faramesh-core: Governance-as-Code for AI agents

by faramesh · MCP Server · ★ 109

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 66/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

agent-governanceagent-governance-toolkitagent-runtimeagent-securityagentic-aiai-agentsautogencrewaideterministicdeterministic-ai

Quick Facts

Stars109
Forks19
LanguageGo
CategoryMCP Server
LicenseApache-2.0
Quality Score66/100
Open Issues10
Last Updated2026-07-21
Created2026-01-14
Platformsgo, mcp
Est. Tokens~16k

About faramesh-core

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

Compatible Skills

These tools work well together with faramesh-core for enhanced workflows:

  • kube-agentic-networking — semantic(0.20)+complementary+same_lang+similar_pop+shared_platform (52%)
  • DashClaw — semantic(0.24)+complementary+rare_topics+similar_pop (52%)

faramesh-core alternative? Top 6 similar tools

Looking for a faramesh-core alternative? If you're comparing faramesh-core with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

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    Remote approvals, policy checks, and execution evidence for unattended AI agents.

  • eval-view by hidai25 · ⭐ 139

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  • agents-shipgate by ThreeMoonsLab · ⭐ 90

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  • Aegis by Justin0504 · ⭐ 525

    Runtime policy enforcement for AI agents. Cryptographic audit trail, human-in-the-loop approvals, kill switch.

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

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