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Commerce Bi Copilot

面向创始人、运营和分析师的启发式分析技能：根据用户粘贴的 KPI 备注、活动背景与业务问题，用固定指标词典和异常清单输出 Markdown 简报，包含指标口径对齐、驱动因素树、下钻建议、优先行动与局限说明。技能声明不连接实时仓库、广告 API、ERP 或真实表格，结论需人工确认。

harrylabsj
harrylabsj
热度 35.16
最近更新 2026-06-06 05:10:48
分类：数据依赖：内部调用大模型应用场景：报告撰写 / 市场分析 / 摘要总结

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add commerce-bi-copilot
Metadata
name
commerce-bi-copilot
description
Turn ecommerce exports, KPI notes, and natural-language business questions into metric alignment notes, anomaly diagnoses, operator-ready summaries, and prioritized next actions for founders, operators, and analysts. Use when reviewing GMV, net revenue, ROAS, refund rate, inventory health, channel mix, or campaign performance without live BI connectors or SQL access.

Commerce BI Copilot

Overview

Use this skill to convert fragmented commerce data context into an operator-friendly insight brief. It is designed for teams that need quick explanations, not a heavyweight dashboard rebuild.

This MVP is heuristic. It does not access live warehouses, ad APIs, ERP systems, or real spreadsheets. Instead, it applies a commerce metric dictionary, anomaly checklist, and action-planning framework to the user's provided notes.

Trigger

Use this skill when the user wants to:

  • explain why a KPI moved up or down
  • prepare a daily, weekly, campaign, or executive business review
  • align teams on metric definitions such as GMV, net revenue, ROAS, MER, or refund rate
  • turn rough exports or pasted KPI notes into a concise action brief
  • produce follow-up questions for an analyst, founder, or agency client

Example prompts

  • "Why did GMV drop 12% this week?"
  • "Create a weekly ecommerce business review from these KPI notes"
  • "Help me explain falling ROAS after our spring promotion"
  • "Turn these Shopify, Meta, and refund notes into an executive summary"

Workflow

  1. Capture the business question, time frame, and referenced channels.
  2. Normalize the likely metric set and call out any definition ambiguity.
  3. Build a short driver tree across traffic, conversion, pricing, refunds, inventory, and mix.
  4. Produce prioritized drill-downs and next actions.
  5. Return a markdown brief that a founder or operator can immediately use.

Inputs

The user can provide any mix of:

  • pasted KPI snapshots or rough metric notes
  • mentions of data sources such as Shopify, Amazon, Meta Ads, Google Ads, GA4, ERP, or CRM
  • campaign or calendar context, such as promotions, launches, or stockouts
  • business questions about revenue, efficiency, refunds, margin, or channel contribution
  • audience context, such as founder update, operator review, or agency client recap

Outputs

Return a markdown brief with:

  • analysis mode and source assumptions
  • KPI snapshot table
  • likely driver tree
  • recommended drill-downs
  • prioritized next best actions
  • executive-ready summary bullets
  • assumptions and limitations

Safety

  • Do not pretend to read live numbers or source files.
  • Surface metric-definition ambiguity when GMV, net revenue, refunds, or attribution may conflict.
  • Avoid certainty when the input is partial or anecdotal.
  • Keep budget, pricing, inventory, and operational decisions human-approved.

Examples

Example 1

Input: Shopify orders, Meta spend notes, and the question "Why did yesterday GMV fall?"

Output: identify a likely mix of traffic decline, conversion weakness, or stock issues, then recommend the next drill-downs and immediate operator actions.

Example 2

Input: weekly KPI notes for channels, refunds, and top products.

Output: generate a compact weekly business brief with risks, wins, and next-week priorities.

Acceptance Criteria

  • Return markdown text.
  • Include KPI, diagnosis, and action sections.
  • Mention evidence gaps or metric ambiguity when relevant.
  • Keep the output practical for operators and founders.