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 RUC-NLPIR · Agent Tool · ★ 191
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is EnvScaler safe to install? View the security audit →
EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis 中文 | English If you like our project, please give us a star ⭐ on GitHub. We greatly appreciate your support. 🎬 De
| Stars | 191 |
| Forks | 14 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 69.4330385962127/100 |
| Open Issues | 3 |
| Last Updated | 2026-09-03 |
| Created | 2026-01-09 |
| Platforms | python |
| Est. Tokens | ~15k |
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EnvScaler is The official implementation of "EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis".. It is categorized as a Agent Tool with 191 GitHub stars.
EnvScaler is primarily written in Python. It covers topics such as agent, llms, tool-use.
You can find installation instructions and usage details in the EnvScaler GitHub repository at github.com/RUC-NLPIR/EnvScaler. The project has 191 stars and 14 forks, indicating an active community.
EnvScaler is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to EnvScaler on Agent Skills Hub include VT.ai, llm-tool-collection, kani. 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: