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ecommerce-analytics

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数据
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MIT
最近提交
2026年10月12日

resources/builtin/marketplace/skills/c91a84e7b206/SKILL.md

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Ecommerce Analytics

  1. Identify the business decision, sources, period, currency, timezone and data freshness. Start from supplied exports; there is no mandatory data service.
  2. Read reconciliation and metrics. Establish row grain and source definitions before joining or calculating. Preserve missing values and unmatched records.
  3. Use available local computation for multi-row arithmetic; keep a reproducible calculation or query with the source/field map. Inspect totals and counterexamples, not only a chart.
  4. Compare like periods, products and channels. Separate volume, price, mix, acquisition, refund and cost effects where the data supports them. Describe causal explanations as hypotheses unless independently established.
  5. Deliver the requested metrics and main drivers with formulas, reconciliation, sources, prioritized actions and the smallest missing inputs. A missing cost category limits profit interpretation rather than blocking all supported sales analysis.

For platform exports read channel mapping. This package provides analytical methods; it does not install a platform API, infer account access or execute store changes.

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