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 amirjahfar1/automate-seo-with-claude --skill seo-ai-search-share-of-voicegit clone --depth 1 https://github.com/amirjahfar1/automate-seo-with-claudeWrote 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/amirjahfar1/automate-seo-with-claude/seo-ai-search-share-of-voice)<a href="https://agentmods.dev/skills/amirjahfar1/automate-seo-with-claude/seo-ai-search-share-of-voice"><img src="https://agentmods.dev/badge/skills/amirjahfar1/automate-seo-with-claude/seo-ai-search-share-of-voice/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/amirjahfar1/automate-seo-with-claude/seo-ai-search-share-of-voice"><img src="https://agentmods.dev/badge/skills/amirjahfar1/automate-seo-with-claude/seo-ai-search-share-of-voice.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 | $0.00109 | $0.01375 |
| Opus 5 | $0.00055 | $0.00687 |
| Sonnet 5 | $0.00022 | $0.00275 |
| Haiku 4.5 | $0.00011 | $0.00137 |
Grade A, and why
seo-ai-search-share-of-voice 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 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.
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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Example output: examples/seo-ai-search-share-of-voice-wix-com-20260427/REPORT.md
AI Search Share of Voice
Compare AI-search visibility for a target brand against competitors across every major LLM engine, then analyse the topic clusters each brand owns and where gaps exist.
Prerequisites
- DataForSEO MCP server connected.
- User provides: (a) target domain and its brand name, (b) list of competitor domains and brand names, (c) country (default:
us), and (d) optionally, which engines to analyse (default: all supported:ai-overview,chatgpt,perplexity,gemini,ai-mode).
Process
-
Leaderboard snapshot
mcp__dataforseo__ai_opt_llm_ment_top_domains+mcp__dataforseo__ai_opt_llm_ment_agg_metrics; Google AI Overview presence frommcp__dataforseo__serp_organic_live_advanced(AIO block)- Pull the LLM-mention leaderboard (top cited/mentioned domains) for the target domain's category in the target country; for the AI Overview engine, read the AIO citation block from
serp_organic_live_advancedon the category's seed keywords. - Capture mention counts and share percentages per engine, per domain.
- Pull the LLM-mention leaderboard (top cited/mentioned domains) for the target domain's category in the target country; for the AI Overview engine, read the AIO citation block from
-
Heatmap table
- Build a table: rows = domains (target + competitors), columns = engines, cells = % share of voice.
- Highlight the leader per engine and the worst performer.
-
Prompt sampling per domain
mcp__dataforseo__ai_opt_llm_ment_search,mcp__dataforseo__ai_opt_llm_ment_top_pages; for actual answersmcp__dataforseo__ai_optimization_chat_gpt_scraper/mcp__dataforseo__ai_optimization_llm_response- For each domain (target and each competitor):
- Use
ai_opt_llm_ment_searchto pull prompts/queries where the domain appears as a cited source (link mention) and where the brand is mentioned by name;ai_opt_llm_ment_top_pagessurfaces the specific pages cited. - Where a live answer is needed to confirm a mention, scrape it with
ai_optimization_chat_gpt_scraper(ChatGPT) orai_optimization_llm_response(other models).
- Use
- Save query text and the exact sources cited so the user can validate.
- For each domain (target and each competitor):
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 · 100 lines · 109 tokens per session scan A d8a3f6bf22c2
seo-ai-search-share-of-voice is a skill published in the GitHub repository amirjahfar1/automate-seo-with-claude (2 stars, last pushed 3mo ago), licensed MIT. It adds 109 tokens to every session and 1,375 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 2026-08-31.
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