# Ai Seo

> >

- **Type:** Skill
- **Install:** `agentstack add skill-mshahiddigital-agentic-local-seo-audit-ai-seo`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [mshahiddigital](https://agentstack.voostack.com/s/mshahiddigital)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [mshahiddigital](https://github.com/mshahiddigital)
- **Source:** https://github.com/mshahiddigital/agentic-local-seo-audit/tree/main/ai-visibility/ai-seo

## Install

```sh
agentstack add skill-mshahiddigital-agentic-local-seo-audit-ai-seo
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# AI Visibility & AI SEO Audit — Phase 14

## Executive Summary

AI search is the fastest-growing traffic channel in 2025. Google AI Overviews appear for 20–35% of local service queries (SparkToro 2025). ChatGPT Search has 200M+ monthly active users. Perplexity processes 100M+ queries/month. Being cited in AI answers = significant visibility gain; being absent = invisible to AI-first searchers. This phase audits where the client appears (or doesn't) across all major AI platforms, identifies content gaps causing AI invisibility, and produces a prioritized action plan for AI citation dominance.

**2025 AI search benchmarks:**
- AIO citation rate: pages with FAQPage schema cited 3.2× more than pages without (Amsive 2025)
- ChatGPT freshness: pages updated within 30 days cited at 76.4% rate vs. 31.2% for 90+ day old pages
- Perplexity local query share: 18% of all queries are local business intent (Perplexity data, 2025)
- AIO + organic position 1 = ~45% CTR combined (vs. ~25% for position 1 alone when AIO shows)

---

## Tools for This Phase

| Tool | Purpose | Cost |
|------|---------|------|
| **Google Search** (incognito) | Test AIO presence for top 20–30 target keywords | Free |
| **ChatGPT** (GPT-4o with Search) | LLM visibility test — local service queries | Free/Paid |
| **Perplexity** | AI search citations for local service queries | Free |
| **Google Gemini** | Gemini AI visibility test + source attribution | Free |
| **Microsoft Copilot** | Bing-powered AI visibility | Free |
| **Google Rich Results Test** | FAQPage, HowTo, Speakable schema validation | Free |
| **Ahrefs** | Featured snippet ownership — AIO pulls from snippets | Paid |
| **SEMrush** | Featured snippet tracking per target keyword | Paid |
| **AlsoAsked.com** | PAA question mapping — AIO uses PAA patterns | Freemium |
| **Otterly.ai** | Track AIO mentions over time (monitoring) | Paid |

---

## The AI Search Landscape (2025-2026)

AI search is no longer emerging — it IS the mainstream:
- **Google AI Overviews**: Shown for ~20-30% of queries (growing)
- **Google AI Mode**: Full conversational search experience
- **ChatGPT Search**: Now default for 200M+ users with web access
- **Perplexity**: 100M+ monthly queries, growing fast
- **Microsoft Copilot**: Integrated into Windows/Edge/Bing
- **Gemini (Google)**: Deep integration with Android + Google Workspace
- **Claude (Anthropic)**: Growing presence with web search

For local businesses, AI search drives both:
1. Direct visibility (being mentioned in AI answers)
2. Competitive displacement (competitor gets the mention instead)

---

## Step 1: Read Project Context

Read `{AUDIT_DIR}/intake-data.md` — business name, URL, services, location.
Read `{AUDIT_DIR}/competitor-profiles.md` — competitor AI visibility signals.
Read `{AUDIT_DIR}/onpage-findings.md` — content structure gaps.

---

## Section 1: Google AI Overviews (AIO) Audit

### Test Protocol
Test top 20-30 target keywords in Google Search (incognito, desktop + mobile):

For each query, document:
| Query | AIO Shows? | Client Cited? | Competitors Cited? | Source URLs Used | AIO Format |
|-------|-----------|--------------|-------------------|-----------------|-----------|
| [query] | Yes/No | Yes/No | [names] | [URLs] | Paragraph/List/Table/Steps |

### AIO Appearance Patterns to Identify
- Which query types trigger AIO? (Informational, local, how-to, comparison?)
- Which content format is used in AIO? (Paragraphs, numbered steps, bullet lists?)
- What's the word count of the cited answer section?
- Does AIO pull from the first paragraph? A specific H2 section?
- Are any client pages cited? If so, which ones and why?

