Vibe-Skills is a collection and routing system that helps AI agents discover, select, and coordinate specialized skills for completing tasks. It is intended for agents that need to organize workflows across many installed capabilities. The catalogue entries are skills and an agent belonging to this system.
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 foryourhealth111-pixel/Vibe-Skills --skill paper-2-webgit clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-SkillsWrote 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/foryourhealth111-pixel/vibe-skills/paper-2-web)<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/paper-2-web"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/paper-2-web/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/foryourhealth111-pixel/vibe-skills/paper-2-web"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/paper-2-web.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00040 | $0.03470 |
| Opus 5 | $0.00020 | $0.01735 |
| Sonnet 5 | $0.00008 | $0.00694 |
| Haiku 4.5 | $0.00004 | $0.00347 |
Grade A, and why
paper-2-web 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- paper-2-web — 89% identical, 16 lines differ
- paper-2-web — 88% identical, 36 lines differ
- paper-2-web — 86% identical, 39 lines differ
How it starts
The opening of the file, as written. The whole thing — 466 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper2All: Academic Paper Transformation Pipeline
Overview
This skill enables the transformation of academic papers into multiple promotional and presentation formats using the Paper2All autonomous pipeline. The system converts research papers (LaTeX or PDF) into three primary outputs:
- Paper2Web: Interactive, explorable academic homepages with layout-aware design
- Paper2Video: Professional presentation videos with narration, slides, and optional talking-head
- Paper2Poster: Print-ready conference posters with professional layouts
The pipeline uses LLM-powered content extraction, design generation, and iterative refinement to create high-quality outputs suitable for conferences, journals, preprint repositories, and academic promotion.
When to Use This Skill
Use this skill when:
- Creating conference materials: Posters, presentation videos, and companion websites for academic conferences
- Promoting research: Converting published papers or preprints into accessible, engaging web formats
- Preparing presentations: Generating video abstracts or full presentation videos from paper content
- Disseminating findings: Creating promotional materials for social media, lab websites, or institutional showcases
- Enhancing preprints: Adding interactive homepages to bioRxiv, arXiv, or other preprint submissions
- Batch processing: Generating promotional materials for multiple papers simultaneously
Trigger phrases:
- "Convert this paper to a website"
- "Generate a conference poster from my LaTeX paper"
- "Create a video presentation from this research"
- "Make an interactive homepage for my paper"
- "Transform my paper into promotional materials"
- "Generate a poster and video for my conference talk"
Boundary
This skill owns transformation of an existing paper into dissemination assets. It can reuse provided figures and paper assets, but it does not generate new scientific diagrams, run data analysis, write the original manuscript, or require a separate visual specialist as part of normal transformation.
What ships with it
5 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 · 466 lines · 40 tokens per session scan A f61c1318a0a2
paper-2-web is a skill published in the GitHub repository foryourhealth111-pixel/Vibe-Skills (3,252 stars, last pushed 12d ago), licensed Apache-2.0. It adds 40 tokens to every session and 3,470 once invoked, about $0.0002 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 2026-09-03.
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research-paper-writing
End-to-end pipeline for writing ML/AI research papers — from experiment design through analysis, drafting, revision, and submission. Covers NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Integrates automated experiment monitoring, statistical analysis, iterative writing, and citation verification.