agentsclimarketplace

Markitdown

Skill ListenLove/codespace-config/opencode/skills/markitdown

opencode config

Install
npx -y skills add ListenLove/codespace-config --skill markitdown

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

当用户提供或引用 PDF、DOCX、PPTX、XLSX 等二进制/办公文档文件时，使用 markitdown_convert_to_markdown 工具将文件转换为 Markdown 格式，以便完整阅读和分析文档内容。转换后基于 Markdown 内容响应用户问题，忽略原文件中的图片（可使用其他模型辅助理解）。

SKILL.md

2.2 KB, 648 tokens by cl100k_base, as published. Nobody here has run it

Markitdown MCP 文件读取

触发条件

当用户消息中包含以下任何情形时，立即加载本 skill：

  • 用户提供、引用或提及了 PDF 文件（.pdf）
  • 用户提供、引用或提及了 Word 文档（.docx）
  • 用户提供、引用或提及了 PPT 演示文稿（.pptx）
  • 用户提供、引用或提及了 Excel 表格（.xlsx）
  • 用户要求读取任何二进制文件或办公文档
  • read 工具返回 "this model does not support pdf input" 或类似错误

核心指令

遇到二进制文件时，不要直接使用 read 工具读取。 按照以下步骤操作：

  1. 使用 markitdown_convert_to_markdown 工具将文件转换为 Markdown
  2. 参数 uri 使用 file:// 协议的绝对路径，例如：
    • file:///home/user/document.pdf
    • file:///mnt/d/project/report.docx
  3. 转换成功后，基于返回的 Markdown 内容理解文档并回答用户问题
  4. 文档中的图片无法直接提取文字，若图片内容关键，可告知用户并建议人工查看

工具签名

markitdown_convert_to_markdown(uri: string)
  • uri: 支持 file:、http:、https:、data: 协议的资源地址

支持的文件格式

格式扩展名
PDF.pdf
Word.docx
PowerPoint.pptx
Excel.xlsx
图片（含文字）.jpg, .png, .jpeg
HTML.html, .htm
CSV.csv
JSON.json
XML.xml
ZIP（内文档）.zip
纯文本.txt, .md

注意事项

  • 转换后的 Markdown 可能丢失原文档的复杂排版和图片内的文字
  • 如果文档中的流程图、架构图等图片内容对回答问题至关重要，可建议用户使用支持图片理解的模型（如 opencode-go/kimi2.6）辅助分析
  • 绝对路径务必使用 file:/// 开头（三个斜杠）

Gives 0 of the 12 instructions most pdf office docs skills give in 648 tokens

Counted across 636 of the 690 authors here whose files we hold, read 2026-08-07

  • extract text using pdfplumberin 89 of 636, across 23 files
  • create PDFs using reportlabin 83 of 636, across 16 files
  • read forms.md to fill out pdf formsin 80 of 636, across 13 files
  • OCR scanned PDFs using pytesseractin 77 of 636, across 10 files
  • merge or split PDFs using qpdfin 70 of 636, across 3 files
  • use excel formulas instead of hardcoded calculated valuesin 68 of 636, across 13 files
  • unpack edit xml and repack existing documentsin 63 of 636, across 8 files
  • document sources for hardcoded valuesin 61 of 636, across 9 files
  • write minimal python code without unnecessary commentsin 59 of 636, across 7 files
  • run the recalculation script after adding or modifying formulasin 59 of 636, across 7 files
  • fix all identified formula errors and recalculatein 58 of 636, across 6 files
  • format years as text stringsin 57 of 636, across 5 files

Said here and by no other author read

  • answer questions based on converted markdown
  • suggest manual review for critical images

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.