claude-ecom MCP — Casos de uso, Instalar & Demo ao vivo

Por que usar

Principais recursos

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

Demo ao vivo

Como fica na prática

claude-ecom-skill.replay ▶ pronto

0/0

Instalar

Escolha seu cliente

~/Library/Application Support/Claude/claude_desktop_config.json · Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "claude-ecom-skill": {
      "command": "git",
      "args": [
        "clone",
        "https://github.com/takechanman1228/claude-ecom",
        "~/.claude/skills/claude-ecom"
      ],
      "_inferred": true
    }
  }
}

Abra Claude Desktop → Settings → Developer → Edit Config. Reinicie após salvar.

~/.cursor/mcp.json · .cursor/mcp.json

{
  "mcpServers": {
    "claude-ecom-skill": {
      "command": "git",
      "args": [
        "clone",
        "https://github.com/takechanman1228/claude-ecom",
        "~/.claude/skills/claude-ecom"
      ],
      "_inferred": true
    }
  }
}

Cursor usa o mesmo esquema mcpServers que o Claude Desktop. Config de projeto vence a global.

VS Code → Cline → MCP Servers → Edit

{
  "mcpServers": {
    "claude-ecom-skill": {
      "command": "git",
      "args": [
        "clone",
        "https://github.com/takechanman1228/claude-ecom",
        "~/.claude/skills/claude-ecom"
      ],
      "_inferred": true
    }
  }
}

Clique no ícone MCP Servers na barra lateral do Cline, depois "Edit Configuration".

~/.codeium/windsurf/mcp_config.json

{
  "mcpServers": {
    "claude-ecom-skill": {
      "command": "git",
      "args": [
        "clone",
        "https://github.com/takechanman1228/claude-ecom",
        "~/.claude/skills/claude-ecom"
      ],
      "_inferred": true
    }
  }
}

Mesmo formato do Claude Desktop. Reinicie o Windsurf para aplicar.

~/.continue/config.json

{
  "mcpServers": [
    {
      "name": "claude-ecom-skill",
      "command": "git",
      "args": [
        "clone",
        "https://github.com/takechanman1228/claude-ecom",
        "~/.claude/skills/claude-ecom"
      ]
    }
  ]
}

O Continue usa um array de objetos de servidor em vez de um map.

~/.config/zed/settings.json

{
  "context_servers": {
    "claude-ecom-skill": {
      "command": {
        "path": "git",
        "args": [
          "clone",
          "https://github.com/takechanman1228/claude-ecom",
          "~/.claude/skills/claude-ecom"
        ]
      }
    }
  }
}

Adicione em context_servers. Zed recarrega automaticamente ao salvar.

claude mcp add claude-ecom-skill -- git clone https://github.com/takechanman1228/claude-ecom ~/.claude/skills/claude-ecom

Uma linha só. Verifique com claude mcp list. Remova com claude mcp remove.

Casos de uso

Usos do mundo real: claude-ecom

Weekly sales review in 10 minutes instead of 2 hours

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

Quando usar: Monday morning review of last week's numbers.

Pré-requisitos

Orders CSV export from Shopify/WooCommerce/etc. — Any platform's standard order export works; column mapping is flexible
Python available locally — python3 --version; skill uses a local Python env
Skill installed — git clone https://github.com/takechanman1228/claude-ecom ~/.claude/skills/claude-ecom

Fluxo

Hand over the CSV

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

→ 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.✓ Copiado

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

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

→ Action list I could paste in a Monday standup

Resultado: A weekly review I actually read.

Armadilhas

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

Combine com: filesystem

Understand why a specific SKU is underperforming

👤 Product managers, merchandisers ⏱ ~20 min intermediate

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

Fluxo

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.✓ Copiado

→ Decomposition with clear delta per factor
Check channel split

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

→ Channel breakdown with concentration analysis

Resultado: A diagnosis, not a vibe.

Customer cohort retention report from order data

👤 Growth teams watching LTV ⏱ ~30 min intermediate

Quando usar: Quarterly review of cohort repeat rates.

Fluxo

Cohort the data

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

→ Triangular retention table
Compare cohorts

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

→ Hypotheses grounded in the data

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

Armadilhas

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

Combinações

Combine com outros MCPs para 10× de alavancagem

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.✓ Copiado

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.✓ Copiado

Ferramentas

O que este MCP expõe

Ferramenta	Entradas	Quando chamar	Custo
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

Custo e limites

O que custa rodar

Cota de API: None
Tokens por chamada: Moderate — summary tables only, not raw rows
Monetário: Free (needs local Python)
Dica: Aggregate in Python; never paste raw orders rows into prompts.

Segurança

Permissões, segredos, alcance

Armazenamento de credenciais: None

Saída de dados: 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.

Solução de problemas

Erros comuns e correções

Python deps missing

The skill uses pandas; run pip install -r ~/.claude/skills/claude-ecom/requirements.txt

Verificar: python -c 'import pandas'

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

Alternativas

claude-ecom vs. outros

Alternativa	Quando usar	Troca
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

Mais

Recursos

📖 Leia o README oficial no GitHub

🐙 Ver issues abertas

🔍 Ver todos os 400+ servidores MCP e Skills