version: "1.0.0" name: weekly-report description: Structure and data sources for the weekly inventory report. Load this when the task is "weekly report", "Monday report", or "summarize inventory status".

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Weekly Inventory Report

Generate the report by writing one Python script via code execution that reads the CSVs and emits markdown. Do not make per-SKU tool calls.

Structure

# Inventory Report — {{warehouse or "All Warehouses"}} — week of {{date}}
 
## Stockouts (on_hand = 0)
| SKU | Product | Warehouse | Days out |
...
 
## Low Stock (below reorder point)
| SKU | On hand | Reorder pt | Days cover | Action |
...top 15 by urgency (lowest days_cover first)...
 
## Open POs
| PO | SKU | Qty | Supplier | ETA |
...from /mnt/user/sinks/purchase_orders.jsonl...
 
## Forecast Risk
SKUs where promo_next_month=1 or is_seasonal=1 and on_hand < 14d cover.
One line each: SKU, reason, recommended action.

Operating cadence (which report is being asked for)

If the request doesn't say which, infer from wording. The structure below is the weekly format; for daily, drop the Open-POs and Forecast-Risk sections and lead with the actions taken.

Aging-PO check (weekly only)

For each open PO, compare days-since-placed to the supplier's lead_time_days. List any PO where elapsed > lead_time as aging and include supplier + days overdue so ops can follow up.

Data sources

• Stockouts & low stock: latest-date rows from /mnt/user/data/stock_levels.csv joined with /mnt/user/data/products.csv
• Days of cover: on_hand / avg_daily_sales (last 14d from /mnt/user/data/sales_history.csv)
• Open POs: /mnt/user/sinks/purchase_orders.jsonl
• Forecast risk: /mnt/user/data/products.csv flags + days-of-cover from above

Do this in code

The CSVs are large (stock_levels is ~67k rows). Write a single script that loads them once, computes everything, and prints the markdown. Don't page through the data with tool calls — that's exactly the pattern this skill replaces.