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 Datus-ai · Agent Tool · ★ 1.8k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Datus-agent safe to install? View the security audit →
Datus — Open-Source Data Engineering Agent What is Datus? Datus is an open-source data engineering agent that builds evolvable context for your data system — turning natural language into accurate SQL through domain-aware reasoning, semantic search, and continuous learning. Data engineering is shifting from "building tables and pipelines" to "delivering scoped, domain-aware agents for analysts and business users." Datus makes that shift concrete. ![Datus Arch
| Stars | 1,824 |
| Forks | 262 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 68.8192750856795/100 |
| Open Issues | 43 |
| Last Updated | 2026-09-21 |
| Created | 2025-07-04 |
| Platforms | cli, python |
| Est. Tokens | ~16k |
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Datus-agent is The Future of Data Engineering — A CLI SQL client for the modern data stack, enabling AI-native context engineering for data.. It is categorized as a Agent Tool with 1.8k GitHub stars.
Datus-agent is primarily written in Python.
You can find installation instructions and usage details in the Datus-agent GitHub repository at github.com/Datus-ai/Datus-agent. The project has 1.8k stars and 262 forks, indicating an active community.
The top alternatives to Datus-agent on Agent Skills Hub include notebooklm-skill, agent-skills, excalidraw-diagram-skill. 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: