sales-analysis

# Sales Analysis (data, not charts) This skill converts **raw** sales data into the analytics a markdown review needs by **running the bundled `sales_analysis.py` script**. The **Sales & Performance MCP** deliberately exposes only raw primitives (`query_sales`, `get_catalog`) - there is no `get_slow_movers` or `get_velocity` - so the script derives the metrics itself. It renders **no charts** on purpose: the interactive **merch-report-html** skill owns all the visuals at the end, which keeps the review fast. This skill just computes the numbers and prints the structured hand-offs. ## When to use **First** in an end-of-quarter markdown review, whenever a manager wants to see top sellers and slow / dead stock over a window. ## Inputs (fetch from the Sales & Performance Agent, then pass as flags) 1. **Raw weekly rows** - `query_sales(start_week, end_week)` for the window (default the full 8 weeks `2026-04-12` ... `2026-05-31`). Each row is `{sku, name, category, week, units, revenue}`. 2. **Catalog facts** - `get_catalog()` -> `{sku, name, price, unit_cost, stock_on_hand}`. ## Run the bundled script ```bash python3 sales_analysis.py \ --woc-threshold 8 \ --rows '[{"sku":"SKU-VR-GOGGLES","name":"OmegaVision VR Headset","category":"accessory","week":"2026-04-12","units":1,"revenue":199.99}]' \ --catalog '[{"sku":"SKU-VR-GOGGLES","name":"OmegaVision VR Headset","price":199.99,"unit_cost":70.00,"stock_on_hand":48}]' ``` It prints a compact summary, then two structured lines: - `TOP_SELLERS_JSON=[{name, units, velocity, revenue}]` - `SLOW_MOVERS_JSON=[{sku, name, price, cost, weeks_of_cover, margin, units}]` ## Metrics it derives (for your report, not for you to recompute) Over a window of `W` weeks, per SKU: `velocity = units_total / W`, `weeks_of_cover = stock_on_hand / velocity`, `margin% = (price - cost) / price * 100`. **Slow movers** = weeks-of-cover above the threshold (default 8), sorted descending. **Top sellers** = highest units. ## Next steps - Pass `SLOW_MOVERS_JSON` (it already carries `sku, name, price, cost`) to the **markdown-optimizer** skill, together with the guardrails from the Store Policy Agent. - Keep `TOP_SELLERS_JSON` and `SLOW_MOVERS_JSON` so the **merch-report-html** skill can render them into the final interactive report. - Do **not** recommend discounts here - that needs the Store Policy Agent's guardrails. ## Notes - The bundled script is `sales_analysis.py`; it uses only the Python standard library (no matplotlib, no network). - Never invent numbers - every value derives from the MCP results you fetched. All data is mock; for demo purposes only.