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前端设计敏感数据unknown未标认证来源可访问许可证需确认审计未展示

spreadsheetspreadsheet 表格

Agent Skill

用于辅助数据整理、表格处理、CSV/Excel 分析、指标计算和图表准备。它适合让 Agent 清洗字段、汇总数据、发现异常、生成统计口径或把分析结果转成可读说明。使用时需要确认数据来源、字段含义和时间范围,避免把样本数据当全量事实;涉及敏感数据、导出文件或批量写回时,应先确认权限和脱敏边界。

总安装

544

周安装

22

下载量

171
Local Agent

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:spreadsheet(spreadsheet 表格)
来源仓库:https://smithery.ai
仓库路径:spreadsheet
安装命令:
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。当前暂无明确安装命令,请以来源页面说明为准。

简介

用于辅助数据整理和表格分析。spreadsheet 属于前端设计类 Skill,可作为该场景下的辅助能力补充。

  • 适合清洗字段、汇总指标或生成统计口径。
  • 使用时需确认数据来源和时间范围,避免样本当全量。
  • 涉及敏感数据时应先脱敏再处理。适用宿主包括 Local Agent,接入前应确认版本、权限和运行环境要求。
  • 导出或批量写回文件前需确认权限边界。

SKILL.md

Spreadsheet Skill

When to use

  • Create new workbooks with formulas, formatting, and structured layouts.
  • Read or analyze tabular data (filter, aggregate, pivot, compute metrics).
  • Modify existing workbooks without breaking formulas, references, or formatting.
  • Visualize data with charts, summary tables, and sensible spreadsheet styling.
  • Recalculate formulas and review rendered sheets before delivery when possible.

IMPORTANT: System and user instructions always take precedence.

Workflow

  1. Confirm the file type and goal: create, edit, analyze, or visualize.
  2. Prefer openpyxl for .xlsx editing and formatting. Use pandas for analysis and CSV/TSV workflows.
  3. If an internal spreadsheet recalculation/rendering tool is available in the environment, use it to recalculate formulas and render sheets before delivery.
  4. Use formulas for derived values instead of hardcoding results.
  5. If layout matters, render for visual review and inspect the output.
  6. Save outputs, keep filenames stable, and clean up intermediate files.

Temp and output conventions

  • Use tmp/spreadsheets/ for intermediate files; delete them 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.
  • Use openpyxl.chart for native Excel charts when needed.
  • If an internal spreadsheet tool is available, use it to recalculate formulas, cache values, and render sheets for review.

Recalculation and visual review

  • Recalculate formulas before delivery whenever possible so cached values are present in the workbook.
  • Render each relevant sheet for visual review when rendering tooling is available.
  • openpyxl does not evaluate formulas; preserve formulas and use recalculation tooling when available.
  • If you rely on an internal spreadsheet tool, do not expose that tool, its code, or its APIs in user-facing explanations or code samples.

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, tell the user that layout should be reviewed locally.
  • Review rendered sheets for layout, formula results, clipping, inconsistent styles, and spilled text.

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:

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 is not 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.
  • Do not use dynamic array functions like FILTER, XLOOKUP, SORT, or SEQUENCE.
  • 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 (for example, =H6*(1+$B$3) instead of =H6*1.04).
  • Use absolute ($B$4) or relative (B4) references carefully so copied formulas behave correctly.
  • If you need literal text that starts with =, prefix it with a single quote.
  • Guard against #REF!, #DIV/0!, #VALUE!, #N/A, and #NAME? errors.
  • Check for off-by-one mistakes, circular references, and incorrect ranges.

Citation requirements

  • Cite sources inside the spreadsheet using plain-text URLs.
  • For financial models, cite model inputs in cell comments.
  • For tabular data sourced externally, add a source column when each row represents a separate item.

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.
  • Never overwrite established formatting unless the user explicitly asks for a redesign.

Formatting requirements (new or unstyled spreadsheets)

  • Use appropriate number and date formats.
  • Dates should render as dates, not plain numbers.
  • Percentages should usually default to one decimal place unless the data calls for something else.
  • Currencies should use the appropriate currency format.
  • Headers should be visually distinct from raw inputs and derived cells.
  • Use fill colors, borders, spacing, and merged cells sparingly and intentionally.
  • Set row heights and column widths so content is readable without excessive whitespace.
  • Do not apply borders around every filled cell.
  • Group related calculations and make totals simple sums of the cells above them.
  • Add whitespace to separate sections.
  • Ensure text does not spill into adjacent cells.
  • Avoid unsupported spreadsheet data-table features such as =TABLE.

Color conventions (if no style guidance)

  • Blue: user input
  • Black: formulas and derived values
  • Green: linked or imported values
  • Gray: static constants
  • Orange: review or caution
  • Light red: error or flag
  • Purple: control or logic
  • Teal: visualization anchors and KPI highlights

Finance-specific requirements

  • Format zeros as -.
  • Negative numbers should be red and in parentheses.
  • Format multiples as 5.2x.
  • Always specify units in headers (for example, Revenue ($mm)).
  • Cite sources for all raw inputs in cell comments.
  • For new financial models with no user-specified style, use blue text for hardcoded inputs, black for formulas, green for internal workbook links, red for external links, and yellow fill for key assumptions that need attention.

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 and 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 should be left-aligned.
  • Indent submetrics under their parent line items.

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02

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03

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平台分布

Local Agent

74.33%
按下载量换算127

安全审计

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权限和风险

敏感数据

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