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 ralfbecher · MCP Server · ★ 56
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is orionbelt-semantic-layer safe to install? View the security audit →
OrionBelt Semantic Layer and Sidecar An Open Source Semantic Sidecar for Agentic AI, Analytics, Quality and Governance Systems. Inject governed semantics into systems that never had them. [](https://pypi.org/project/orionbelt-sem
| Stars | 56 |
| Forks | 7 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 63.9564672075354/100 |
| Open Issues | 2 |
| Last Updated | 2026-06-18 |
| Created | 2026-02-10 |
| Platforms | cli, mcp, python |
| Est. Tokens | ~20k |
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orionbelt-semantic-layer is Open-source Semantic Sidecar for AI, analytics, and governed data systems. Compiles declarative YAML models into optimized SQL, semantic context, KPIs, and DQ rules.. It is categorized as a MCP Server with 56 GitHub stars.
orionbelt-semantic-layer is primarily written in Python. It covers topics such as agentic-ai, analytics-as-code, bigquery.
You can find installation instructions and usage details in the orionbelt-semantic-layer GitHub repository at github.com/ralfbecher/orionbelt-semantic-layer. The project has 56 stars and 7 forks, indicating an active community.
The top alternatives to orionbelt-semantic-layer on Agent Skills Hub include orionbelt-semantic-layer, sidemantic, bonnard-cli. 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: