Convert order transaction CSVs into structured ecommerce business reviews.
npx skills add https://github.com/takechanman1228/claude-ecom --skill ecom
Please help me install this Agent Skill. Skill: ecom Source: https://github.com/takechanman1228/claude-ecom/tree/main/skills/ecom Command: npx skills add https://github.com/takechanman1228/claude-ecom --skill ecom
Turn raw order transaction CSVs into a concise, consultant-style ecommerce business review that highlights momentum, structural health, and prioritized next actions so non-technical store owners and analysts can understand what's driving revenue and what to fix.
Place your orders CSV in the working directory and run the review command (for example: /ecom review) to produce a REVIEW.md in the output folder.
High-intent search queries and answers about installing and using this skill.
To generate an ecommerce business review, place your order transaction CSV in the working directory and run the review command to output a structured REVIEW.md with KPI decomposition and prioritized action plans.
Multi-horizon KPI analysis computes 30d, 90d, and 365d ecommerce metrics with prior-period comparisons, decomposing retention and AOV into pass, watch, or fail health checks displayed in a readable KPI tree.
Yes, you can analyze D2C retail order datasets by asking inline natural-language questions like how retention is looking, receiving focused answers without generating a full REVIEW.md report.
Yes, the ecommerce review process requires a local Python engine to compute review.json from your orders CSV and generate period-specific reports using on-disk reference files for narrative templates.
The best way to diagnose structural health issues is running a consultant-grade ecommerce review that produces What, Why, and What-to-do findings with guardrails and a prioritized action plan.
Period-specific ecommerce KPI reports are limited by the need for a local Python engine and properly formatted order transaction CSVs, requiring on-disk reference files for cluster logic and recommended actions.
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