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.
- License 许可
- Apache-2.0
- 最近更新
- 2026-11-14
- Stars
- ★ 3.3w
- Forks
- 3.9k
01 前言 Front Matter
- name
- spreadsheet
- metadata
- { "author": "openai" }
- description
- 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.
02 资源文件 8
- 参考文档agents/openai.yaml349 B
- 资产文件assets/spreadsheet-small.svg1.4 KB
- 资产文件assets/spreadsheet.png3.0 KB
- 许可证LICENSE.txt10.5 KB
- 脚本references/examples/openpyxl/create_basic_spreadsheet.py1.4 KB
- 脚本references/examples/openpyxl/create_spreadsheet_with_styling.py3.1 KB
- 脚本references/examples/openpyxl/read_existing_spreadsheet.py1.6 KB
- 脚本references/examples/openpyxl/styling_spreadsheet.py2.0 KB
03 SKILL.md 正文
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
- Confirm the file type and goals (create, edit, analyze, visualize).
- Use
openpyxlfor.xlsxedits andpandasfor analysis and CSV/TSV workflows. - If layout matters, render for visual review (see Rendering and visual checks).
- Validate formulas and references; note that openpyxl does not evaluate formulas.
- 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
openpyxlfor creating/editing.xlsxfiles and preserving formatting. - Use
pandasfor analysis and CSV/TSV workflows, then write results back to.xlsxor.csv. - If you need charts, prefer
openpyxl.chartfor 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_XLSXpdftoppm -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.
04 同仓库更多 Skills
wealthville-pool-scoring
Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter/Hold/Exit/Reduce/Avoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.
technical-debt-auditor
Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO/FIXME/HACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like "where should I focus refactoring", "find code smells", "audit my codebase", "what technical debt do we have", "prioritize cleanup work", or asks about code quality metrics. Also trigger when the user mentions "code rot", "legacy cleanup", "refactoring priorities", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say "technical debt" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.
readonly-sql-guard
Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production/ERP/analytics database (a "query" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write/DDL/side-effect/transaction-escape risks. Checks what a statement CALLS, not just its shape.
web-artifacts-builder
Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for complex artifacts requiring state management, routing, or shadcn/ui components - not for simple single-file HTML/JSX artifacts.
webapp-testing
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
discernment-nudge
After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.