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 AgriciDaniel · MCP Server · ★ 707
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is codex-seo safe to install? View the security audit →
Watch on YouTube Codex SEO - SEO Audit Skill Suite for Codex Codex-first SEO analysis suite with 1 orchestrator skill, 26 specialist workflows, 24 TOML agent profiles, MCP/API extensions, deterministic headless runners, and premium audit report generation. Codex SEO is a Codex-native port of , synchronized to upstream at and adapted for Codex skills, Codex plugins, TOML agents, shared cache artifacts, and repeatable local/API execution. It covers technical SEO, on-page analysis, content quality, E-E-A-T, schema markup, ima
| Stars | 707 |
| Forks | 96 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 70.6848067618086/100 |
| Open Issues | 26 |
| Last Updated | 2026-09-11 |
| Created | 2026-04-27 |
| Platforms | browser, cli, codex, gemini, mcp, python |
| Est. Tokens | ~19k |
These tools work well together with codex-seo for enhanced workflows:
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codex-seo is Codex-first SEO skill suite. 26 workflows, 24 TOML agents, DataForSEO/Gemini/Google/Firecrawl integrations, GEO/AEO, CWV, schema, backlinks, local/maps, and deterministic reports.. It is categorized as a MCP Server with 707 GitHub stars.
codex-seo is primarily written in Python. It covers topics such as ai-search, ai-seo, automation.
You can find installation instructions and usage details in the codex-seo GitHub repository at github.com/AgriciDaniel/codex-seo. The project has 707 stars and 96 forks, indicating an active community.
The top alternatives to codex-seo on Agent Skills Hub include geo-optimizer-skill, eGEOagents, open-seo-mcp-skills. 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: