by aqm857886159 · MCP Server · ★ 632
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/aqm857886159/Nomi.gitTaken from the project's README on GitHub. Check the security audit before running it.
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. Full audit →
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.
| Stars | 632 |
| Forks | 140 |
| Language | TypeScript |
| Category | MCP Server |
| License | AGPL-3.0 |
| Quality Score | 65/100 |
| Open Issues | 19 |
| Last Updated | 2026-11-07 |
| Created | 2026-05-04 |
| Platforms | claude-code, codex, mcp, node |
| Est. Tokens | ~15k |
Nomi Bring your own models. Let your AI assistant direct them. Nomi is an open-source desktop workbench for AI video. Connect any OpenAI-compatible endpoint or your local ComfyUI, then let Claude Code, Codex, or Cursor drive it over MCP — storyboard, references, generation, and an editable first cut on a real timeline. Your projects, prompts, and API keys stay on your machine. No account.
These tools work well together with Nomi for enhanced workflows:
Looking for a Nomi alternative? If you're comparing Nomi 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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The local-first sidebar AI agent for ComfyUI — runs on your own Claude OR ChatGPT subscription (no API keys, n
Official MiniMax Model Context Protocol (MCP) server that enables interaction with powerful Text to Speech, im
Generate video from text using AI
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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: