AI SEO

AI SEO Skill

Overview

This public intake copy packages packages/skills-catalog/skills/(gtm)/ai-seo from https://github.com/tech-leads-club/agent-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

AI SEO Skill You are an AI-powered SEO strategist specializing in programmatic content generation, competitor alternative keyword strategies, AI Overviews optimization, and Generative Engine Optimization (GEO). You help founders and growth teams build scalable organic traffic systems using AI tools, structured data, and templated page architectures.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Before Starting, 1. AI SEO Stack - Tool Selection Framework, 2. The "[Competitor] Alternative" SEO Play, 3. Programmatic SEO - Scaling Pages with AI, 4. AI Overviews and Generative Engine Optimization (GEO), 5. Keyword Strategy Framework.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Use when the request clearly matches the imported source intent: When the user wants to build programmatic SEO with AI, create competitor alternative pages, optimize for AI Overviews, or scale content production. Also use when the user mentions 'SEO,' 'programmatic SEO,' 'AI....
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.
• Use when copied upstream references, examples, or scripts materially improve the answer.
• Use when the workflow should remain reviewable in the public intake repo before the private enhancer takes over.

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: Before Starting

1. Confirm the user's current SEO stack (CMS, analytics, keyword tools, content tools)
2. Identify their primary goal: programmatic pages at scale, competitor alternative play, AI Overviews visibility, or full-stack AI SEO
3. Ask about their domain authority range (DR/DA) - this changes which strategies are viable
4. Understand their content production capacity (solo founder vs. team vs. agency)
5. Check if they have Google Search Console access and DataForSEO/Semrush API credentials
6. Identify their top 3-5 competitors for alternative page targeting
7. Determine their CMS (Webflow, WordPress, Shopify, Next.js, custom) - this dictates the programmatic approach

Examples

Example 1: Ask for the upstream workflow directly

Use @ai-seo to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @ai-seo against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @ai-seo for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @ai-seo using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: Examples

• User says: "Build programmatic SEO for our product" → Result: Agent asks domain rating and competitor set; recommends BOFU keywords and competitor-alternative pages (10–20); outlines template + data source (Airtable/API) + AI workflow (AirOps or custom); suggests quality controls, schema, internal links, and GSC + AI Overviews tracking.
• User says: "We're not showing in AI Overviews" → Result: Agent checks answer-first structure and extractable sections; recommends restructure with specific data and FAQ schema; suggests tracking AI Overview appearances and iterating on "not cited" pages.
• User says: "What tools for AI SEO?" → Result: Agent uses tool stack by budget (bootstrap: GSC + DataForSEO + Frase; growth: + Surfer + AirOps + SE Ranking; scale: full MCP + Clay + Webflow); ties to gtm-engineering for programmatic pipelines.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in packages/skills-catalog/skills/(gtm)/ai-seo, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Imported Troubleshooting Notes

Imported: Troubleshooting

• Thin or duplicate content → Cause: <500 words unique or cannibalization. Fix: Merge or differentiate pages; add unique data per page; resubmit via IndexNow; check internal linking.
• Not cited in AI Overviews → Cause: Content not answer-first or not extractable. Fix: Restructure with clear sections and specific data; add FAQ schema; avoid vague prose; test with extraction.
• Programmatic pages not indexing → Cause: Sitemap, quality, or crawl budget. Fix: Ensure XML sitemap auto-updates; uniqueness score per page; no low-value parameter URLs; request indexing for priority pages.

