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 addyosmani · Agent Tool · ★ 289
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is agentic-seo safe to install? View the security audit →
agentic-seo Audit your documentation and website for Agentic Engine Optimization (AEO): ensure your content is discoverable, parseable, and useful to AI coding agents. AI coding agents like Claude Code, Cursor, Cline, and Aider consume documentation fundamentally differently from humans. They issue single HTTP requests, strip HTML, count tokens, and either use your content as context or silently discard it. checks whether your documentation is ready for this new reality. Quick Start What It Checks runs 10 checks across 5 categories, scoring your site out of 100: Discovery (25 points) -
| Stars | 289 |
| Forks | 47 |
| Language | JavaScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 77.2650689639548/100 |
| Open Issues | 10 |
| Last Updated | 2026-04-12 |
| Created | 2026-04-11 |
| Platforms | node |
| Est. Tokens | ~7k |
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agentic-seo is Agentic SEO / Agentic Engine Optimization (AEO) tools. It is categorized as a Agent Tool with 289 GitHub stars.
agentic-seo is primarily written in JavaScript. It covers topics such as agent-seo, agentic-seo, agents.
You can find installation instructions and usage details in the agentic-seo GitHub repository at github.com/addyosmani/agentic-seo. The project has 289 stars and 47 forks, indicating an active community.
agentic-seo is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to agentic-seo on Agent Skills Hub include Agentic-SEO-Skill, backlink_skills, Seo-Promt-Master. 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: