without losing identity, and publicly attest to behavior.

OpenClaw

0x0-messenger

eijiac24

Send and receive P2P messages using disposable numbers and PINs. No servers, no accounts. Use for human notifications, approval flows, and agent-to-agent communication.