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Paper2slides

Community
QuZhan51496
paper2slides

Turn an academic paper PDF into a presentation deck (.pptx) end-to-end. Use this skill whenever the user wants to "make slides from a paper", "generate a deck from this PDF", "make a PPT from this paper", "generate slides from a PDF document", "make a deck from a research paper", or supplies a research paper PDF and asks for a .pptx out. Trigger even when the user only says "deck this paper" or "summarize as slides". This is the dedicated, self-contained skill for academic-paper-to-deck flows.

Overview

PublisherQuZhan51496
Repositorypaper2anything
Skill namepaper2slides
Stars
471
Forks
11
Bundled files
15
LicenseApache-2.0
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.

  • 15 bundled files

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

  • Open source

    Published by QuZhan51496 on GitHub. Read the source before you install it.

Installation

Install the Paper2slides 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/QuZhan51496/paper2anything/tree/main/paper2slides
  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/QuZhan51496/paper2anything.git /tmp/paper2anything
mkdir -p .claude/skills
cp -r /tmp/paper2anything/paper2slides .claude/skills/paper2slides
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Paper2slides 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 Paper2slides 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 Paper2slides 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.

paper2slides

Turn an academic paper PDF into a presentation-ready .pptx. This is a conductor skill: you make the editorial and design judgments, while the mechanical steps are handled by this skill's own self-contained scripts — PDF parsing via the MinerU cloud API (scripts/parse_pdf.py) and .pptx rendering via a PptxGenJS bridge (scripts/render_pptx.py). It orchestrates a "paper → outline → spec → render → QA" pipeline and depends on no other skill.

Quick Reference

StageInputOutputOwner
0.5. configureuser dialogue<workdir>/config.jsonyou (AskUserQuestion to confirm three items: length tier + whether to run visual QA + color scheme)
1. extractpaper.pdfpaper_meta.json + figures_index.json + figures/ + pages/ + equations + hi-res figure/table crops (produced in one shot by the MinerU cloud API)scripts/parse_pdf.py
2. outlinepaper_meta.jsonslide_outline.jsonyou (per references/outline-heuristics.md)
3. specslide_outline.json + figuresslide_spec.jsonyou (per references/design-style.md)
4. renderslide_spec.jsonoutput.pptxscripts/render_pptx.py (PptxGenJS bridge)
5. qaoutput.pptxpass / fail + fix listcontent QA always runs; visual QA is gated by Stage 0.5's config.json/visual_qa

See references/pipeline.md for the detailed per-stage protocol.

Invocation Contract

Invocation form:

/paper2slides <paper.pdf> [output.pptx] [--from-stage <name>] [--force]
  • output.pptx omitted → <paper-dir>/<paper-stem>_slides/<paper-stem>.pptx; on a name collision the directory auto-appends _v2 _v3
  • Intermediate artifacts land in <paper-dir>/.paper2anything/slides/<paper-stem>/; when the paper directory is read-only, fall back to ~/.cache/paper2anything/slides/

Python environment: all scripts run in the paper2anything conda environment. Command prefix conda run -n paper2anything --no-capture-output python -m scripts.<name> ... (the prefix can be omitted once conda activate paper2anything is in effect). Every -m scripts.<name> must be run from this skill's directory (the parent of scripts/), otherwise you get No module named 'scripts'; the prefix-omission convention is in references/pipeline.md §General Conventions.

The first step is always to resolve the workspace:

bash
conda run -n paper2anything --no-capture-output python -m scripts.workdir resolve \
    <paper.pdf> [--output <out.pptx>] --ensure

The returned JSON contains all the named paths (paper_meta_path, slide_outline_path, slide_spec_path, figures_dir, ...) and each stage's completion status. Every later stage references the paths in this JSON — do not assemble paths yourself; the rules are centralized in scripts/workdir.py.

Re-run semantics:

FlagMeaning
defaultalready-completed stages are skipped (judged by whether the output files exist)
--forceignore all markers and run everything
--from-stage <name>re-run starting from the named stage (the entry point for the long-lived "interactive mode")

<name> ∈ {configure, extract, outline, spec, render, qa}. To go through the three pre-flight questions again, use --from-stage configure (overwrites the old config.json).

Pipeline

Execute in the order below. Each stage has a "completion test" — once its output file appears the stage counts as done, and is auto-skipped on re-run. Each stage in this section writes only three things: (a) the minimal action to do (including the command to type), (b) the single most error-prone pitfall, and (c) a pointer to the corresponding Stage in references/pipeline.md. The full protocol, prerequisites, common errors, and edge cases all live in pipeline.md; this section does not restate them.

