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 · LLM Plugin · ★ 2.4k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is code-interpreter safe to install? View the security audit →
-- What is E2B? E2B is an open-source infrastructure that allows you to run AI-generated code in secure isolated sandboxes in the cloud. To start and control sandboxes, use our JavaScript SDK or Python SDK. Run your first Sandbox Install SDK JavaScript / TypeScript npm i @e2b/code-interpr
| Stars | 2,400 |
| Forks | 226 |
| Language | Python |
| Category | LLM Plugin |
| License | Apache-2.0 |
| Quality Score | 67.0938266320734/100 |
| Open Issues | 21 |
| Last Updated | 2026-09-10 |
| Created | 2024-03-11 |
| Platforms | python |
| Est. Tokens | ~14k |
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code-interpreter is Python & JS/TS SDK for running AI-generated code/code interpreting in your AI app. It is categorized as a LLM Plugin with 2.4k GitHub stars.
code-interpreter is primarily written in Python. It covers topics such as ai, ai-data-analysis, anthropic.
You can find installation instructions and usage details in the code-interpreter GitHub repository at github.com/e2b-dev/code-interpreter. The project has 2.4k stars and 226 forks, indicating an active community.
code-interpreter is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to code-interpreter on Agent Skills Hub include cascadeflow, ax, griptape. 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: