No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by takashiishida · Agent Tool · ★ 91
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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paper2slides Transform any arXiv papers into slides using LLMs! This tool is useful for quickly grasping the main ideas of research papers. Some examples of generated slides are in the Demo directory. Installation Python 3.10 or higher is required. To install: Clone this repository: Install the required Python packages: Ensure is installed and available in your system's PATH. Optionally check if you can compile the sample by . Check if is generated correctly. Optionally check and are working. Set up your OpenAI API key: Quick Start Once installed, generate slides from any arXiv paper: This will download the paper, generate slides, compile to PDF, and open the presentation automatically. Usage CLI The script provides a CLI interface with subcommands:
| Stars | 91 |
| Forks | 9 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 66.421567960198/100 |
| Open Issues | 1 |
| Last Updated | 2026-03-17 |
| Created | 2024-07-07 |
| Platforms | python |
| Est. Tokens | ~684k |
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paper2slides is Transform any arXiv papers into slides using LLMs. It is categorized as a Agent Tool with 91 GitHub stars.
paper2slides is primarily written in Python. It covers topics such as llm, llm-agent, openai.
You can find installation instructions and usage details in the paper2slides GitHub repository at github.com/takashiishida/paper2slides. The project has 91 stars and 9 forks, indicating an active community.
paper2slides is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to paper2slides on Agent Skills Hub include runprompt, open-assistant-api, palico-ai. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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