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 mverab · MCP Server · ★ 194
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is eGEOagents safe to install? View the security audit →
🌍 E-GEO Generative Engine Optimization for AI-Powered Search 🚀 One command. Premium results. Rank higher in ChatGPT, Perplexity, Claude, and Gemini. 📖 Docs • [🐛 Issues](https://github.com/mverab/eGEOagents/issues
| Stars | 194 |
| Forks | 56 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 64.5322287352084/100 |
| Open Issues | 4 |
| Last Updated | 2026-09-26 |
| Created | 2026-01-12 |
| Platforms | claude-code, cli, gemini, mcp, python |
| Est. Tokens | ~16k |
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eGEOagents is Open-source Generative Engine Optimization (GEO) & Answer Engine Optimization (AEO) toolkit — optimize content to rank in ChatGPT, Perplexity, Gemini & Claude. AI SEO / LLM SEO. CLI, Claude Code, MCP.. It is categorized as a MCP Server with 194 GitHub stars.
eGEOagents is primarily written in Python. It covers topics such as aeo, ai-search-optimization, ai-seo.
You can find installation instructions and usage details in the eGEOagents GitHub repository at github.com/mverab/eGEOagents. The project has 194 stars and 56 forks, indicating an active community.
eGEOagents is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to eGEOagents on Agent Skills Hub include geo-optimizer-skill, open, ansvisor. 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: