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 davialabs · Agent Tool · ★ 1.7k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is davia safe to install? View the security audit →
What is Davia? Davia is an open-source tool designed for AI coding agents to generate interactive internal documentation for your codebase. When your AI coding agent uses Davia, it writes documentation files locally with interactive visualizations and editable whiteboards that you can edit in a Notion-like platform or locally in your IDE. <img src="https://storage.goo
| Stars | 1,654 |
| Forks | 121 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 67.4044862229861/100 |
| Open Issues | 6 |
| Last Updated | 2026-01-19 |
| Created | 2025-11-05 |
| Platforms | node |
| Est. Tokens | ~88k |
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davia is Interactive, editable docs designed for coding agents. It is categorized as a Agent Tool with 1.7k GitHub stars.
davia is primarily written in TypeScript. It covers topics such as ai-tools, documentation, documentation-generator.
You can find installation instructions and usage details in the davia GitHub repository at github.com/davialabs/davia. The project has 1.7k stars and 121 forks, indicating an active community.
davia is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to davia on Agent Skills Hub include llm-wiki-agent, obsidian-wiki, readme-ai. 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: