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 fabio-rovai · MCP Server · ★ 531
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is open-ontologies safe to install? View the security audit →
Open Ontologies A Terraforming MCP for Knowledge Graphs Validate, classify, and govern AI-generated ontologies. Written in Rust. Ships as a single binary. Quick Start · Studio · Benchmarks · IES · Tools · <a href
| Stars | 531 |
| Forks | 72 |
| Language | Rust |
| Category | MCP Server |
| License | MIT |
| Quality Score | 70.5358550104284/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-22 |
| Created | 2026-03-09 |
| Platforms | mcp, rust |
| Est. Tokens | ~32k |
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open-ontologies is Plan, apply and roll back changes to a production ontology, with a blast radius report and a proof an auditor can re-check.. It is categorized as a MCP Server with 531 GitHub stars.
open-ontologies is primarily written in Rust. It covers topics such as ai-native, description-logics, formal-verification.
You can find installation instructions and usage details in the open-ontologies GitHub repository at github.com/fabio-rovai/open-ontologies. The project has 531 stars and 72 forks, indicating an active community.
open-ontologies is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to open-ontologies on Agent Skills Hub include ontosphere, TrustGraph, jacobian. 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: