BC ecommerce-ops-cockpit
国内外电商全链路决策台，覆盖国内（淘宝/天猫/京东/拼多多/抖店/小红书/快手小店/微信小店）与跨境（Amazon/eBay/Walmart/Shopee/Lazada/TikTok Shop 海外/Temu/SHEIN/AliExpress/Shopify 独立站）。能力含：选品评估与竞品分析、定价与真实利润核算、保本售价、跨境到岸成本与保本售价（含头程、关税、VAT、FBA、出口退税、汇率敏感性）、投放前保本ROI与最高可承受出价与目标ROI反推、有流量不出单与转化率下滑与ROI上不去的漏斗诊断、GMV与利润变化归因（价格/销量/结构三分量加毛利桥）、多平台运营周报、商品标题与主图文案与详情页与短视频脚本与直播话术的撰写审核与广告法违禁词极限词检测、批量改价上下架的 dry-run 预演、平台流量机制与冷启动打法、运营节奏与大促排期与备货计划、平台结算单对账（佣金率区间、负结算、退款大于销售额、零销售额有费用、口径不一致、坑位费重复计、补贴跨期、返点阶梯）。当用户问「这款产品值不值得做」「怎么定价」「真实利润多少」「保本ROI多少」「最高能出价多少」「跨境到岸成本怎么算」「关税VAT怎么算」「有流量不出单」「转化率下滑」「ROI做不上去」「GMV跌了是价格还是销量问题」「帮我写或审标题主图详情页短视频脚本直播话术」「有没有违禁词」「平台流量机制或冷启动」「运营节奏大促排期备货」「结算单对账佣金率坑位费返点」「批量改价怎么预演」时使用。综合任务必须真实调度专家团队并等待回传（跨境场景必派跨境与合规专家），交付前须通过数据验真、PDF验真与交付清单复核三道关卡，报告默认输出含图表的PDF。
国内外电商全链路决策台，覆盖国内（淘宝/天猫/京东/拼多多/抖店/小红书/快手小店/微信小店）与跨境（Amazon/eBay/Walmart/Shopee/Lazada/TikTok Shop 海外/Temu/SHEIN/AliExpress/Shopify…
As a process C 50/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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: 38. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "agent_created"
Process rating: all ten parameters 50/100
- 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
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 50 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2414 tokens
- 100Running it twice. No mutating operations
- low 13 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)
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +3Description length 706: enough signal without eating the budget
- +4Structure: 14 headings
- +3Step-by-step instructions: 50 items
- +4Has examples (3 code blocks)
- +4Reference files are cited in the instructions (16 of 18)
- +3All 15 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.