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 VibeTensor · MCP Server · ★ 815
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is attestix safe to install? View the security audit →
Attestix Compliance Automation for AI Agents <img src="https://img.shields.io/pypi/pyversions/attestix?color=4f46e5&style=flat-square" alt="P
| Stars | 815 |
| Forks | 83 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 70.2185720297894/100 |
| Open Issues | 22 |
| Last Updated | 2026-09-29 |
| Created | 2026-02-16 |
| Platforms | mcp, python |
| Est. Tokens | ~19k |
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attestix is Attestix - Attestation Infrastructure for AI Agents. DID-based agent identity, W3C Verifiable Credentials, EU AI Act compliance layer, delegation chains, and reputation scoring. 47 MCP tools across 9 . It is categorized as a MCP Server with 815 GitHub stars.
attestix is primarily written in Python. It covers topics such as agent-identity, ai-agents, attestation.
You can find installation instructions and usage details in the attestix GitHub repository at github.com/VibeTensor/attestix. The project has 815 stars and 83 forks, indicating an active community.
attestix is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to attestix on Agent Skills Hub include asqav-sdk, linkedin-mcp-server, tradememory-protocol. 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: