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 georgekhananaev/claude-skills-vault --skill seo-sxogit clone --depth 1 https://github.com/georgekhananaev/claude-skills-vaultWrote 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/georgekhananaev/claude-skills-vault/seo-sxo)<a href="https://agentmods.dev/skills/georgekhananaev/claude-skills-vault/seo-sxo"><img src="https://agentmods.dev/badge/skills/georgekhananaev/claude-skills-vault/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/georgekhananaev/claude-skills-vault/seo-sxo"><img src="https://agentmods.dev/badge/skills/georgekhananaev/claude-skills-vault/seo-sxo.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.02411 |
| Opus 5 | $0.00051 | $0.01205 |
| Sonnet 5 | $0.00020 | $0.00482 |
| Haiku 4.5 | $0.00010 | $0.00241 |
Grade A, and why
seo-sxo scanned grade A with 1 finding 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 12d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- [ ] Target URL was fetched via `scripts/fetch_page.py` (not raw curl/fetch) This is a copy
91% identical to seo-sxo — 16 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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
scripts/fetch_page.py(SSRF-safe) - Parse with
scripts/parse_html.pyto 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 SERP features: ratings, 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 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.
- 12d ago First seen · 255 lines · 101 tokens per session scan A 0a99fd8b8339
seo-sxo is a skill published in the GitHub repository georgekhananaev/claude-skills-vault (28 stars, last pushed 1mo ago), licensed MIT. It adds 101 tokens to every session and 2,411 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 91% identical to seo-sxo, differing in 16 lines, and is treated as a copy.
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