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 tigerless-labs · Codex Skill · ★ 301
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is seo-ops safe to install? View the security audit →
seo-ops SEO Foundation Checks as an Agent Skill seo-ops checks a site's SEO foundation — the machine-readable structure search engines and AI retrieval need to fetch, read, index, and cite it. The only required input is a URL: the checker fetches the site like a crawler — no code access, no framework integration — and produces a pass/fail report with evidence. The whole repo is these three pieces: The checker script — give it a URL and it fetches like a crawler and reports against the C set. Zero LLM, no frontend-framework dependency: it only reads the live HTTP/HTML output, so React / Vue / Next / WordPress / plain stat
| Stars | 301 |
| Forks | 13 |
| Language | Python |
| Category | Codex Skill |
| Quality Score | 65.9800272809242/100 |
| Open Issues | 4 |
| Last Updated | 2026-09-01 |
| Created | 2026-08-25 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~14k |
These tools work well together with seo-ops for enhanced workflows:
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seo-ops is SEO foundation checks as an Agent Skill: give it a URL, get a crawler's-eye pass/fail report with evidence. 26 structural checks, zero LLM, deterministic.. It is categorized as a Codex Skill with 301 GitHub stars.
seo-ops is primarily written in Python. It covers topics such as agent-skills, claude-code, codex.
You can find installation instructions and usage details in the seo-ops GitHub repository at github.com/tigerless-labs/seo-ops. The project has 301 stars and 13 forks, indicating an active community.
The top alternatives to seo-ops on Agent Skills Hub include digital-marketing-pro, seobuild-onpage, on-page-agent. 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: