by SeldomQA · Agent Tool · ★ 77
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is auto-wing safe to install? View the security audit →
auto-wing auto-wing is a tool that uses LLM to assist automated testing, give your automated testing wings. auto-wing是一个利用LLM辅助自动化测试的工具, 为你的自动化测试插上AI的翅膀。 Features ⭐ 支持主流 、、库，支持和的操作。 ⭐ 支持多模型：、、、 和 。 ⭐ 支持多种操作：、、。 ⭐ 智能缓存管理： 减少LLM调用，增加执行速度。 ⭐ 无痛的集成到现有自动化项目（、）中。 Install 支持pip安装，。 Setting Env 方法一 申请LLM需要的key，在项目的根目录下创建文件。推荐和 ，一是便宜，二是方便。
pip install autowingTaken from the project's README on GitHub. Check the security audit before running it.
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 →
| Stars | 77 |
| Forks | 14 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 71.7398842110952/100 |
| Open Issues | 6 |
| Last Updated | 2026-08-30 |
| Created | 2025-01-22 |
| Platforms | python |
| Est. Tokens | ~14k |
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auto-wing is auto-wing is a tool that uses LLM to assist automated testing. It is categorized as a Agent Tool with 77 GitHub stars.
auto-wing is primarily written in Python. It covers topics such as deepseek, openai, playwright.
The README on GitHub (github.com/SeldomQA/auto-wing) gives: pip install autowing. Usage details are in the same README.
auto-wing is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to auto-wing on Agent Skills Hub include llmio, bridgic, oreilly-ai-agents. 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: