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 ralforion · MCP Server · ★ 97
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 Define your metrics once in YAML. Let agents and BI tools query them without ever touching your schema. A semantic sidecar: it rides alongside the systems you already run instead of replacing them. <a href="https:/
| Stars | 97 |
| Forks | 9 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 62.8849595364862/100 |
| Last Updated | 2026-09-22 |
| Created | 2026-02-10 |
| Platforms | cli, mcp, python |
| Est. Tokens | ~23k |
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orionbelt-semantic-layer is Source-available semantic layer and sidecar for agentic AI and governed analytics. Compiles declarative YAML models into optimized SQL, KPIs, and semantic context across 8 SQL dialects.. It is categorized as a MCP Server with 97 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/ralforion/orionbelt-semantic-layer. The project has 97 stars and 9 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: