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 e2b-dev · Claude Skill · ★ 6.4k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is fragments safe to install? View the security audit →
Fragments by E2B This is an open-source version of apps like Anthropic's Claude Artifacts, Vercel v0, or GPT Engineer. Powered by the E2B SDK. → Try on fragments.e2b.dev Features Based on Next.js 14 (App Router, Server Actions), shadcn/ui, TailwindCSS, Vercel AI SDK. Uses the E2B SDK by E2B to securely execute code generated by AI. Streaming in the UI. Can install and use any package from npm, pip. Supported stacks (add your own): 🔸 Python interpreter 🔸 Next.js 🔸 Vue.js 🔸 Streamlit 🔸 Gradio Supported LLM Providers (add your own): 🔸 OpenAI 🔸 Anthropic 🔸 Google AI 🔸 Mistral 🔸 Groq 🔸 Fireworks 🔸 Together AI 🔸 Ollama Integrates with Morph Apply model for token efficient, accurate and faster code editing. Make sure to give us a star! Get started
| Stars | 6,378 |
| Forks | 862 |
| Language | TypeScript |
| Category | Claude Skill |
| License | Apache-2.0 |
| Quality Score | 67.2725905275633/100 |
| Open Issues | 21 |
| Last Updated | 2026-09-19 |
| Created | 2024-07-10 |
| Platforms | claude-code, node |
| Est. Tokens | ~15k |
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fragments is Open-source Next.js template for building apps that are fully generated by AI. By E2B.. It is categorized as a Claude Skill with 6.4k GitHub stars.
fragments is primarily written in TypeScript. It covers topics such as ai, ai-code-generation, anthropic.
You can find installation instructions and usage details in the fragments GitHub repository at github.com/e2b-dev/fragments. The project has 6.4k stars and 862 forks, indicating an active community.
fragments is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to fragments on Agent Skills Hub include repomix, ai, ax. 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.
Sources & who's responsible: