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xlsx-parsing

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Read Microsoft Excel (.xlsx) files robustly with `openpyxl` (or `pandas`). Covers multi-sheet workbooks, header rows, empty cells, merged cells, comma-separated list cells, and converting a sheet to a list-of-dicts the rest of your code can consume. Use when a task input or reference document is an `.xlsx` file rather than JSON/CSV.

tasks-extra/nda-playbook-review/environment/skills/xlsx-parsing/SKILL.md

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# xlsx-parsing

Excel workbooks are the lingua franca of operational documents that nobody bothered to put in a database — playbooks, rate cards, deviation policies, finance models, SLAs. They show up in tasks with three properties that trip up naive readers:

1. **Multiple sheets**, only one of which is the data you actually want.
2. **Sparse cells** — a row that uses a column may sit next to a row that doesn't, leaving `None` cells. Empty is meaningful (the rule does not apply), not an error.
3. **Composite cells** — a single cell that contains a comma-separated list, a JSON blob, or a sentence rather than an atomic value.

Treat the workbook as a *typed table* with declared columns, not a free-form spreadsheet. Read every sheet you need, normalise it to `list[dict[str, Any]]`, then operate on that.

## Reading with `openpyxl` (pure Python, no compiled dependencies)

```python
import openpyxl
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