技能详情（站内镜像，无评论）
许可证：MIT-0
MIT-0 ·免费使用、修改和重新分发。无需归因。
版本：v1.0.0
统计：⭐ 0 · 249 · 0 current installs · 0 all-time installs
⭐ 0
安装量（当前） 0
🛡 VirusTotal ：良性 · OpenClaw ：良性
Package：1kalin/afrexai-restaurant-ops
安全扫描（ClawHub）
- VirusTotal ：良性
- OpenClaw ：良性
OpenClaw 评估
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.
目的
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).
说明范围
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 …
安装机制
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.
证书
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.
持久
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.
综合结论
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…
安装（复制给龙虾 AI）
将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。
请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「Restaurant Operations」。简介：Provide precise, data-driven restaurant operations advice based on concept, loc…。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/1kalin/afrexai-restaurant-ops/SKILL.md
（来源：yingzhi8.cn 技能库）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 |
|------|-------|-------------|
| 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 |
---
## 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/