CF retail-digital-ai-expert
【零售数字化AI化高级专家 / Retail Digital & AI Transformation Expert】 —— 面向零售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零售数字化AI化专家水平。 ■ 核心定位：覆盖零售全业态（街边夫妻老婆店→社区超市→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→仓储与物流中心→门店运营→收银与支付→电商与全渠道→会员与CRM→财务与人力→品牌与加盟）、全技术栈（POS/ERP/WMS/OMS/CRM/CDP/BI/AI Platform/IoT/Cloud）的端到端数字化转型技能。 ■ 业态全覆盖：街边便利店、社区超市、大卖场/ hypermarket、百货商场、专营专卖店(服装/美妆/药房/3C电子/家居建材)、快时尚、生活方式零售(名创优品/无印良品)、折扣零售、DTC品牌店、电商/全渠道、连锁加盟品牌、全球万店品牌——无论哪种零售业态，均有一对一数字化方案。 ■ 方法论驱动：内置 零售数字化成熟度五维模型（R-DMM：技术/运营/数据/组织/客户）、零售AI场景优先级RICE评分卡、零售科技选型七维决策矩阵、零售数字化ROI/TCO计算模型、零售全渠道成熟度模型、零售品类管理数字化框架、零售供应链四流合一模型、零售连锁加盟数字化管控五层模型、零售私域运营AIPL模型、全链路60场景数字化-智能化对标框架。 ■ 全球最佳实践对标：Walmart（统一Agentic AI框架/Sparky/Marty）、Amazon（Rufus AI购物/AWS零售）、Costco（会员AI/供应链优化）、7-Eleven（日本7-Eleven数字化平台）、名创优品（全球数据中台/Dynamics 365/海鼎ERP）、屈臣氏（O+O全渠道/CDP）、盒马（全链路数字化/悬挂链物流）、Sam's Club（AI消除1亿+人工任务）、Nike（DTC数字化全栈）、LVMH（奢侈品数字化/AR/GenAI）——深度拆解12家全球顶级零售企业的数字化AI化策略。 ■ 零售科技供应商全景图：全球POS/ERP/WMS/OMS/CRM/CDP/电商平台/IoT/AI九大品类、80+主流供应商深度对照。 ■ 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、全渠道运营方案、连锁加盟数字化管控方案。 ■ 触发词覆盖（中英文150+）：零售数字化、零售AI、零售智能化、智慧零售、智慧门店、零售科技、零售SaaS、零售POS、零售ERP、WMS仓储管理、OMS订单管理、零售CRM、会员系统、全渠道零售、私域运营、品类管理、进销存、零售供应链、零售数据中台、零售BI、零售AI应用、智能导购、AI推荐、需求预测、动态定价、智能补货、库存优化、视觉搜索、无人零售、直播电商、社交电商、DTC品牌、快闪店、retail digital transformation、retail AI、retail technology、retail POS、retail ERP、retail digitalization、smart retail、retail tech、omnichannel retail、unified commerce、retail SaaS、headless commerce、retail automation、retail data platform、WMS、OMS、CDP、retail media network。
【零售数字化AI化高级专家 / Retail Digital & AI Transformation Expert】 —— 面向零售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零售数字化AI化专家水平。 ■…
As a process F 27/100 · Will not run — References files that are not bundled: tools/数字化ROI快速计算器.md, examples/, workflows/
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: 0. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- error
description-longdescription is 1527 chars, limit 1024 - warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
body-longSKILL.md body ≈ 23752 tokens (recommended < 5000); move details to references/ - warning
missing-refreference to a missing file: tools/数字化ROI快速计算器.md - warning
missing-refreference to a missing file: examples/ - warning
missing-refreference to a missing file: workflows/ - warning
missing-refreference to a missing file: tools/ - warning
missing-refreference to a missing file: workflows/phase-08-持续优化与迭代/ - warning
missing-refreference to a missing file: templates/数字化成熟度评估报告模板.md - warning
missing-refreference to a missing file: templates/数字化转型路线图模板.md - warning
missing-refreference to a missing file: templates/技术选型与供应商评估模板.md - warning
missing-refreference to a missing file: templates/AI场景优先级评分卡模板.md - warning
missing-refreference to a missing file: templates/ROI与商业论证模板.md - warning
missing-refreference to a missing file: templates/项目实施计划模板.md - warning
missing-refreference to a missing file: templates/项目建议书Proposal模板.md - warning
missing-refreference to a missing file: templates/SOW合同模板.md - warning
missing-refreference to a missing file: templates/变革管理计划模板.md - warning
missing-refreference to a missing file: templates/项目验收报告模板.md - warning
missing-refreference to a missing file: templates/全渠道运营方案模板.md - warning
missing-refreference to a missing file: templates/加盟商数字化管控模板.md - note
description-budgetdescription takes 1527 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 27/100
- 0Tools and files. 17 referenced file(s) missing: tools/数字化ROI快速计算器.md, examples/, workflows/
- 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
- 0Progress reporting. Says nothing while it works
- 10Execution cost. Instruction body is 23752 tokens: crowds the task out of the window
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 2 mutating operations with no state check
- 100Steps. 70 steps
- 100Consistency. Name and required fields are in place
- low 42 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 1527: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- -217 emoji in the instructions: noise for the model
- +2Single-language instructions
- +4Structure: 220 headings
- +3Step-by-step instructions: 70 items
- +4Has examples (27 code blocks)
- +4Reference files are cited in the instructions (1 of 6)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 20.