claude-ecom (Claude Skill) — Установка & Живое демо

Зачем использовать

Ключевые функции

CSV in, structured review out
KPI decomposition (revenue = traffic × CVR × AOV)
Python-backed calculations for accuracy
Prioritized findings, not a wall of charts
Next-actions list tied to findings

Живое демо

Как выглядит на практике

claude-ecom-skill.replay ▶ готово

0/0

Установка

Репозиторий автора · Официальная документация Claude Skills

Сценарии использования

Реальные сценарии: claude-ecom

Weekly sales review in 10 minutes instead of 2 hours

👤 DTC founders, e-commerce managers ⏱ ~15 min beginner

Когда использовать: Monday morning review of last week's numbers.

Поток

Hand over the CSV

Use claude-ecom. Here's last week's orders.csv — do a KPI decomposition and tell me what moved.✓ Скопировано

→ Revenue, CVR, AOV numbers with week-over-week deltas
Ask for findings

Rank the top 3 findings by impact. Be specific — named SKUs, traffic sources, time windows.✓ Скопировано

→ Findings with data points, not 'revenue went up'
Extract actions

For each finding, propose one concrete action I can take this week.✓ Скопировано

→ Action list I could paste in a Monday standup

Итог: A weekly review I actually read.

Подводные камни

Garbage CSVs produce garbage reviews — Clean the export first — merge refunds, exclude test orders

Сочетать с: filesystem

Understand why a specific SKU is underperforming

👤 Product managers, merchandisers ⏱ ~20 min intermediate

Когда использовать: A hero SKU suddenly slowed down and you need to know whether it's traffic, conversion, or pricing.

Поток

Filter to SKU

Use claude-ecom. Focus on SKU SHIRT-BLK-M for the last 60 days. Decompose revenue into traffic × CVR × AOV and compare to the prior 60.✓ Скопировано

→ Decomposition with clear delta per factor
Check channel split

Is the drop concentrated in one channel or across all?✓ Скопировано

→ Channel breakdown with concentration analysis

Итог: A diagnosis, not a vibe.

Customer cohort retention report from order data

👤 Growth teams watching LTV ⏱ ~30 min intermediate

Когда использовать: Quarterly review of cohort repeat rates.

Поток

Cohort the data

Use claude-ecom. Build a monthly acquisition-cohort retention table — orders/customer by month since first order.✓ Скопировано

→ Triangular retention table
Compare cohorts

Which cohort is best? Worst? What's different about them?✓ Скопировано

→ Hypotheses grounded in the data

Итог: A retention view you could share with an investor.

Подводные камни

Short data windows distort recent cohorts — Flag cohorts with <3 months of history as provisional

Комбинации

Сочетайте с другими MCP — эффект x10

claude-ecom-skill + filesystem

Point at a folder of weekly CSVs for automated trend analysis

Run the weekly review for each CSV in data/weekly/ and build a running dashboard.✓ Скопировано

claude-ecom-skill + bigquery-server

Instead of CSVs, pull from a warehouse

Query BigQuery for last week's orders and feed into claude-ecom for the review.✓ Скопировано

Инструменты

Что предоставляет этот MCP

Инструмент	Входные данные	Когда вызывать	Стоимость
load_csv	path, column mapping	Starting any analysis	local
kpi_decompose	date range	Weekly / monthly review	local Python
rank_findings	analysis output	After decomposition	0
cohort_table	cohort granularity	Retention analysis	local

Стоимость и лимиты

Во что обходится

Квота API: None
Токенов на вызов: Moderate — summary tables only, not raw rows
Деньги: Free (needs local Python)
Совет: Aggregate in Python; never paste raw orders rows into prompts.

Безопасность

Права, секреты, радиус поражения

Хранение учётных данных: None

Исходящий трафик: Order summaries and findings are sent to Claude API; raw rows can stay local if you keep aggregation server-side

Order data often includes customer PII. Mask or exclude email/name/address columns before analysis.

Устранение неполадок

Частые ошибки и исправления

Column mapping fails

Standard Shopify/Woo exports work out of the box. For custom exports, supply a column map in the prompt.

Wild numbers in the output

Check for duplicate order rows or non-currency values in the revenue column

Альтернативы

claude-ecom в сравнении

Альтернатива	Когда использовать	Компромисс
Looker Studio / Metabase dashboards	You want persistent dashboards, not one-shot reviews	Setup cost; no LLM-generated narrative
Shopify's own reports	Quick built-in views	Shallow; no cross-store or cohort analysis

Ещё

Ресурсы

📖 Читать официальный README на GitHub

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