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JunMA98JunMA98/Computer-science-claude-skills/skills/markitdown/SKILL.md

markitdown

Skill3 starsChanged 8 months agoMITView on GitHub

Convert papers, slide decks, reports, office files, and mixed-format project materials into Markdown for CS research and engineering workflows. Use when turning PDFs, PPTX, DOCX, HTML, JSON, CSV, or similar inputs into LLM-friendly text for literature review, repo cleanup, release prep, or paper-to-code analysis.

42 other projects use a version of this.

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Used with

Projects with it also use playwright, using-superpowers and 7 more.

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The file

We split this skill into the rules it gives an agent

5 rules. 0 are common, 5 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 29 edited versions we read keep these.

  • 5

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use this skill when document conversion is the bottleneck.

    line 8
  2. Distinctive:

    Treat OCR or AI-enhanced descriptions as fallback, not default.

    line 22
  3. Distinctive:

    If a document contains diagrams or equations, note possible fidelity loss.

    line 23
  4. Distinctive:

    Use these when they help:

    line 28
  5. Distinctive:

    When the user asks to convert material without more detail, return:

    line 46

“Common” is measured: kept by at least 6 in 10 of the 29 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

MarkItDown

Use this skill when document conversion is the bottleneck. The goal is to turn source files into clean markdown that can be searched, compared, summarized, or fed into other skills.

Common CS Workflows

  • convert papers or supplements for reading and note extraction
  • convert slide decks into reviewable markdown outlines
  • normalize mixed repo docs before GitHub release
  • extract benchmark tables or structured notes from reports
  • convert exported issue logs, HTML docs, or API pages into text

Working Rules

  • Convert first, then clean structure and headings.
  • Preserve tables, code blocks, and lists where possible.
  • Treat OCR or AI-enhanced descriptions as fallback, not default.
  • If a document contains diagrams or equations, note possible fidelity loss.
  • For scanned PDFs or image-heavy files, explicitly warn about extraction quality.

Local Resources

Use these when they help:

  • scripts/batch_convert.py
  • scripts/convert_literature.py
  • scripts/convert_with_ai.py only when standard conversion is insufficient
  • references/file_formats.md
  • references/api_reference.md
  • assets/example_usage.md

Recommended Outputs

Pick the output based on the task:

  • paper markdown for downstream summarization
  • cleaned note file for a literature matrix
  • repo documentation conversion batch
  • markdown bundle for release preparation

Default Output

When the user asks to convert material without more detail, return:

  1. the converted markdown or conversion result
  2. any fidelity issues
  3. suggested cleanup steps before using the output downstream

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