Seo ai search share of voice
Skill amirjahfar1/automate-seo-with-claude/skills/seo-ai-search-share-of-voice
Measure AI Search share of voice for a target domain versus competitors across ChatGPT, Perplexity, Gemini, Google AI Overview, and AI Mode. Pulls the AIO leaderboard, then samples prompts where each domain appears as a source or brand mention, and analyses topic clusters each brand owns. Use when the user asks for AI Search share of voice, LLM visibility tracking, AEO/GEO analysis, AI Overview competitive analysis, or wants to know which brands LLMs cite in their category.From its SKILL.md
npx -y skills add amirjahfar1/automate-seo-with-claude --skill seo-ai-search-share-of-voiceAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
5.3 KB, ~1.3k tokens by cl100k_base, as published. Nobody here has run it
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):
-
Topic clustering
- Group prompts by theme (e.g., pricing, feature comparison, tutorials, alternatives, reviews).
- For each brand, note which clusters it dominates and which it is absent from.
-
Gap and recommendation synthesis
- Identify 3 to 5 topic clusters where the target underperforms competitors despite having relevant content.
- Recommend specific actions: new content angles, structured data additions, partnerships with frequently-cited sources, comparison pages, FAQ/How-To schema.
Output format
Create a folder seo-ai-search-share-of-voice-{target-slug}-{YYYYMMDD}/ with:
seo-ai-search-share-of-voice-{target-slug}-{YYYYMMDD}/
├── 01-leaderboard.md # raw leaderboard per engine
├── 02-heatmap.md # visual heatmap table
├── 03-prompts-{domain}.md # one file per domain with 20 sampled prompts
├── 04-topic-clusters.md # cluster membership per brand
└── REPORT.md # executive summary
REPORT.md follows this shape:
# AI Search Share of Voice: {target brand} vs competitors
## Summary
- Target: {target} ({share}% across all engines)
- Leader: {leader brand} ({share}%)
- Target rank: {n} of {total}
## Heatmap
| Domain | AI Overview | ChatGPT | Perplexity | Gemini | AI Mode |
|---|---|---|---|---|---|
| {target} | {%} | {%} | {%} | {%} | {%} |
| {comp1} | ... | ... | ... | ... | ... |
## Who owns what
### {target brand}
Strong in: {cluster 1}, {cluster 2}
Absent from: {cluster 3}, {cluster 4}
### {competitor 1 brand}
...
## Topic cluster ownership
| Cluster | Leader | Share | Target position | Gap |
|---|---|---|---|---|
| Pricing | {brand} | {%} | {n} | {% behind} |
| Alternatives | {brand} | {%} | {n} | {% behind} |
| Tutorials | {brand} | {%} | {n} | {% behind} |
## Top 5 actions to close gaps
1. {action with target cluster}
2. ...
Tips
- Do not hallucinate citation counts. If the API returns zero prompts for a given domain/engine, report zero, do not estimate.
- For each competitor, validate the brand-name match in the prompt text. Sometimes "Wix" appears in a sentence about "wiktionary" or a person's name. Flag ambiguous matches in the raw-prompt file.
base_domainscope is the default; do not narrow tosubdomainunless the user asks.- DataForSEO allows up to 2,000 calls/min, 30 concurrent; pace sequentially. With 5 domains and 2 mention queries per engine per domain, pace the loop. DataForSEO bills per call — cap large mention lists with
limitandai_optimization_llm_mentions_filters. - The report is not a one-time artefact. Recommend the user re-run monthly and diff results to see ranking momentum.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.
Gives 0 of the 12 instructions most marketing audience skills give in ~1.3k tokens
Counted across 690 of the 894 authors here whose files we hold, read 2026-08-07
- Apply Poppins font to headingsin 41 of 690, across 6 files
- Apply Lora font to body textin 41 of 690, across 6 files
- Use Arial fallback for headingsin 39 of 690, across 4 files
- Use Georgia fallback for body textin 39 of 690, across 4 files
- Maintain text hierarchy and formattingin 39 of 690, across 4 files
- Use accent colors for non-text shapesin 38 of 690, across 3 files
- Use RGB values for precise color matchingin 38 of 690, across 3 files
- Use brand colors for primary text and backgroundsin 36 of 690, across 1 file
- Read product marketing context file before asking questions, starting, or auditingin 35 of 690, across 23 files
- Use active voice instead of passive voicein 26 of 690, across 10 files
- Implement or generate appropriate JSON-LD structured datain 24 of 690, across 17 files
- Prioritize clarity over clevernessin 22 of 690, across 8 files
Said here and by no other author read
- pull llm-mention leaderboard data
- capture mention counts per engine
- build heatmap table of domains and engines
- sample prompts per target and competitor
- save query text and cited sources
- group prompts by theme
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.