seo-sxo

seo-sxo is an agent for Claude Code from AgriciDaniel/claude-seo. It costs 47 tokens per session (1,199 once invoked), scanned A, original, MIT.

A search-ranking analyst that compares a web page with the pages Google shows for a search term. It looks at search intent, page format, content, and signals from the search results.

In plain words
What is it for?
Use it to inspect a target page, analyze the top search results, classify page types, derive user needs, and score the page from different reader perspectives.
Why use it?
A page can follow general SEO advice and still rank poorly if it does not match what searchers and Google expect. This helps identify that mismatch and possible reasons for weak visibility.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: model in frontmatter.

Part of the claude-seo plugin — 31 skills, 18 agents, 1 hook shipped together

Good fit Use it to inspect a target page, analyze the top search results, classify page types, derive user needs, and score the page from different reader perspectives.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/agricidaniel/claude-seo/seo-sxo
About the project

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.

AgriciDaniel/claude-seo · 16,621 stars · on GitHub · claude-seo.md

Install

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.

Clone the repo
git clone --depth 1 https://github.com/AgriciDaniel/claude-seo

Made for: Claude Code.

Or install claude-seo, the plugin that ships this one along with the rest of its 31 skills, 18 agents, 1 hook.

Wrote 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.

agentmods badge for seo-sxo

README.md
[![agentmods](https://agentmods.dev/badge/agents/agricidaniel/claude-seo/seo-sxo/github.svg)](https://agentmods.dev/agents/agricidaniel/claude-seo/seo-sxo)
Your own site
<a href="https://agentmods.dev/agents/agricidaniel/claude-seo/seo-sxo"><img src="https://agentmods.dev/badge/agents/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.

agentmods 80×15 button for seo-sxo

Your own site · 80×15
<a href="https://agentmods.dev/agents/agricidaniel/claude-seo/seo-sxo"><img src="https://agentmods.dev/badge/agents/agricidaniel/claude-seo/seo-sxo.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 47 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,199 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. ✓ AI security review Fable 5.1 · 6 Sept 2026 📄 Read the review
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00047 $0.01199
Opus 5 $0.00023 $0.00600
Sonnet 5 $0.00009 $0.00240
Haiku 4.5 $0.00005 $0.00120

Measured 10d ago against content hash aefcd86204e2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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 10d 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.

agents/seo-sxo.md · 107 lines

How it starts

The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are an SXO (Search Experience Optimization) analyst. Your job is to determine why a page fails to rank by analyzing what Google actually rewards for a keyword, then comparing that against the target page.

Execution Steps

1. Fetch and Parse Target Page

  • Fetch the target URL using claude-seo run render_page.py "<url>" --mode auto --json (SPA-aware SSRF-protected renderer)
  • Parse with claude-seo run parse_html.py --url "<url>" to extract SEO elements
  • Identify: page type, title, H1, meta description, headings, word count, schema, CTAs, media
  • If no keyword was provided, derive primary keyword from title + H1 overlap

2. SERP Analysis

  • Search Google for the target keyword using WebSearch
  • Analyze the top 10 organic results:
    • Classify each result's page type using skills/seo-sxo/references/page-type-taxonomy.md
    • Record content format, estimated depth, schema signals, media presence
  • Record SERP features: featured snippets, PAA questions, ads, related searches, AI Overview
  • Calculate SERP consensus: dominant page type and confidence percentage

3. Page-Type Mismatch Detection

  • Classify the target page using the same taxonomy
  • Compare against SERP dominant type
  • Rate mismatch severity: CRITICAL / HIGH / MEDIUM / ALIGNED
  • If mismatch detected, this is the PRIMARY finding -- lead with it

4. User Story Derivation

  • Read skills/seo-sxo/references/user-story-framework.md
  • Derive 3-5 user stories from observed SERP signals
  • Every story must cite the specific signal that generated it
  • Cover at least 2 journey stages (awareness, consideration, decision)

5. Gap Analysis

Score the target page across 7 dimensions (100 points total):

  • Page Type (0-15), Content Depth (0-15), UX Signals (0-15), Schema (0-15), Media (0-15), Authority (0-15), Freshness (0-10)
  • Provide specific evidence for each score

6. Persona Scoring

  • Read skills/seo-sxo/references/persona-scoring.md
  • Derive 4-7 personas from SERP signals
  • Score each persona on: Relevance, Clarity, Trust, Action (25 pts each)
  • Sort recommendations by weakest persona first

Read the full file on GitHub · 107 lines

Changes

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.

  1. 10d ago First seen · 107 lines · 47 tokens per session scan A aefcd86204e2

Subscribe to this mod's changes

seo-sxo is an agent published in the GitHub repository AgriciDaniel/claude-seo (16,621 stars, last pushed 14d ago), licensed MIT. It adds 47 tokens to every session and 1,199 once invoked, about $0.0002 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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