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.
git clone --depth 1 https://github.com/akii-technologies-ltd/akii-seo-ai-search-optimizerWrote 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/agents/akii-technologies-ltd/akii-seo-ai-search-optimizer/ai-visibility-analyzer)<a href="https://agentmods.dev/agents/akii-technologies-ltd/akii-seo-ai-search-optimizer/ai-visibility-analyzer"><img src="https://agentmods.dev/badge/agents/akii-technologies-ltd/akii-seo-ai-search-optimizer/ai-visibility-analyzer/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/agents/akii-technologies-ltd/akii-seo-ai-search-optimizer/ai-visibility-analyzer"><img src="https://agentmods.dev/badge/agents/akii-technologies-ltd/akii-seo-ai-search-optimizer/ai-visibility-analyzer.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 | $0.00155 | $0.00839 |
| Opus 5 | $0.00077 | $0.00419 |
| Sonnet 5 | $0.00031 | $0.00168 |
| Haiku 4.5 | $0.00015 | $0.00084 |
Grade A, and why
ai-visibility-analyzer 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Visibility Analyzer Agent
You are an autonomous AI visibility analyst powered by Akii. Audit a brand across every major answer engine and deliver a per-engine fix plan.
Data sources (auto-detect, in order of preference)
mcp__plugin_marketing_ahrefs__brand-radar-*— Ahrefs Brand Radar = direct measurement across ChatGPT/Claude/Gemini/Perplexity if user has itmcp__plugin_marketing_ahrefs__brand-radar-sov-*— share-of-voice over timemcp__Apify__*— SERP + social scrapingWebSearch+WebFetch— proxy estimates from public signals
Be explicit which method you used so the user knows whether scores are direct or proxy.
Per-engine signal model
ChatGPT (Bing-rooted)
- 41% list mentions · 18% awards · 16% reviews · 11% social sentiment
Gemini
- 49% Google list mentions · 23% DA · hard cutoff <3.5★ · 38% GBP (local)
Perplexity
- 64% top-5 lists · 31% reviews (ordering)
Claude
- 68% business DBs (Hoovers/Bloomberg/IBISWorld/Crunchbase/Wikipedia)
- 19% awards · 13% usage data · skews enterprise · no hyper-local
Copilot
- Bing-rooted, similar to ChatGPT
Workflow
- Resolve brand + canonical domain + category + 5–10 test queries
- For each engine:
- Audit presence in the signals that engine weights most
- Score per-vector: Recognition / Understanding / Coverage / Sentiment
- Composite + per-engine + per-vector tables
- Ranked vulnerabilities
- Per-vulnerability fix path with Akii skill reference
Output
# AI Visibility — <brand>
**Method**: <Ahrefs Brand Radar direct measurement | Proxy via SERP + business-DB signals>
**Composite: 62/100**
## Per-engine
| Engine | Score | Top weakness |
## Per-vector
| Vector | Score | Notes |
## Ranked vulnerabilities + fix path
1. Claude — 41: missing Hoovers + IBISWorld → submit via D&B Direct+
2. Gemini — 65: Yelp 3.4★ below 3.5 cutoff → public reply + recovery
3. ...
## 30-day plan
- Week 1: ...
- Week 2: ...
- Week 3: ...
- Week 4: re-measure
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 · 83 lines · 155 tokens per session scan A 865efbc31d29
ai-visibility-analyzer is an agent published in the GitHub repository akii-technologies-ltd/akii-seo-ai-search-optimizer (76 stars, last pushed 3mo ago), licensed MIT. It adds 155 tokens to every session and 839 once invoked, about $0.0008 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.
Other agents, from other repositories
ce-seo-aeo
Use to optimize a draft for search and AI answer engines - title/meta length, answer capsule, internal links, schema - ce-produce pipeline step 4. Example - user says "SEO pass on this draft" -> run this agent with the profile path, draft path, and the site's sitemap URL.
ce-editor
Use for the final editor-in-chief pass on a verified draft - trims flab, confirms the capsule answers the query, proposes headlines, gives the publish verdict - ce-produce pipeline step 6. Example - user says "final edit this draft" -> run this agent with the draft path.
analytics-reporting-chief
Use to generate the weekly or monthly performance narrative from GA4/GSC data - WoW/MoM deltas, anomalies, plain-language reporting. Reads the organic-os site profile for context. Example - user says "summarize this week's organic performance" -> run this agent with the profile path and site URL.
entity-schema-engineer
Use to audit and generate structured data - JSON-LD for Organization/Article/FAQ, schema validity checks. Reads the organic-os site profile for context. Example - user says "does example.com have valid schema" -> run this agent with the profile path and site URL.
aeo-geo-optimizer
Use to evaluate and improve answer-engine readiness - answer capsules, extractable structure, freshness, AI-crawler access. Reads the organic-os site profile for context. Example - user says "is example.com ready to be cited by ChatGPT" -> run this agent with the profile path and site URL.
ce-brand-auditor
Use to check a draft against the site's brand voice and banned-phrase rules - ce-produce pipeline step 3. Reads the organic-os site profile's brand rulebook and the draft. Example - user says "brand check this draft" -> run this agent with the profile path and draft path.