by xingyaoww · Agent Tool · ★ 1.7k
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git clone https://github.com/xingyaoww/code-actTaken from the project's README on GitHub. Check the security audit before running it.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 1,704 |
| Forks | 146 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 67/100 |
| Open Issues | 15 |
| Last Updated | 2024-05-23 |
| Created | 2024-01-13 |
| Platforms | python |
| Est. Tokens | ~1639k |
Executable Code Actions Elicit Better LLM Agents 📃 Paper • 🤗 Data (CodeActInstruct) • 🤗 Model (CodeActAgent-Mistral-7b-v0.1) • 🤖 Chat with CodeActAgent! We propose to use executable code to consolidate LLM agents’ actions into a unified action space (CodeAct).
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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: