Excel Export

When to Use

Use when the agent must deliver a .xlsx Excel file — sales reports, query results, multi-sheet data exports, financial summaries, or any structured data that needs polished spreadsheet formatting. This skill handles workbook creation only. Do not use it to read, edit, or preserve existing Excel files.

Setup

python3 -m venv ~/.openclaw/workspace/.venv_excel
~/.openclaw/workspace/.venv_excel/bin/pip install xlsxwriter

No credentials required.

How to Run

~/.openclaw/workspace/.venv_excel/bin/python skills/excel-export/scripts/build_xlsx.py \
  --input  <path/to/workbook.json> \
  --output <path/to/report.xlsx>

Default export directory: ~/.openclaw/workspace/exports/excel/.

The script prints a JSON summary to stdout on success:

{
  "success": true,
  "output": "/absolute/path/to/report.xlsx",
  "sheets": [
    { "sheet": "Ventes", "rows": 42 },
    { "sheet": "Charges", "rows": 18 }
  ]
}

Input Format

The input is a single JSON file. Top-level shape:

{
  "sheets": [ ... ]
}

Each sheet object:

Each column object:

Rows are objects keyed by column key, not positional arrays. Missing keys produce blank cells. Unknown keys cause a validation error.

Column Types

Use numfmt on any column to override its type's default format.

Rendering Rules

Layout — fixed, deterministic, never configured per-sheet:

• No title/subtitle → table header on row 1.
• Title only → title row 1, blank row 2, table header row 3.
• Title + subtitle → title row 1, subtitle row 2, blank row 3, table header row 4.

Freeze pane — always at the first data row (A2, A4, or A5 depending on title presence).

Table style — each sheet is one rectangular Excel table with filters, banded rows, and Table Style Medium 9.

Column widths — auto-estimated from headers and sampled values, capped at 50 characters. Explicit width in the column definition overrides the estimate.

Formulas — use Excel structured references (e.g. =[@Revenue]/SUM([Revenue])). Formula columns are injected through the table definition, not written cell-by-cell, so they apply to every data row automatically.

Totals row — if any column has total, the table includes a totals row. The first column displays "Total" as a label unless it has its own total function.

Empty datasets — produce a valid workbook with headers, filters, styling, and zero data rows.

Data-Integrity Doctrine

These rules are always enforced — they are not optional:

1. Preserve data types. Leading-zero strings, phone numbers (+212…), and numeric strings longer than 15 digits are always stored as text. Excel silently corrupts these if written as numbers.

1. Keep calculations in Excel. When the workbook should stay live, write formulas — not hardcoded derived values from Python. Use structured references so formulas survive row insertions and deletions.

1. Treat dates explicitly. Dates are serial numbers with legacy quirks. The script writes real date objects with explicit dd/mm/yyyy formatting — never raw serial numbers or ambiguous strings.

1. Validate before delivery. The script validates every input field, rejects unknown keys, and fails fast with clear error messages. A workbook should never ship with silent data loss.

1. Regional defaults are French / Morocco. Date format dd/mm/yyyy, currency MAD, booleans Oui / Non. Note: final display still depends partly on the viewer's local Excel regional settings.

Full Example

{
  "sheets": [
    {
      "name": "Ventes Q1",
      "title": "Rapport des Ventes — Q1 2026",
      "subtitle": "Direction Commerciale",
      "columns": [
        { "key": "region",  "header": "Région",     "type": "text" },
        { "key": "ca",      "header": "CA (MAD)",   "type": "currency", "total": "sum" },
        { "key": "volume",  "header": "Volume",     "type": "integer",  "total": "sum" },
        { "key": "growth",  "header": "Croissance", "type": "percent" },
        { "key": "date",    "header": "Date",       "type": "date" },
        { "key": "active",  "header": "Actif",      "type": "boolean" }
      ],
      "rows": [
        { "region": "Casablanca", "ca": 1250000, "volume": 340, "growth": 0.15, "date": "2026-03-31", "active": true },
        { "region": "Rabat",      "ca": 980000,  "volume": 210, "growth": -0.03, "date": "2026-03-31", "active": true },
        { "region": "Tanger",     "ca": 670000,  "volume": 155, "growth": 0.08,  "date": "2026-03-31", "active": false }
      ]
    }
  ]
}

~/.openclaw/workspace/.venv_excel/bin/python skills/excel-export/scripts/build_xlsx.py \
  --input  ~/.openclaw/workspace/exports/ventes_q1.json \
  --output ~/.openclaw/workspace/exports/excel/ventes_q1.xlsx

Validation Rules

The script fails fast with a clear error on:

• Invalid or missing JSON
• Duplicate sheet names
• Sheet names exceeding 31 characters, containing [ ] : * ? / \, or starting/ending with apostrophes
• subtitle without title
• Row count above 1,048,576 or column count above 16,384
• Unknown keys in row objects
• Invalid type or total values
• Non-string formula definitions
• Unrecognized boolean values (only true/false, 1/0, yes/no, oui/non accepted)
• Non-integer values in integer columns (e.g. 12.9 is rejected, not truncated)
• Unparseable dates, datetimes, or numeric strings in typed columns

SQL-Driven Exports

When generating Excel files from SQL query results, read references/SQL_TO_EXCEL_RECIPE.md for the standard pipeline: query with mssql, normalize values, build the JSON spec, then render. The recipe includes SQL-to-JSON type mappings, normalization rules, ready-made templates, and common mistakes.

Limitations (v1)

• Creation only — does not read or edit existing workbooks.
• No template preservation, merged cells, or conditional formatting.
• One fixed visual theme — no configurable colour system.
• No CSV input — upstream steps must produce JSON.
• xlsxwriter does not calculate formulas; the recipient's Excel client recalculates on open.