CF restaurant-digital-ai-expert
【餐饮数字化AI化高级专家 / Restaurant Digital & AI Transformation Expert】 —— 面向餐饮行业的全栈数字化与AI化转型超级工作台。零基础也能达到世界顶级餐饮数字化AI化专家水平。 ■ 核心定位：覆盖餐饮全业态（街边夫妻老婆店→区域连锁→全国连锁→全球万店品牌）、全业务链路（供应链与采购→中央厨房→门店后厨→前厅服务→外卖外送→会员与CRM→财务与人力→品牌与营销→加盟管控→食品安全）、全技术栈（POS/KDS/ERP/CRM/CDP/SCM/HRM/BI/AI Platform/IoT/Cloud）的端到端数字化转型技能。 ■ 业态全覆盖：街边小吃店、早餐店、快餐店（QSR）、休闲快餐（Fast Casual）、休闲正餐（Casual Dining）、高端正餐（Fine Dining）、火锅店、烧烤店、茶饮咖啡店、烘焙甜品店、团餐/食堂、酒店餐饮、云厨房/幽灵厨房、美食广场、连锁加盟品牌、全球万店品牌——无论哪种餐饮业态，均有一对一数字化方案。 ■ 方法论驱动：内置 餐饮数字化成熟度五维模型（R-DMM：技术/运营/数据/组织/客户） 、餐饮AI场景优先级RICE评分卡、餐饮科技选型七维决策矩阵、餐饮数字化ROI/TCO计算模型、餐饮全渠道成熟度模型、餐饮食品安全HACCP数字化框架、餐饮供应链四流合一模型（商流/物流/资金流/信息流）、餐饮连锁加盟数字化管控五层模型、餐饮私域运营AIPL模型、全链路56场景数字化-智能化对标框架。 ■ 全球最佳实践对标：Yum! Brands（百胜）Byte by Yum平台、McDonald's Google Cloud Edge + ArchIQ、Starbucks Deep Brew AI、Chipotle全栈自研、海底捞数字化中台、瑞幸全链路数字化、蜜雪冰城万店供应链、麦当劳中国智慧大脑（南京研发中心）——深度拆解11家全球顶级餐饮企业的数字化AI化策略。 ■ 餐饮科技供应商全景图：全球POS/KDS/ERP/CRM/外卖/供应链/IoT/AI八大品类、60+主流供应商深度对照（Toast/Square/Oracle MICROS/客如云/美团餐饮/哗啦啦/天财商龙/食亨/奥琦玮/美味不用等等）。 ■ 交付物模板工场：数字化成熟度评估报告、数字化转型3年路线图、技术选型与供应商评估报告、AI场景优先级评分卡与实施路线图、ROI/TCO商业论证报告、实施方案与里程碑计划、变革管理与培训计划、食品安全数字化方案、供应链数字化方案。 ■ 触发词覆盖（中英文120+）：餐饮数字化、餐饮AI、餐饮智能化、餐厅数字化、智慧餐饮、智慧餐厅、餐饮科技、餐饮SaaS、餐饮系统、POS选型、餐饮ERP、厨房显示KDS、餐饮CRM、会员系统、线上点餐、外卖系统、供应链数字化、餐饮供应链、食材采购、食品安全数字化、HACCP数字化、后厨智能化、前厅数字化、连锁餐饮、加盟管理、餐饮加盟、全渠道运营、私域流量、餐饮数据中台、餐饮BI、餐饮AI应用、智能点餐、语音点餐、AI视觉识别、需求预测、动态定价、智能排班、云厨房、中央厨房、团餐数字化、restaurant digital transformation、restaurant AI、restaurant technology、restaurant POS、restaurant digitalization、smart restaurant、restaurant tech、QSR digital、fast food AI、restaurant SaaS、cloud kitchen、ghost kitchen、food service digital、restaurant automation。
【餐饮数字化AI化高级专家 / Restaurant Digital & AI Transformation Expert】 —— 面向餐饮行业的全栈数字化与AI化转型超级工作台。零基础也能达到世界顶级餐饮数字化AI化专家水平。 ■…
As a process F 29/100 · Will not run — References files that are not bundled: templates/实施方案与里程碑计划模板.md, templates/变革管理与培训计划模板.md, templates/食品安全数字化方案模板.md
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- Shorten the description to 1024 characters.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- The text references files that are not there: add them or drop the references.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 64. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- error
description-longdescription is 1582 chars, limit 1024 - warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
body-longSKILL.md body ≈ 23935 tokens (recommended < 5000); move details to references/ - warning
missing-refreference to a missing file: templates/实施方案与里程碑计划模板.md - warning
missing-refreference to a missing file: templates/变革管理与培训计划模板.md - warning
missing-refreference to a missing file: templates/食品安全数字化方案模板.md - warning
missing-refreference to a missing file: templates/连锁加盟数字化管控方案模板.md - note
description-budgetdescription takes 1582 of the ~15000-char shared budget for all skills - note
frontmatter-keyunknown frontmatter key "contact" - note
frontmatter-keyunknown frontmatter key "language"
Process rating: all ten parameters 29/100
- 0Tools and files. 4 referenced file(s) missing: templates/实施方案与里程碑计划模板.md, templates/变革管理与培训计划模板.md, templates/食品安全数字化方案模板.md
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 10Execution cost. Instruction body is 23935 tokens: crowds the task out of the window
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 4 mutating operations with no state check
- 100Steps. 101 steps
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- low 47 top-level sections: this looks like several domains in one skill
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 1582: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +4Structure: 224 headings
- +3Step-by-step instructions: 101 items
- +4Has examples (30 code blocks)
- +4Reference files are cited in the instructions (6 of 6)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 22.