Stage 0.5 — Configure (you, AskUserQuestion to confirm three items)

After Stage 0 resolves the workspace and before Stage 1, use [AskUserQuestion] to confirm three items with the user, and Write the answers to the Stage 0 JSON's config_path (<workdir>/config.json):

  1. deck_length: concise / standard / detailed / auto (no page-count target, recommended)
  2. visual_qa: true (default, adds the soffice→jpg→subagent visual loop) / false (run only the cheap content QA)
  3. color_scheme: auto (default, Stage 3 matches a palette to the paper's character) / custom (the user describes a preference in one line, stored in config and parsed by Stage 3)
  • Reuse = skip: when config.json already exists and neither --from-stage configure nor --force is given, don't ask again — reuse the last configuration.
  • When the user's initial request already states a preference, set the corresponding item as the AskUserQuestion default (still show it for confirmation).
  • The full option table, the deck_length page-count band mapping, and the prefill / downstream-consumption details are in references/pipeline.md §Stage 0.5; the config.json schema is in references/schemas.md.

Stage 1 — Extract (script)

Use the MinerU cloud API (requires MINERU_API_TOKEN, configured uniformly in the paper2anything package-root .env; with no token it errors out immediately):

bash
set -a; source <paper2anything package root>/.env; set +a   # export the unified .env (includes MINERU_API_TOKEN)
conda run -n paper2anything --no-capture-output python -m scripts.parse_pdf <paper.pdf>

Produces paper_meta.json + figures_index.json + pages/ + hi-res crops in one shot (the structured metadata comes directly from MinerU).

For --dpi tuning (default 300) and the known imperfections of MinerU parsing (e.g. the bbox sometimes pins the y start onto a subfigure caption), see references/pipeline.md §Stage 1.

Pitfall: before entering Stage 2 you must run the 4 checks at the end of references/schemas.md (title/authors/same-kind merge/missing key kind) to verify paper_meta.json; the check results are not written back to paper_meta.json, but are reflected directly in the Stage 2 outline.

Stage 2 — Outline (you)

Input paper_meta.json + figures_index.json + config.json → output slide_outline.json (schema in references/schemas.md). Per references/outline-heuristics.md, set roles and order, and write each slide's title/bullets/figure_ref/speaker_notes. Pitfall: read config.json/deck_length first — auto does not constrain the slide count; a non-auto value is a soft target for outline granularity, and you must not cut core narrative roles just to hit a number (details in outline-heuristics.md).

After writing, validate that the JSON is well-formed with Python:

bash
conda run -n paper2anything --no-capture-output python -c \
    "import json,sys; json.load(open(sys.argv[1])); print('ok')" \
    <workdir>/slide_outline.json

For the full protocol and common errors, see references/pipeline.md §Stage 2.

Stage 3 — Spec (you)

Input slide_outline.json + figures_index.json + figures/ + pages/ → output slide_spec.json. Per references/design-style.md, choose palette/fonts/layout_kind (avoiding consecutive repeats), and translate the content into elements. Pitfall: every number and term must have a source in paper_meta / figures_index — do not fabricate.

When you need an icon use kind:"icon"; for naming see the "Icons" section of references/pptxgenjs.md, and for the schema see the icon element in references/schemas.md.

When you need to crop a region from a full paper page:

bash
conda run -n paper2anything --no-capture-output python -m scripts.page_screenshot \
    <workdir> <page> <x> <y> <w> <h>

bbox uses relative ratios 0..1; fill the output relative path into the corresponding image element's path. Hard gate for cropping: the first call's bbox must be value-for-value equal to figures_index.json/captions[i].bbox, and you may not eyeball the full page before cropping the first version — for the full baseline and the QA re-crop loop see references/design-style.md §3 (skipping the first cut and going straight to eyeballing = violating §3). The Stage 3 key constraints (coordinates ≤ canvas / margin:0 etc.) are in references/pipeline.md §Stage 3.

Stage 4 — Render (script)

bash
conda run -n paper2anything --no-capture-output python -m scripts.render_pptx \
    <workdir>/slide_spec.json <workdir>/output.pptx

The output is <workdir>/output.pptx; after a successful render, copy it to the final output_path given by Stage 0 (first mkdir -p its parent directory <paper-stem>_slides/). Pitfall: it depends on node+pptxgenjs (installed globally); if node is not on PATH the script errors. On failure, first --dry-run to generate only render/build.js and locate the spec problem. For prerequisites and common errors see references/pipeline.md §Stage 4.

Stage 5 — QA

Execute per the QA section below: content QA always runs, and visual QA is gated by config.json/visual_qa.

