claude-ecom MCP — Anwendungsfälle, Installieren & Live-Demo

Warum nutzen

Hauptfunktionen

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

Live-Demo

In der Praxis

claude-ecom-skill.replay ▶ bereit

0/0

Installieren

Wählen Sie Ihren Client

~/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
    }
  }
}

Öffne Claude Desktop → Settings → Developer → Edit Config. Nach dem Speichern neu starten.

~/.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 nutzt das gleiche mcpServers-Schema wie Claude Desktop. Projektkonfiguration schlägt die globale.

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
    }
  }
}

Klicken Sie auf das MCP-Servers-Symbol in der Cline-Seitenleiste, dann "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
    }
  }
}

Gleiche Struktur wie Claude Desktop. Windsurf neu starten zum Übernehmen.

~/.continue/config.json

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

Continue nutzt ein Array von Serverobjekten statt einer 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"
        ]
      }
    }
  }
}

In context_servers hinzufügen. Zed lädt beim Speichern neu.

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

Einzeiler. Prüfen mit claude mcp list. Entfernen mit claude mcp remove.

Anwendungsfälle

Praxisnahe Nutzung: claude-ecom

Weekly sales review in 10 minutes instead of 2 hours

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

Wann einsetzen: Monday morning review of last week's numbers.

Voraussetzungen

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

Ablauf

Hand over the CSV

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

→ 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.✓ Kopiert

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

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

→ Action list I could paste in a Monday standup

Ergebnis: A weekly review I actually read.

Fallstricke

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

Kombinieren mit: filesystem

Understand why a specific SKU is underperforming

👤 Product managers, merchandisers ⏱ ~20 min intermediate

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

Ablauf

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.✓ Kopiert

→ Decomposition with clear delta per factor
Check channel split

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

→ Channel breakdown with concentration analysis

Ergebnis: A diagnosis, not a vibe.

Customer cohort retention report from order data

👤 Growth teams watching LTV ⏱ ~30 min intermediate

Wann einsetzen: Quarterly review of cohort repeat rates.

Ablauf

Cohort the data

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

→ Triangular retention table
Compare cohorts

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

→ Hypotheses grounded in the data

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

Fallstricke

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

Kombinationen

Mit anderen MCPs für 10-fache Wirkung

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.✓ Kopiert

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.✓ Kopiert

Werkzeuge

Was dieses MCP bereitstellt

Werkzeug	Eingaben	Wann aufrufen	Kosten
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

Kosten & Limits

Was der Betrieb kostet

API-Kontingent: None
Tokens pro Aufruf: Moderate — summary tables only, not raw rows
Kosten in €: Free (needs local Python)
Tipp: Aggregate in Python; never paste raw orders rows into prompts.

Sicherheit

Rechte, Secrets, Reichweite

Credential-Speicherung: None

Datenabfluss: 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.

Fehlerbehebung

Häufige Fehler und Lösungen

Python deps missing

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

Prüfen: 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

Alternativen

claude-ecom vs. andere

Alternative	Wann stattdessen	Kompromiss
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

Mehr

Ressourcen

📖 Offizielle README auf GitHub lesen

🐙 Offene Issues ansehen

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