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 faramesh · MCP Server · ★ 100
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is faramesh-core safe to install? View the security audit →
Every agent tool call is a policy decision. Declare permissions in . A local daemon permits, defers, or denies each tool call before it runs. Decisions are hash-chained in a WAL. No SDK lock-in. No cloud required. <img alt="Quickstart" src="https://img.s
| Stars | 100 |
| Forks | 19 |
| Language | Go |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 66.1214279963307/100 |
| Open Issues | 10 |
| Last Updated | 2026-07-21 |
| Created | 2026-01-14 |
| Platforms | go, mcp |
| Est. Tokens | ~16k |
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faramesh-core is Governance-as-Code for AI agents. Declarative constraints with deterministic enforcement. Provisioning Identity, Tool-based rules, Brokering Credentials & Ensuring safe deployment. It is categorized as a MCP Server with 100 GitHub stars.
faramesh-core is primarily written in Go. It covers topics such as agent-governance, agent-governance-toolkit, agent-runtime.
You can find installation instructions and usage details in the faramesh-core GitHub repository at github.com/faramesh/faramesh-core. The project has 100 stars and 19 forks, indicating an active community.
faramesh-core is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to faramesh-core on Agent Skills Hub include Sponsio, DashClaw, oreilly-ai-agents. 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: