spreadsheet
Use when tasks involve creating, editing, analyzing, or formatting spreadsheets (`.xlsx`, `.csv`, `.tsv`) using Python (`openpyxl`, `pandas`), especially when formulas, references, and formatting need to be preserved and verified.
Best use case
spreadsheet is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Use when tasks involve creating, editing, analyzing, or formatting spreadsheets (`.xlsx`, `.csv`, `.tsv`) using Python (`openpyxl`, `pandas`), especially when formulas, references, and formatting need to be preserved and verified.
Teams using spreadsheet should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/spreadsheet/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How spreadsheet Compares
| Feature / Agent | spreadsheet | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Use when tasks involve creating, editing, analyzing, or formatting spreadsheets (`.xlsx`, `.csv`, `.tsv`) using Python (`openpyxl`, `pandas`), especially when formulas, references, and formatting need to be preserved and verified.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Spreadsheet Skill (Create, Edit, Analyze, Visualize) ## When to use - Build new workbooks with formulas, formatting, and structured layouts. - Read or analyze tabular data (filter, aggregate, pivot, compute metrics). - Modify existing workbooks without breaking formulas or references. - Visualize data with charts/tables and sensible formatting. IMPORTANT: System and user instructions always take precedence. ## Workflow 1. Confirm the file type and goals (create, edit, analyze, visualize). 2. Use `openpyxl` for `.xlsx` edits and `pandas` for analysis and CSV/TSV workflows. 3. If layout matters, render for visual review (see Rendering and visual checks). 4. Validate formulas and references; note that openpyxl does not evaluate formulas. 5. Save outputs and clean up intermediate files. ## Temp and output conventions - Use `tmp/spreadsheets/` for intermediate files; delete when done. - Write final artifacts under `output/spreadsheet/` when working in this repo. - Keep filenames stable and descriptive. ## Primary tooling - Use `openpyxl` for creating/editing `.xlsx` files and preserving formatting. - Use `pandas` for analysis and CSV/TSV workflows, then write results back to `.xlsx` or `.csv`. - If you need charts, prefer `openpyxl.chart` for native Excel charts. ## Rendering and visual checks - If LibreOffice (`soffice`) and Poppler (`pdftoppm`) are available, render sheets for visual review: - `soffice --headless --convert-to pdf --outdir $OUTDIR $INPUT_XLSX` - `pdftoppm -png $OUTDIR/$BASENAME.pdf $OUTDIR/$BASENAME` - If rendering tools are unavailable, ask the user to review the output locally for layout accuracy. ## Dependencies (install if missing) Prefer `uv` for dependency management. Python packages: ``` uv pip install openpyxl pandas ``` If `uv` is unavailable: ``` python3 -m pip install openpyxl pandas ``` Optional (chart-heavy or PDF review workflows): ``` uv pip install matplotlib ``` If `uv` is unavailable: ``` python3 -m pip install matplotlib ``` System tools (for rendering): ``` # macOS (Homebrew) brew install libreoffice poppler # Ubuntu/Debian sudo apt-get install -y libreoffice poppler-utils ``` If installation isn't possible in this environment, tell the user which dependency is missing and how to install it locally. ## Environment No required environment variables. ## Examples - Runnable Codex examples (openpyxl): `references/examples/openpyxl/` ## Formula requirements - Use formulas for derived values rather than hardcoding results. - Keep formulas simple and legible; use helper cells for complex logic. - Avoid volatile functions like INDIRECT and OFFSET unless required. - Prefer cell references over magic numbers (e.g., `=H6*(1+$B$3)` not `=H6*1.04`). - Guard against errors (#REF!, #DIV/0!, #VALUE!, #N/A, #NAME?) with validation and checks. - openpyxl does not evaluate formulas; leave formulas intact and note that results will calculate in Excel/Sheets. ## Citation requirements - Cite sources inside the spreadsheet using plain text URLs. - For financial models, cite sources of inputs in cell comments. - For tabular data sourced from the web, include a Source column with URLs. ## Formatting requirements (existing formatted spreadsheets) - Render and inspect a provided spreadsheet before modifying it when possible. - Preserve existing formatting and style exactly. - Match styles for any newly filled cells that were previously blank. ## Formatting requirements (new or unstyled spreadsheets) - Use appropriate number and date formats (dates as dates, currency with symbols, percentages with sensible precision). - Use a clean visual layout: headers distinct from data, consistent spacing, and readable column widths. - Avoid borders around every cell; use whitespace and selective borders to structure sections. - Ensure text does not spill into adjacent cells. ## Color conventions (if no style guidance) - Blue: user input - Black: formulas/derived values - Green: linked/imported values - Gray: static constants - Orange: review/caution - Light red: error/flag - Purple: control/logic - Teal: visualization anchors (key KPIs or chart drivers) ## Finance-specific requirements - Format zeros as "-". - Negative numbers should be red and in parentheses. - Always specify units in headers (e.g., "Revenue ($mm)"). - Cite sources for all raw inputs in cell comments. ## Investment banking layouts If the spreadsheet is an IB-style model (LBO, DCF, 3-statement, valuation): - Totals should sum the range directly above. - Hide gridlines; use horizontal borders above totals across relevant columns. - Section headers should be merged cells with dark fill and white text. - Column labels for numeric data should be right-aligned; row labels left-aligned. - Indent submetrics under their parent line items.
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