### AIO Optimization Strategy
To be cited in AI Overviews, content needs:
1. **Direct answers** — First 40-60 words answer the query directly
2. **Question-format headers** — H2: "What is [topic]?" / "How does [process] work?"
3. **Structured formatting** — numbered steps, bullet lists, tables rank well
4. **Unique data/insights** — AI cites specific facts, statistics, and expert opinions
5. **E-E-A-T signals** — Strong author credentials, institutional trust
6. **Comprehensive coverage** — Answer main query + related sub-questions
7. **Page authority** — Higher authority pages cited more often

---

## Section 2: Google AI Mode Audit

Google AI Mode (launched 2024, expanding 2025) enables fully conversational search.

### Test AI Mode Queries
- Enable AI Mode (Google account > Experiments or rolled-out region)
- Test conversational queries:
  - "What's the best [service] in [city] and why?"
  - "Compare [client] vs. [competitor 1] for [service]"
  - "I need [service] near [neighborhood] — any recommendations?"
  - Follow-up: "What makes them stand out?"

### Document Findings
| Query | Client Mentioned? | Accuracy | Competitor Priority | Source |
|-------|-----------------|---------|-------------------|--------|

---

## Section 3: LLM Visibility Audit (ChatGPT, Perplexity, Gemini, Copilot, Claude)

### Test Matrix
For each platform × query type:

**Platforms to test:**
- ChatGPT (GPT-4o with Search)
- Perplexity
- Google Gemini
- Microsoft Copilot
- Claude.ai (Anthropic)

**Query types to test:**
1. `Best [service] in [city]`
2. `[Service] near [neighborhood/landmark]`
3. `[Business Name]` — accuracy check
4. `[Business Name] reviews` — reputation
5. `How to find a good [service type] in [city]`
6. `[Specific service] cost in [city]`

**For each result document:**
- Business mentioned? (Yes/No)
- Accuracy of info (address, phone, hours, services)?
- Positive/negative framing?
- Competitors mentioned instead?
- Source URLs cited?
- Position (1st mention / 2nd / not mentioned)?

### LLM Visibility Score (per platform)
- Mentioned accurately in top 3 answers: 5/5
- Mentioned but with errors: 3/5
- Mentioned peripherally: 1/5
- Not mentioned: 0/5

---

## Section 4: AEO — Answer Engine Optimization Assessment

AEO focuses on structuring content so AI can extract and use it as answers.

### Content Assessment
For each key service page and FAQ page:
- [ ] Does each section start with a direct, standalone answer?
- [ ] Are definitions provided for key service terms?
- [ ] Are process steps numbered and clearly labeled?
- [ ] Are comparisons structured in tables?
- [ ] Are FAQs in explicit question + answer format (not buried)?
- [ ] Are specific figures, statistics, and data points included?
- [ ] Can the answer be extracted and read without surrounding context?

### FAQ Content Audit
- FAQ page exists?
- FAQs based on real customer questions (not generic)?
- FAQPage schema implemented?
- PAA (People Also Ask) boxes captured with existing content?
- Test: do current FAQs appear in PAA for target queries?

### Featured Snippet Optimization
Since AI Overviews pull from featured snippets:
- Which target queries have featured snippets?
- Does the client own any?
- What format does the winning snippet use?
- What page structure modifications are needed to win it?

---

## Section 5: GEO — Generative Engine Optimization Assessment

GEO focuses on making content easily citable by AI systems.

### Citeability Checklist
- [ ] Specific, quotable statements with data?
  Good: "Chicago homeowners saved an average of $2,400/year after our insulation upgrade"
  Bad: "We help homeowners save money on energy bills"

- [ ] Original research, surveys, or case studies?
- [ ] Named expert quotes with credentials?
- [ ] Step-by-step instructions with clear numbered format?
- [ ] Comparison tables with specific data?
- [ ] Local statistics about the service area?
- [ ] Industry benchmarks stated clearly?

### Source Authority Assessment
AI systems cite authoritative sources. Check:
- Domain authority and trust indicators
- E-E-A-T signals present (author, expertise, experience demonstrated)
- Linked to from other authoritative sources in the niche?
- Consistently mentioned on industry resource pages?

### Entity Optimization for AI
AI systems understand entities better than raw keyword text:
- Is the business entity clearly defined (who, what, where, when)?
- Are service entities named consistently (using industry-standard terminology)?
- Are geographic entities precise (neighborhood, city, region)?
- Do related entities connect? (business → services → location → team)?
- Schema markup reinforces all entity relationships?

---

## Section 6: Structured Data for AI Readability

Schema types most valuable for AI citation:
| Schema Type | AI Benefit |
|------------|-----------|
| LocalBusiness | Business entity establishment |
| FAQPage | Direct Q&A extraction |
| HowTo | Step-by-step answer extraction |
| Article + Author | E-E-A-T, content authority |
| AggregateRating | Trust signal for AI recommendations |
| Speakable | Marks content optimized for voice/AI reading |
| Service | Clear service entity definition |
| Review | Specific review content citation |

Validate all schema at: search.google.com/test/rich-results

---

## Section 7: Competitor AI Visibility Comparison

| Platform | Client Score | Comp 1 | Comp 2 | Comp 3 |
|----------|-------------|--------|--------|--------|
| Google AIO | | | | |
| ChatGPT Search | | | | |
| Perplexity | | | | |
| Gemini | | | | |
| Copilot | | | | |
| Overall AI Score | | | | |

**Findings:** Which competitor dominates AI visibility and why? What content do they have that the client lacks?

---

## Section 8: AI SEO Action Plan

### Priority Matrix

| Action | Impact (1–5) | Feasibility (1–5) | Priority | Effort |
|--------|-------------|-------------------|---------|--------|
| Add FAQPage schema to all service pages | 5 | 5 | 25 | 30 min/page |
| Rewrite service page intros (direct 50-word answer) | 5 | 4 | 20 | 30–60 min/page |
| Add specific data points (costs, timelines) to pages | 5 | 4 | 20 | 30–60 min/page |
| Create dedicated FAQ page (conversational queries) | 4 | 5 | 20 | 2–4 hrs |
| Add question-format H2s to top 5 service pages | 4 | 4 | 16 | 30 min/page |
| Add HowTo schema to process pages | 4 | 5 | 20 | 30 min/page |
| Publish original research / local statistics page | 5 | 3 | 15 | 4–8 hrs |
| Build E-E-A-T author profiles (credentials, bios) | 4 | 4 | 16 | 2–4 hrs |
| Refresh pages > 30 days old (freshness = AIO boost) | 4 | 5 | 20 | 30–60 min/page |
| Add Speakable schema to key answer sections | 3 | 4 | 12 | 30 min |

### Immediate (Week 1–2) — Quick Wins
- [ ] Add FAQPage schema to all service pages — Effort: 30 min/page — Expected: 3.2× more AIO citations
- [ ] Rewrite service page intros with direct standalone answers (first 50 words) — Effort: 30–60 min/page
- [ ] Add specific data points (pricing, timelines, statistics) to every service page — Effort: 30–60 min/page

### Short-Term (Month 1)
- [ ] Create dedicated FAQ page optimized for conversational queries — Effort: 2–4 hrs
- [ ] Restructure top 5 service pages with question-format H2s — Effort: 30 min/page
- [ ] Publish original research piece (local survey, case study, cost data) — Effort: 4–8 hrs
- [ ] Add HowTo schema to all process-based pages — Effort: 30 min/page

### Medium-Term (Months 2–3)
- [ ] Build E-E-A-T author profiles for all content contributors — Effort: 2–4 hrs
- [ ] Earn citations from authoritative local/industry sites — Effort: ongoing
- [ ] Create content that fills AI citation gaps vs. top competitor — Effort: 2–4 hrs/piece

---

## Scoring

| Category | Weight |
|----------|--------|
| AI platform visibility (AIO + ChatGPT + Perplexity + Gemini + Copilot) | 20% |
| AI citability scoring (passage-level extractability) | 20% |
| AI crawler access (Tier 1–3 crawlers + llms.txt) | 15% |
| AEO content structure (answer blocks, FAQs, featured snippets) | 15% |
| Platform-specific optimization readiness | 15% |
| Schema for AI readability (FAQPage, Speakable, knowsAbout) | 15% |

---

## Output

Write complete findings to `{AUDIT_DIR}/ai-seo-findings.md` with YAML frontmatter:

```yaml
---
skill: ai-visibility/ai-seo
phase: 14
date: [YYYY-MM-DD]
business: [Business Name]
url: [URL]
score: [X/100]
aio_cited: [yes|no|partial]
chatgpt_cited: [yes|no]
perplexity_cited: [yes|no]
faqpage_schema: [yes|no|partial]
aio_keywords_tested: [X]
aio_keywords_cited: [X]
---
```

Include:
- Score X/100 with per-category breakdown
- Full AI visibility matrix (all 5 platforms × 6 query types)
- AIO appearance analysis for top 20–30 keywords
- Competitor AI visibility comparison table
- AEO content structure checklist (per key page)
- GEO citeability checklist
- Schema for AI readability (present vs. missing per page)
- Content gap list (what's missing for AI citation)
- Priority matrix (all actions, Impact × Feasibility scored)
- 30/90-day AI SEO action plan with effort estimates

**Output files:**
- `{AUDIT_DIR}/ai-seo-findings.md` — AI visibility audit with score and citation gap analysis
- `{REPORTS_DIR}/phase-14-ai-seo.pdf` — auto-generated PDF after phase completes

**Key consumers:**
- `ai-visibility/voice-search` — voice and AIO share optimization signals
- `audit/onpage-seo` — AIO structure requirements inform on-page optimization
- `cross-cutting/serp-trust-auditor` — Trust & AI Readiness (T2) dimension
- `output/report-generation` — AI visibility section in master report

---

## Section 9: AI Citability Scoring Framework

AI models cite passages that meet specific structural criteria. GEO-optimized content achieves 30–115% higher visibility in AI-generated responses (Georgia Tech / Princeton / IIT Delhi 2024). Optimal AI-cited passages are **134–167 words**, self-contained, fact-rich, and answer-first.

### Citability Rubric (per content block, 0–100)

| Category | Weight | What It Measures | Scoring |
|----------|--------|-----------------|---------|
| Answer Block Quality | 30% | Does the passage open with a direct, quotable answer? Uses "X is..." or answer-first patterns? First 40–60 words stand alone? | 90+: every section opens with 1–2 sentence answer; 50–69: answers buried mid-paragraph; H2>H3 hierarchy? Question-based headings? Short paragraphs (2–4 sentences)? Tables for comparisons? Lists for processes? | 90+: clean hierarchy + question headings + tables/lists; 50–69: some structure;  Content delivery networks (CDNs) are distributed server systems that cache and serve web content from locations geographically close to end users. A CDN reduces latency by 50–70% on average by serving assets from edge servers rather than a single origin server. The three largest CDN providers as of 2025 are Cloudflare (serving approximately 20% of all websites), Amazon CloudFront, and Akamai Technologies.
> — 58 words. Self-contained: yes. Facts: 3 specific data points. Definition pattern: yes.

**LOW citability (score ~15):**
> If you've ever wondered why some websites load faster than others, the answer might surprise you. There's this amazing technology that has been around for a while now. It's changed the way we think about web performance. Let me explain how it works.
> — 52 words. Self-contained: no (no topic named). Facts: 0. Definition pattern: no.

### Citation Research Data

| Finding | Source |
|---------|--------|
| Optimal AI-cited passage length: 134–167 words | Bortolato 2025 analysis of AI Overview passages |
| Definition patterns increase citation rate by 2.1× | Georgia Tech 2024 |
| Adding statistics increases citation by 40% | Princeton GEO study 2024 |
| Adding authority quotations increases citation by 115% in some categories | IIT Delhi 2024 |
| Fluency optimization increases visibility by 30% average | Georgia Tech 2024 |
| Content with source citations cited 20–25% more often by Perplexity/ChatGPT | Industry data 2025 |

### AI System Citation Preferences

| AI System | Citation Preference |
|-----------|-------------------|
| **Google AI Overviews** | Concise answer blocks (40–60 words). Content already ranking in top 10. Structured formatting (tables, lists). |
| **ChatGPT (Search)** | Explicit definitions, named sources, recent dates. Cites 2–4 sources per response. Wikipedia source

…

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [mshahiddigital](https://github.com/mshahiddigital)
- **Source:** [mshahiddigital/agentic-local-seo-audit](https://github.com/mshahiddigital/agentic-local-seo-audit)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-mshahiddigital-agentic-local-seo-audit-ai-seo
- Seller: https://agentstack.voostack.com/s/mshahiddigital
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.