implement-paper
Convert academic papers into interactive marimo notebooks, automatically extracting formulas, charts, and code, supporting reproducible research and teaching demonstrations.
npx skills add marimo-team/skills --skill implement-paperBefore / After Comparison
1 组Manually reading PDF papers, transcribing formulas into LaTeX or code editors, searching for datasets and code implementations within papers, and manually configuring environments and reproducing experimental results. Reproducing a single paper typically takes 2-3 days and often leads to missing critical details.
Automatically parsing PDFs and extracting formulas, figures, and code snippets, generating complete marimo notebooks with runnable code cells, and one-click reproduction of experiments and visualization results from papers. Reproducing a single paper now only takes 2-3 hours and allows for interactive exploration of paper methods.
implement-paper
Implement Paper
Turn a research paper into an interactive marimo notebook. For general marimo notebook conventions (cell structure, PEP 723 metadata, output rendering, marimo check, variable naming, etc.), refer to the marimo-notebook skill.
Step 1: Understand what the user wants
Before fetching or reading anything, have a short conversation to scope the work. Ask the user:
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Which part of the paper interests you most? A paper may have multiple contributions — the user likely cares about one or two. Don't implement the whole thing.
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What's the goal? Are they trying to understand the method, reproduce a result, adapt it to their own data, or teach it to someone else? This changes the notebook's tone and depth.
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Do they want to use a specific dataset? If it's relevant, ask. Otherwise, suggest simulating data.
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Does this require PyTorch? Some papers need it, many don't. Ask if unclear — it's a heavy dependency.
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What's their background? The paper aims to fill a knowledge gap — gauge what the user already knows so the notebook can meet them where they are. Skip basics they're familiar with, explain prerequisites they're not.
Only move on once you have a clear picture of what to build.
Step 2: Fetch the paper
See references/fetching-papers.md for how to retrieve paper content via alphaxiv.org. This avoids reading raw PDFs and gives you structured markdown.
Step 3: Plan the notebook
After reading the paper, outline the notebook structure for the user before writing code.
Keep the notebook as small as possible. Sometimes the idea is best conveyed with just a single interactive widget — if you need a custom one, consider the anywidget skill. Other times you need a full training loop — if so, consider using the marimo-batch skill for heavy computation. The goal is the minimum amount of code needed to get the idea across.
A typical arc:
Section Purpose Typical elements
Title & context
Orient the reader
mo.md() with paper title, authors, link
Background Set up prerequisites Markdown + equations
Method Core algorithm step-by-step Code + markdown interleaved
Experiments Reproduce key results Interactive widgets + plots
Conclusion
Summarize takeaways
mo.md()
Not every notebook needs all sections. Share the outline with the user and adjust before writing code.
Step 4: Build the notebook
Create the marimo notebook following the marimo-notebook skill conventions.
Key guidelines:
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Never assume the dataset. Use whatever the user specified in Step 1. If they didn't specify one, simulate data.
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Make it self-contained. A reader should understand the notebook without reading the full paper.
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Use KaTeX for equations. Render key equations with
mo.md(r"""$...$""")so the notebook mirrors the paper's notation. Keep notation consistent with the paper. -
Add interactivity where it aids understanding. Sliders for hyperparameters, dropdowns for dataset variants, or toggles for ablations help readers build intuition.
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Show, don't just tell. Prefer a plot or table over a paragraph of explanation.
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Name variables to match the paper's notation where practical (e.g.,
alpha,X,W), and add comments mapping them to equation numbers.
Tips
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Don't reproduce the entire paper. Focus on what the user asked about in Step 1.
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Iterate visually. Build up figures incrementally (e.g., show data → show model fit → show residuals) rather than dumping everything into one plot.
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If the paper uses heavy notation, include a small "notation reference" cell with a markdown table mapping symbols to descriptions.
Weekly Installs260Repositorymarimo-team/skillsGitHub Stars77First SeenMar 10, 2026Security AuditsGen Agent Trust HubPassSocketPassSnykWarnInstalled onclaude-code196opencode157codex151gemini-cli149github-copilot148cursor147
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