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Scholar Slides

Community
luwill
scholar-slides

Use when the user wants a research paper, arXiv/DOI link, or research topic turned into academic slides — journal club / lab meeting (组会汇报), thesis defense (答辩幻灯片), or conference / job talk (学术报告) — or shares a paper and asks to "make slides / 做成 PPT / 讲一下这篇论文", especially when equations, numbers, figures, and citations must stay accurate and editable. NOT for marketing/pitch decks with no scholarly source, NOT for writing the paper itself, and NOT for stylized visual/image decks (use paper-slide-deck for those). No poster or grant-pitch workflow exists in this skill.

Overview

Publisherluwill
Repositoryresearch-skills
Skill namescholar-slides
Stars
863
Forks
100
Bundled files
101
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 101 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by luwill on GitHub. Read the source before you install it.

Installation

Install the Scholar Slides AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/luwill/research-skills/tree/main/scholar-slides
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/luwill/research-skills.git /tmp/research-skills
mkdir -p .claude/skills
cp -r /tmp/research-skills/scholar-slides .claude/skills/scholar-slides
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Scholar Slides in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Scholar Slides on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Scholar Slides is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

scholar-slides

Turn a research paper (or topic) into a fidelity-first academic slide deck. Academia inverts the priorities of a normal deck tool: source fidelity > polish, evidence > persuasion, editability > flash. Equations, tables, numbers, figures, and citations stay true text/vector and traceable to the source — never rasterized by an image model, never fabricated.

Design rationale and the survey it is built on live in docs/; build plan in IMPLEMENTATION_PLAN.md.

Locked stack (see docs/03-design-proposal.md §0)

  • Render backend: reveal.js + KaTeX → vector PDF (Playwright) + editable PPTX (parity-tested). Beamer later.
  • Default deck type: lab meeting / journal club (组会) — reading-first, high density.
  • Citations: Zotero-first (mcp__zotero__*), Crossref/arXiv/DOI fallback.
  • Language: bilingual, English-default.

The non-negotiable integrity gate (ALWAYS on — see references/integrity.md)

Apply at every stage; enforced at Checkpoint 3:

  • Never fabricate numbers, citations, or figures. Everything traces to the source digest.
  • Math/tables/citations stay vector/text — never an image model's pixels.
  • Reuse real figures (bbox-cropped + Figure N [cite]); a conceptual schematic may be redrawn only if labeled "redrawn, not from source".
  • Flag, don't invent: an unresolved figure/number/citation emits a visible [MISSING: …] / [UNVERIFIED: …] placeholder surfaced to the user — never a silent fill.
  • Provenance is mandatory: every reused asset carries its source; every claim is traceable.

Pipeline (7 stages, 3 human checkpoints)

INPUT → 1.INGEST/DIGEST → [CKPT-1] → 2.DECK-TYPE → 3.OUTLINE → [CKPT-2]
      → 4.PER-SLIDE SPEC → 5.RENDER → 6.SELF-REVIEW(QA) → [CKPT-3] → 7.EXPORT
  1. Ingest → typed digest — references/ingestion.md. Run scripts/prepare_source.py <pdf|arXiv id|arXiv URL> to build the digest-input bundle (text, figure/table inventory with bboxes, cropped figures), then synthesize the typed paper digest from it (grounded in the bundle, never invented). → [CKPT-1] confirm the digest with the user (contributions, headline result, figure→slide map, any FLAGGED asset).
  2. Deck-type & parameters — references/deck-types.md. Pick deck type (default 组会), audience, time budget → slide budget, density register, required archetypes.
  3. Narrative outline — references/narrative.md. Re-sequence the paper into talk order; action-title per slide; arc-tension check. → [CKPT-2] approve the story arc.
  4. Per-slide spec — references/slide-spec.md. One self-contained, on-disk spec per slide (layout, content, equations, figures, citations, speaker notes).
  5. Render — references/math.md, references/figures-tables.md, references/citations.md, references/charts.md, references/design-system.md, references/export.md. reveal.js + KaTeX; real tables; real bbox-cropped figures (furniture-stripped; one panel per slide for multi-panel); data-bound redrawn charts; Zotero/BibTeX citations. Pick the visual theme via meta.theme (journal-club default / conference).
  6. Self-review QA — references/qa-self-review.md + references/integrity.md, then the aesthetics loop in references/aesthetics-review.md. Integrity scan + static validator + render screenshots + narrative/timing checks; the render pass also measures geometry deterministically (canvas voids, figure-text projected below the 12px legibility floor). Then score the rendered pixels on the 6-dimension rubric (adversarial persona), write the scores to <deck>/aesthetics_report.json (schema in aesthetics-review.md), and rework any slide with a dimension ≤ 2 or total < 18 until the report's rework list is empty — the QA gate flags a missing report (P3) and an unspent rework list (P2). It also nudges on bullet-ratio and layout monotony. → [CKPT-3] truth sign-off: review every [MISSING]/[UNVERIFIED] and generated asset.
  7. Export — references/export.md. Vector PDF (projection) + speaker notes + editable PPTX; verify the PPTX preserves the spec natively with scripts/verify_pptx_parity.py (protects the minimal-manual-edits promise). Regress the whole corpus with node scripts/run_benchmark.mjs.

