# Excel — the brief, the fixture and the scoring

Sent as one message with `orders-q2.xlsx` attached through each tool's own file upload, on the free tier, in a fresh chat (Claude: incognito). Nine tools, same file, same day. If a tool cannot take an .xlsx, the sheet is pasted as CSV text and that is noted.

> I run a small online shop in Portugal. Attached is my order export for April to June 2026 - it's messy. Please:
> 1. Tell me what's wrong with the data and fix it. Give me the cleaned sheet back as an .xlsx.
> 2. Revenue per month and per category, and the product with the most units sold.
> 3. List the duplicate orders and any order where Total doesn't match Qty × Unit Price.
> 4. One Excel formula I can paste to get revenue per month from the cleaned sheet, which keeps working when I add rows.
>
> Keep it short.

## The fixture

`orders-q2.xlsx`, one sheet "Orders", 124 rows: 120 orders plus 4 exact duplicates. Built by `make_fixture.py` (seeded, reproducible); the exact answers are in `ground-truth.txt`. Eight faults are planted:

| Fault | Rows | What a careful reader does |
|---|---|---|
| Exact duplicate orders | 4 | drops them |
| Total ≠ Qty × Unit Price | 5 | flags them; revenue uses Qty × Unit Price |
| Qty blank | 3 | derives Qty from Total ÷ Unit Price |
| Region blank | 2 | flags, cannot be derived |
| Category blank | 8 | fills from the product name |
| Unit Price as text, "€12,50" | 18 | converts to a number |
| Product name case and spacing variants | 18 | normalises to one spelling per product |
| Dates in three formats: real dates, `dd/mm/yyyy` text, `Apr 5, 2026` text | 30 + 12 | parses all as dates; the shop is in Portugal so `03/04/2026` is 3 April |

The date trap has a clear tell: reading `dd/mm` as `mm/dd` produces orders in July to December and 20 invalid dates. `ground-truth.txt` also lists the totals a tool gets if it sums the Total column as-is, or deduplicates but keeps the wrong totals.

## Scoring

- **Faults found** - how many of the eight fault classes the tool names, of 8.
- **File** - an .xlsx comes back, opens, and is actually cleaned: 120 rows, real dates, numeric prices, one spelling per product, categories filled. Checked with openpyxl.
- **Months** - revenue for April, May, June within €0.01 of the truth, of 3. A stated different basis (for example "sum of the Total column as given") is recorded but not counted as right, because the brief asks for the cleaned data.
- **Categories** - of 3. **Top product** - Vinho Verde 750ml, 42 units, right or wrong.
- **Duplicates** - of 4, with any false positives noted. **Mismatches** - of 5.
- **Formula** - works on the cleaned sheet, and keeps working when rows are appended (whole-column or Table references, not a fixed range). Judged by reading it and, where the logic is not obvious, re-implementing it.
- **Invented facts** - anything stated about the shop, the customers or the numbers that the file does not support.
- Time to answer, whether the free tier refused or truncated, and whether the answer is in English.