ovo-local-llm: A private Claude-Code-style coding agent for Apple Silicon

by ovoment · Agent Tool · ★ 105

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

How to install ovo-local-llm

git clone https://github.com/ovoment/ovo-local-llm.git

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 76/100

Flagged: sudo usage. 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

Stars105
Forks18
LanguageTypeScript
CategoryAgent Tool
LicenseMIT
Quality Score76/100
Open Issues9
Last Updated2026-04-22
Created2026-04-20
Platformsclaude-code, node
Est. Tokens~349k

About ovo-local-llm

🦉 A private Claude-Code-style coding agent for Apple Silicon Run chat, code, and local model workflows on-device — without shipping your work to the cloud. MLX-nativ

Compatible Skills

These tools work well together with ovo-local-llm for enhanced workflows:

  • Agent — semantic(0.15)+complementary+shared_fw(ollama,openai)+similar_pop+shared_platform (61%)
  • maclocal-api — semantic(0.40)+complementary+shared_fw(openai)+rare_topics+similar_pop (61%)

ovo-local-llm alternative? Top 6 similar tools

Looking for a ovo-local-llm alternative? If you're comparing ovo-local-llm with other agent tool 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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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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