Read only the reference file for the stage you are in (progressive disclosure — never glob references/). Each stage reads the confirmed artifact of the prior stage — the digest (Stage 1, user-confirmed at CKPT-1) and then deck.json — rather than re-deriving from the paper.

Scripts (scripts/)

Ingestion — pipeline stage 1 (Python, via ./.venv/bin/python; deps in requirements.txt):

  • prepare_source.py — Stage 1 entry point: PDF/arXiv → digest-input bundle.
  • ingest_pdf.py — PyMuPDF text+layout extraction; arXiv-id detection.
  • detect_figures.py — figure/table caption inventory + bounding-box localization.
  • crop_figure.py — clean single-figure bbox crop (no neighbor columns).

Deck build & render — pipeline stage 5 (Node, via node; deps in package.json, npm install + npx playwright install chromium):

  • build_deck.mjs — render entry point: deck.json → self-contained reveal.js deck.
  • render_deck.mjs — deck → one-page-per-slide vector PDF (and per-slide PNGs for QA).
  • lib/ — layouts (registered-layout lock), math (KaTeX), table, figure, escape, qa.

Citations & QA — pipeline stages 5–6 (citations = Python; QA = Node):

  • fetch_bib.py — citation resolver (arXiv/DOI/Crossref) with order-sensitive title verification.
  • qa_report.mjs — CKPT-3 gate: integrity scan + validate_deck.mjs + verify_slides.mjs + timing.

Speaker notes (Node):

  • speaker_notes.mjs — deck.json → notes.md handout + bilingual talk-length estimate.
  • lib/notes.mjs — timing + handout pure logic; design system in assets/templates/themes/.

Export & charts — pipeline stage 7 (export = Node; charts = Python):

  • export_pptx.mjs — deck.json → editable PPTX (native text/tables/notes; figures+equations as images).
  • make_chart.py — chart spec → data-bound Okabe–Ito plot (values plotted verbatim).

Tests: ./.venv/bin/python -m pytest (Python) and node --test tests/*.test.mjs (Node — 逐个点名会静默漏跑新测试文件,用 glob).

Learnings: read before changing pipeline behavior

learnings/ holds post-incident notes with measured numbers (figure-size vertical budget, craft rules vs looks). Scan it before changing rendering, sizing, or QA behavior; append a dated note after any rework-worthy surprise.

Build status

The whole pipeline is implemented and validated on real papers. Not built: Beamer export, OCR for scanned PDFs, draft-deck ingestion; conference/答辩 deck types are authoring guidance in references/deck-types.md (no dedicated code). A deck is "done" only after the CKPT-3 human truth sign-off — the gate surfaces defects but cannot certify truth.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

and 41 more files.

Frequently asked questions

What does the Scholar Slides AI skill do?

Use when the user wants a research paper, arXiv/DOI link, or research topic turned into academic slides — journal club / lab meeting (组会汇报), thesis defense (答辩幻灯片), or conference / job talk (学术报告) — or shares a paper and asks to "make slides / 做成 PPT / 讲一下这篇论文", especially when equations, numbers, figures, and citations must stay accurate and editable. NOT for marketing/pitch decks with no scholarly source, NOT for writing the paper itself, and NOT for stylized visual/image decks (use paper-slide-deck for those). No poster or grant-pitch workflow exists in this skill.

Why use Scholar Slides on TypingMind?

Because you install it once and use it with any model. Scholar Slides is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Scholar Slides in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/luwill/research-skills/tree/main/scholar-slides. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Scholar Slides?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Scholar Slides?

As many as you like. As long as a model supports skills, you can use Scholar Slides with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Scholar Slides AI skill free?

It is published on GitHub by luwill. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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