---

name: ecom description: > Ecommerce business review for D2C stores from order transaction CSVs. Runs the bundled Python engine (KPI trees, ~30 pass/watch/fail health checks, 30d/90d/365d windows), then interprets the results: either a full narrative business review written to REVIEW.md, or an inline answer to a focused question. when_to_use: > Use for "ecommerce review", "store review", "store health", "business review", revenue/customer/product analysis of an online store, or questions over order data like "how was last month", "how's retention", "why did revenue drop". argument-hint: "review [30d|90d|365d] [question]" allowed-tools:

• Read
• Grep
• Glob
• Write
• Bash(ecom *)

---

ecom — Ecommerce Business Review Toolkit

D2C ecommerce analytics. The bundled Python engine computes KPIs, runs health checks, and scores performance from order transaction data; you (Claude) interpret the numbers and write the human-readable report.

Key principle: Python computes the numbers. Claude interprets them. Never present raw numbers without business context.

Arguments: $ARGUMENTS (if empty, infer intent from the conversation)

Mode Selection

Arguments / intent	Mode	Output
empty or review	Full Review (auto-selects periods from data)	REVIEW.md
review 30d / 90d / 365d	Full Review, single period	REVIEW_{PERIOD}.md
contains a natural-language question	Focused Query	Inline answer, no file

Input

Order transaction CSV. Each row = one order or line item. Required columns: order ID, order date, customer ID (or email), revenue (after discounts, before tax/shipping). Optional: quantity, SKU/product, discount amount. Column names are fuzzy-matched by the loader.

If no CSV is specified, Glob for *.csv in the working directory and ask the user if multiple plausible candidates exist.

Running the Engine

The ecom CLI is on PATH while this plugin is enabled:

ecom review orders.csv --output <output-dir>
ecom review orders.csv --period 90d --output <output-dir>

If ecom is not on PATH, use the bundled launcher with the same arguments: "${CLAUDE_SKILL_DIR}/../../bin/ecom". The first run bootstraps a private Python venv under ~/.local/share/claude-ecom/ and may take a minute; later runs are instant.

Output: review.json (or review_{period}.json for --period runs) in the output directory (defaults to current directory).

Workflow

Phase 1: Compute (Python)

Run the engine (add --period per Mode Selection). It computes, per available period: summary KPIs with prior-period comparison, a new-vs-returning KPI tree, revenue driver decomposition (AOV / volume / mix), and — for 365d — repeat purchase rate and a 12-month monthly_trend. It also evaluates ~30 health checks across Revenue, Customer, and Product; each returns pass / watch / fail and powers the 🟢/🟡/🔴 markers.

Phase 2: Interpret (you — Claude)

Full Review:

1. Read review.json (see Data Rules below; full schema in review-schema.md)
2. Read report-format.md — REQUIRED before writing; contains all section templates and Finding Quality Standards
3. Read review-narratives.md to pick the narrative arc
4. Load other references as needed (table below)
5. Write REVIEW.md (or REVIEW_{PERIOD}.md) satisfying the Report Contract below

Focused Query:

1. Read focused-query.md — REQUIRED; contains the query-to-period mapping and answer format
2. Run the engine per that mapping, read the JSON, answer inline (10-30 lines). Do NOT write a file.

Your job is to weave trends and diagnostics into one coherent story. Period analysis tells you where things are heading; health checks tell you what's broken right now. The report combines both.

Report Contract (Full Review)

Before writing, verify the report will contain ALL sections in this exact order. Missing or reordered sections are a format violation.

1. Executive Summary — narrative blockquote (4-6 lines) + Scoreboard table
2. 30d Pulse section (if data available) — KPI tree + max 1 finding
3. 90d Momentum section (if data available) — KPI tree + drivers + max 2 findings
4. 365d Structure section (if data available) — KPI tree + drivers + max 3 findings
5. Action Plan — max 5 items grouped by time horizon + Guardrails (REQUIRED)
6. Data Notes — 2-4 lines

Hard rules:

• Target ~150 lines total; 5-7 findings max across all periods; never repeat a finding across periods
• Sections in this order; do NOT reorganize by theme; no standalone "What's Working Well" / "Issues to Address" sections — positive signals live in the Executive Summary and KPI tree markers
• Every period section uses the KPI tree format with 🟢/🟡/🔴 markers
• Every finding follows What is → Why it matters → What to do (quantitative fact → data-backed tension → direction; "consider", "improve", "optimize", "explore" are banned)
• Action Plan is the single source of truth for deadlines and success metrics, and ends with Guardrails (2-3 must-not-deteriorate metrics)

Templates, examples, and the full quality standards are in report-format.md.

Reference Files

Load on-demand — do NOT load all at startup. Paths are relative to this skill's directory.

File	When to load
report-format.md	Every Full Review, before writing
review-narratives.md	Every Full Review — narrative arc by health level and trajectory
focused-query.md	Every Focused Query
review-schema.md	When review.json semantics are unclear
finding-clusters.md	Full Review — to group related issues into themes
recommended-actions.md	When turning watch/fail checks into actions
impact-formulas.md	When estimating revenue impact
health-checks.md	When a check's definition/threshold is unclear
benchmarks.md	When comparing KPIs to D2C benchmarks

Data Rules

• review.json only. All numbers MUST come from review.json or be derived from its values. Never reference external sources (Shopify Analytics, GA, ...). Recommending an external investigation as an action is allowed; quoting numbers from one is not.
• periods contains only the periods listed true in data_coverage; all _change fields are proportional vs the prior period (0.08 = +8%).
• health.top_issues is the pre-sorted subset of failing checks; action_candidates are Python's suggestions — refine and rewrite them in business language, never copy verbatim.
• Never expose internal check IDs or check counts in the report.
• When data_quality is non-empty, mention relevant warnings in Data Notes, and do not present partial-month MoM as real performance signals.

Incomplete Data

• Omit what you can't measure. No N/A, no empty sections, no apologies.
• Shorter data = shorter report. Gaps are noted in Data Notes only.

Language

Write the entire report (including Data Notes) in ONE language — match the user's prompt/store language.

Quality Gates

• Never present numbers without interpretation — always explain why
• Business language, not jargon; explain terms on first use
• Connect related findings into systemic patterns
• 80/20 rule: ~80% confirmation (builds trust), ~20% surprise (drives action)
• No numeric scores, letter grades, or percentage health ratings — pass/watch/fail signals only