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 off-grid-ai · MCP Server · ★ 3.1k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is OGAM safe to install? View the security audit →
Off Grid AI The Swiss Army Knife of On-Device AI Chat. Generate images. Use tools. See. Listen. All on your phone or Mac. All offline. Zero data leaves your device. BUILT BY <source media="(pref
| Stars | 3,092 |
| Forks | 297 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 64.5684303881146/100 |
| Open Issues | 150 |
| Last Updated | 2026-09-14 |
| Created | 2026-01-29 |
| Platforms | mcp, node |
| Est. Tokens | ~17k |
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OGAM is The Swiss Army Knife of Offline AI. Chat, see, speak, and generate images on your phone or Mac — GGUF LLMs, vision, Whisper speech-to-text, Stable Diffusion, tool calling, and local-network servers. R. It is categorized as a MCP Server with 3.1k GitHub stars.
OGAM is primarily written in TypeScript. It covers topics such as android, edge-ai, gguf.
You can find installation instructions and usage details in the OGAM GitHub repository at github.com/off-grid-ai/OGAM. The project has 3.1k stars and 297 forks, indicating an active community.
OGAM is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to OGAM on Agent Skills Hub include vllm-mlx, Box, mobile-mcp. 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: