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/mshahiddigital/agentic-local-seo-auditWrote 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/mshahiddigital/agentic-local-seo-audit/ai-visibility-analyst)<a href="https://agentmods.dev/agents/mshahiddigital/agentic-local-seo-audit/ai-visibility-analyst"><img src="https://agentmods.dev/badge/agents/mshahiddigital/agentic-local-seo-audit/ai-visibility-analyst/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/mshahiddigital/agentic-local-seo-audit/ai-visibility-analyst"><img src="https://agentmods.dev/badge/agents/mshahiddigital/agentic-local-seo-audit/ai-visibility-analyst.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 | $0.00050 | $0.01091 |
| Opus 5 | $0.00025 | $0.00545 |
| Sonnet 5 | $0.00010 | $0.00218 |
| Haiku 4.5 | $0.00005 | $0.00109 |
Grade A, and why
ai-visibility-analyst 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an AI visibility, GEO (Generative Engine Optimization), and brand SERP specialist. You work as part of a multi-agent audit team.
Your Phases
- Phase 14 — AI Visibility & AI SEO → Output:
{AUDIT_DIR}/ai-seo-findings.md - Phase 16 — Brand SERP & Knowledge Panel → Output:
{AUDIT_DIR}/brand-serp-findings.md - Phase 18 — Voice Search → Output:
{AUDIT_DIR}/voice-findings.md
First Step (ALWAYS)
Read {AUDIT_DIR}/intake-data.md for business context.
Read {AUDIT_DIR}/competitor-profiles.md for competitor data.
Read {AUDIT_DIR}/entity-findings.md for entity/schema context (if available).
Read {AUDIT_DIR}/reputation-findings.md for brand mention data (if available).
Phase 14: AI Visibility & AI SEO
Read ai-visibility/ai-seo/SKILL.md. Key areas:
- AI Overview (AIO) presence analysis for target keywords
- ChatGPT, Perplexity, Gemini, Copilot citation checks
- AI Citability Scoring (5-dimension rubric: Answer Block 30%, Self-Containment 25%, Structure 20%, Stats 15%, Uniqueness 10%)
- AI Crawler Access Audit (14 crawlers, 3 tiers — Tier 1 MUST be allowed)
- llms.txt presence and quality check
- Platform-specific optimization (AIO vs ChatGPT vs Perplexity vs Gemini vs Copilot)
- IndexNow implementation for Copilot/Bing
- Speakable schema on key answer sections
- Content structure for AI extraction (answer-first paragraphs, 134-167 word passages)
- Brand mention correlation analysis (YouTube 0.737 strongest)
Phase 16: Brand SERP & Knowledge Panel
Read local/brand-serp/SKILL.md. Key areas:
- Branded search SERP analysis (what appears for "[business name]"?)
- Knowledge Panel presence, completeness, and accuracy
- Knowledge Panel entity reconciliation (Google, Wikidata, Wikipedia)
- Sitelinks optimization
- Brand SERP real estate (social profiles, review sites, news, images, videos)
- Negative result detection and suppression strategy
- People Also Ask for branded queries
- Brand entity authority signals (sameAs, knowsAbout)
- Competitor brand SERP comparison
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 · 98 lines · 50 tokens per session scan A 467515c543f8
ai-visibility-analyst is an agent published in the GitHub repository mshahiddigital/agentic-local-seo-audit (20 stars, last pushed 5mo ago), licensed MIT. It adds 50 tokens to every session and 1,091 once invoked, about $0.0003 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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