vesta-mac-dist: Vesta macOS Distribution - Official releases and downloads.Vesta AI Chat Assistant for macOS

by scouzi1966 · MCP Server · ★ 84

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

Security audit verdict: SAFE · quality 64/100

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.

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Quick Facts

Stars84
Forks8
CategoryMCP Server
Quality Score64/100
Open Issues4
Last Updated2026-05-09
Created2025-09-08
Platformsmcp
Est. Tokens~712k

About vesta-mac-dist

Vesta AI Explorer for macOS - macOS Tahoe 26 vesta-mac.pages.dev Install with Homebrew (recommended) Direct Download Nightly via Homebrew Vesta is a multi-backend AI chat application for macOS that runs models locally on Apple Silicon.

Compatible Skills

These tools work well together with vesta-mac-dist for enhanced workflows:

  • SAM — semantic(0.35)+complementary+rare_topics+similar_pop (56%)
  • swift-ios-skills — semantic(0.25)+complementary+rare_topics+similar_pop (48%)
  • local-ai-mac — semantic(0.25)+complementary+rare_topics+similar_pop (48%)
  • unsloth-buddy — semantic(0.22)+complementary+rare_topics+similar_pop (47%)

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  • SAM by SyntheticAutonomicMind · ⭐ 132

    Synthetic Autonomic Mind - An AI assistant for everyone.

  • apple-rag-mcp by BingoWon · ⭐ 121

    Apple developer docs and WWDC transcripts with RAG retrieval and Jev relevance ranking.

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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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