Claude SEO is an open-source Claude Code plugin that runs specialized agents and skills to audit websites across technical SEO, content quality, structured data, AI-search optimization, local and e-commerce SEO, and international SEO. SEO practitioners use it to produce prioritized recommendations based on primary-source guidance, with optional extensions for external data and crawling.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add AgriciDaniel/claude-seo --skill seo-sxogit clone --depth 1 https://github.com/AgriciDaniel/claude-seoWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/agricidaniel/claude-seo/seo-sxo)<a href="https://agentmods.dev/skills/agricidaniel/claude-seo/seo-sxo"><img src="https://agentmods.dev/badge/skills/agricidaniel/claude-seo/seo-sxo/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agricidaniel/claude-seo/seo-sxo"><img src="https://agentmods.dev/badge/skills/agricidaniel/claude-seo/seo-sxo.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00101 | $0.02444 |
| Opus 5 | $0.00051 | $0.01222 |
| Sonnet 5 | $0.00020 | $0.00489 |
| Haiku 4.5 | $0.00010 | $0.00244 |
Grade A, and why
seo-sxo scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Search Experience Optimization (SXO)
SXO bridges the gap between SEO (what Google rewards) and UX (what users need). Traditional SEO audits check technical health. SXO asks: "Does this page deserve to rank for this keyword based on what Google is actually rewarding in the SERP?"
Core Insight
A page can score 95/100 on technical SEO and still fail to rank because it is the wrong page type for the keyword. If Google shows 8 product pages and 2 comparison pages for your keyword, your blog post will never break through -- no matter how well-optimized it is.
Commands
| Command | Purpose |
|---|---|
/seo sxo <url> |
Full SXO analysis (auto-detect keyword from page) |
/seo sxo <url> <keyword> |
Full SXO analysis for a specific keyword |
/seo sxo wireframe <url> |
Generate IST/SOLL wireframe with concrete placeholders |
/seo sxo personas <url> |
Persona-only scoring (skip SERP analysis) |
Execution Pipeline
Step 1: Target Acquisition
- Fetch the target URL via
claude-seo run render_page.py <URL> --mode auto(SPA-aware and SSRF-safe) - Parse with
claude-seo run parse_html.py <URL>to extract: title, H1, meta description, headings hierarchy, word count, schema markup, CTAs, media elements - If no keyword provided, extract primary keyword from title tag + H1 overlap
- Validate keyword is non-empty before proceeding
Step 2: SERP Backwards Analysis
Read references/page-type-taxonomy.md for classification rules.
- Search Google for the target keyword (WebSearch)
- For each of the top 10 organic results, record:
- URL and domain authority tier (brand / niche authority / unknown)
- Page type (classify using taxonomy)
- Content format (long-form, listicle, how-to, comparison, tool, video)
- Word count estimate (from snippet length and page structure)
- Schema types present (from currently supported SERP features; exclude FAQ/HowTo)
- Media signals (video carousel, image pack, thumbnail presence)
- Record SERP features present:
- Featured snippet (paragraph / list / table / video)
- People Also Ask (extract all visible questions)
- Ads (top and bottom -- count and analyze ad copy themes)
- Related searches (extract all)
- Knowledge panel / local pack / shopping results
- AI Overview presence and source types
- Calculate SERP consensus:
- Dominant page type (>60% = strong consensus, 40-60% = mixed, <40% = fragmented)
- Content depth expectations (average word count tier)
- Schema expectation (most common structured data types)
- Media expectations (video required? images critical?)
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 255 lines · 101 tokens per session scan A 738567e0baa6
seo-sxo is a skill published in the GitHub repository AgriciDaniel/claude-seo (16,692 stars, last pushed 16d ago), licensed MIT. It adds 101 tokens to every session and 2,444 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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