by takashiishida · Agent Tool · ★ 94
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/takashiishida/paper2slides.git
Taken from the project's README on GitHub.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 94 |
| Forks | 9 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 66/100 |
| Open Issues | 1 |
| Last Updated | 2026-03-17 |
| Created | 2024-07-07 |
| Platforms | python |
| Est. Tokens | ~684k |
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.
Looking for a paper2slides alternative? If you're comparing paper2slides with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Run LLM prompts from your shell
Hands-on tutorials for building AI agents from scratch. Learn LLM APIs, prompt engineering, tool calling, and
The Open Assistant API is a ready-to-use, open-source, self-hosted agent/gpts orchestration creation framework
Build, Improve Performance, and Productionize your LLM Application with an Integrated Framework
Open-source versioning, tracing, and annotation tooling.
An Automated AI-Powered Prompt Optimization Framework
Explore other popular agent tool tools:
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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.
Sources & who's responsible: