Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add inhyeoklee/paper2slides-skill --skill skillgit clone --depth 1 https://github.com/inhyeoklee/paper2slides-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/inhyeoklee/paper2slides-skill/skill)<a href="https://agentmods.dev/skills/inhyeoklee/paper2slides-skill/skill"><img src="https://agentmods.dev/badge/skills/inhyeoklee/paper2slides-skill/skill/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/inhyeoklee/paper2slides-skill/skill"><img src="https://agentmods.dev/badge/skills/inhyeoklee/paper2slides-skill/skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00028 | $0.04992 |
| Opus 5 | $0.00014 | $0.02496 |
| Sonnet 5 | $0.00006 | $0.00998 |
| Haiku 4.5 | $0.00003 | $0.00499 |
Grade A, and why
paper2slides scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 469 lines — stays where its author put it; the contents beside it link to each section on GitHub.
paper2slides — Agent-Driven Journal Club Presentations
Overview
This skill transforms a scientific paper PDF into a show-ready, curated Reveal.js presentation with speaker notes. It combines two layers:
- Mechanical layer — the
paper2slidesPython library extracts figures, segments panels, and scaffolds a raw Reveal.js deck. - Intelligence layer — you, the agent, read the paper, understand the science, curate the slide deck, write titles/captions, and compose speaker notes.
The mechanical extraction alone produces a figure dump. The agent's job is to turn that into a coherent, PhD-level journal club talk.
Prerequisites
The paper2slides library must be installed. If the library source is available locally:
cd <path_to_paper2slides_library> && pip install -e .
Otherwise install from the packaged distribution:
pip install paper2slides
Required Python packages (auto-installed): pymupdf, pillow, numpy, jinja2, click
Workflow (5 Phases)
Phase 1: Extract & Segment (Mechanical)
Run the paper2slides CLI to extract figures and segment panels:
paper2slides "<path/to/paper.pdf>" -o "<output_dir>"
This produces:
<output_dir>/assets/img/panels/fig1a.png, fig1b.png, ...— individual panel images<output_dir>/index.html— a raw, uncurated Reveal.js deck (figure dump)<output_dir>/assets/css/style.css— slate + accented theme with expanded font choices, high-contrast body text, per-aim color tokens, feature-family badge colors, and ready-made component classes for aim-flow / info-row-3 / taxonomy-grid / compare-grid / criteria-grid / badges / schematics
Verify: Check the terminal output for the number of figures and panels extracted. If zero figures found, the PDF may use vector graphics — consider alternatives like screenshots.
Phase 2: Read & Comprehend the Paper
Use PyMuPDF to extract and read the paper text programmatically:
import fitz
doc = fitz.open("<path/to/paper.pdf>")
for i in range(len(doc)):
page = doc[i]
text = page.get_text('text')
print(f'=== PAGE {i+1} ===')
print(text)
doc.close()
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 469 lines · 28 tokens per session scan A 1590a9a0f5b6
paper2slides is a skill published in the GitHub repository inhyeoklee/paper2slides-skill (5 stars, last pushed 3mo ago), licensed MIT. It adds 28 tokens to every session and 4,992 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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