Defaults & Errors

Default behavior

InputDefault
output.pptx omitted<paper-dir>/<paper-stem>_slides/ (containing <paper-stem>.pptx); on a name collision the directory appends _v2, _v3
work directory not writable in the paper directoryfall back to ~/.cache/paper2anything/slides/<paper-stem>-<hash12>/
re-run on the same paperalready-completed stages (output files already exist) are auto-skipped
config.json already existsStage 0.5 skips the questions and reuses the last configuration; to change config use --from-stage configure
Stage 0.5 not asked / visual_qa defaulteddeck_length=auto, visual_qa=true (run visual QA)
--from-stage <N>force a re-run from the named stage, without checking outputs
--forceforce a re-run of all stages (rare, only on a schema upgrade)

Error-Recovery Quick Reference

Only the few classes that need your judgment / routing are listed (all technical recovery is in pipeline.md):

SymptomHandling
Stage 1 section count < 5 or > 15you manually add / merge during the Stage 2 check
Stage 5 reports "card bottom half empty / unbalanced columns / bottom whitespace"not soft — fix per the three-lever model in references/design-style.md "Principles for Fixing QA Issues" (adjust text amount > adjust bullet spacing > adjust image size, stackable), then --from-stage render to re-run
user/QA reports "leader markers not aligned with text"per references/design-style.md "Visual-Richness Recommendations" item A, batch-reset icon_y to the alignment formula + a final self-check, then --from-stage render to re-run
skill triggered but the user only wants to "read the PDF"mis-trigger — tell the user this skill builds a slide deck and ask whether to proceed; do not continue with paper2slides if they only want to read the PDF

For the full error recovery (Stage 1/4 technical, annotation green box, dpi, figure_ref, etc.) see the "Error-Recovery Quick Reference" section of references/pipeline.md.

QA

Apply the QA loop in references/qa.md (the self-contained "Verification Loop" — content QA

  • visual subagent review). Read config.json/visual_qa first; the key points:
  • content QA always runs: markitdown checks placeholders / number consistency / bullets not lifted from the abstract / no leftover placeholder in the title.
  • visual QA runs only when config.json/visual_qa == true: soffice→pdf→jpg→dispatch a single subagent to batch-review.
  • After fixing issues, edit slide_spec.json then --from-stage render to fully re-render before QA; the recheck rounds narrow per the Verification Loop — round 1 covers the full deck, from round 2 on look only at the pages flagged last round ∪ the pages changed this round, with a final full pass over the whole deck before convergence.
  • The final report must state whether visual QA was run; when skipped, note "visual QA was skipped per config; to change config use --from-stage configure then --from-stage qa".
  • Whitespace / unbalanced columns / misaligned leader markers are not soft, they are hard issues that must be fixed — this is where this skill most often misjudges; don't wave them off as "soft" when reviewing the subagent's report.

For the verification loop and the base visual-subagent prompt template, see references/qa.md; for the full A/B protocol and the qa_log.json structure, see references/pipeline.md §Stage 5; for the visual subagent prompt's two added paragraphs, the three-lever fix model, and the recheck-narrowing details, see the "Visual Subagent Prompt for QA" + "Principles for Fixing QA Issues" sections of references/design-style.md.

Where to Look When Stuck

This skill is self-contained — everything you need is under references/ in this directory:

ConfusionGo here
MinerU parsing anomalies (missing figure / garbled text / misaligned table / lost formula)references/pipeline.md §Stage 1 + scripts/lib/mineru_client.py (token, model_version, zip-download retry)
PPT visual design, color palettes, layout choices, taboosthe "Design fundamentals" + "Avoid" sections of references/design-style.md
PptxGenJS API usage, gotchas, icon generationreferences/pptxgenjs.md (includes the academic icon-name table)
QA flow and the visual-subagent promptreferences/qa.md
Detailed JSON schema fields for each stage's output (including config.json)references/schemas.md
How sections map to slide rolesreferences/outline-heuristics.md
Matching color/layout to the paper's scenarioreferences/design-style.md

Bundled files

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

Frequently asked questions

What does the Paper2slides AI skill do?

Turn an academic paper PDF into a presentation deck (.pptx) end-to-end. Use this skill whenever the user wants to "make slides from a paper", "generate a deck from this PDF", "make a PPT from this paper", "generate slides from a PDF document", "make a deck from a research paper", or supplies a research paper PDF and asks for a .pptx out. Trigger even when the user only says "deck this paper" or "summarize as slides". This is the dedicated, self-contained skill for academic-paper-to-deck flows.

Why use Paper2slides on TypingMind?

Because you install it once and use it with any model. Paper2slides 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 Paper2slides in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/QuZhan51496/paper2anything/tree/main/paper2slides. 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 Paper2slides?

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 Paper2slides?

As many as you like. As long as a model supports skills, you can use Paper2slides with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Paper2slides AI skill free?

Yes. It is published on GitHub by QuZhan51496 under the Apache-2.0 license. 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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