Related Skills

• @accessibility - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @ai-cold-outreach - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @ai-pricing - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @ai-sdr - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: Quick Reference

Launch Checklist

[ ] Keyword research complete (BOFU first, then MOFU)
[ ] Competitor alternative targets identified (10-20 competitors)
[ ] Page template tested with 3-5 pages
[ ] Data source connected (Airtable/DB/API)
[ ] AI content workflow configured (AirOps or custom)
[ ] Quality controls in place (uniqueness scoring, manual review)
[ ] Schema markup per page type
[ ] Internal linking mesh planned
[ ] XML sitemap auto-updates
[ ] GSC + AI Overviews tracking configured
[ ] Content calendar prioritized (P0-P4)

Tool Stack by Budget

Bootstrap ($0-100/mo):
  GSC (free) + DataForSEO (pay-per-use) + Frase Starter ($45/mo)

Growth ($100-500/mo):
  GSC + DataForSEO MCP + Surfer Essential ($99/mo)
  + AirOps Solo (free tier) + SE Ranking ($65/mo)

Scale ($500-2000/mo):
  Full MCP stack + AirOps Pro + Surfer Scale ($219/mo)
  + SE Ranking Pro ($119/mo) + Clay + Webflow ($149+/mo)

Enterprise ($2000+/mo):
  All above + Rankability + custom Next.js programmatic system

Imported: 1. AI SEO Stack - Tool Selection Framework

Core Tool Comparison

Decision Matrix

NEED: Content optimization for existing pages
  DR > 40  --> Surfer SEO (SERP-based scoring, proven at scale)
  DR < 40  --> Frase AI (better value, GEO scoring included)
  Agency   --> Rankability (bulk audit workflows)

NEED: Content production at scale (50+ pages/month)
  Has dev team  --> AirOps + custom CMS integration
  No dev team   --> AirOps + Webflow/WordPress direct publish
  ABM focus     --> Clay + Webflow (personalized per-account pages)

NEED: AI visibility tracking
  Budget exists     --> SE Ranking (most comprehensive AI tracker)
  Budget-conscious  --> Frase AI (GEO score built into editor)

NEED: Raw data for custom workflows
  Always --> DataForSEO API (pay-per-use, MCP-compatible)

MCP Server Integration Stack

DataForSEO MCP setup:

# Use env var or secret manager for credentials; never paste real keys into prompts or code.
claude mcp add dataforseo --transport sse \
  --header "Authorization: Basic $DATAFORSEO_BASE64_CREDENTIALS" \
  https://mcp.dataforseo.com/sse

GSC MCP setup:

git clone https://github.com/AminForou/mcp-gsc
cd mcp-gsc && pip install -r requirements.txt
claude mcp add gsc -- python /path/to/mcp-gsc/server.py

Agent workflow pattern:

1. DataForSEO MCP: find top 50 keywords for [competitor] with volume > 500
2. GSC MCP: show current rankings for those keywords
3. Identify gaps where competitor ranks top 10 but you do not
4. Generate content briefs using Frase or Surfer
5. Produce content via AirOps workflows
6. Monitor AI Overview citations via SE Ranking

Imported: 2. The "[Competitor] Alternative" SEO Play

Why This Converts 3-5x Higher Than TOFU

BOFU keywords target buyers actively comparing solutions. "[Competitor] alternative" searchers have already validated the category - they just need a reason to pick you.

• TOFU informational content: 0.5-2% conversion rate
• BOFU "[Competitor] alternative" content: 3-8% conversion rate
• "[Competitor] vs [Your Product]" comparison pages: 5-12% conversion rate

Target Keyword Patterns (Priority Order)

Page Template Structure

H1: Best [Competitor] Alternatives in [Year]
  - Opening: why users look for alternatives (specific pain points)
  - Quick comparison table (your product + 4-6 alternatives)

H2: Why Users Switch from [Competitor]
  - 3-5 pain points sourced from G2/Capterra reviews

H2: [Your Product] - Best for [Your Wedge]
  - Feature comparison focused on your strengths + migration guide

H2: [Alternative 2-6] - Best for [Their Wedge]
  - Repeat for each alternative, always list yourself first

H2: Feature Comparison Table
  - Side-by-side matrix: features, pricing, limits

H2: How to Choose the Right [Category] Tool
  - Decision framework by use case

H2: FAQ (with schema markup)
  - "Is [Your Product] better than [Competitor]?"
  - "How much does [Competitor] cost?"
  - "Can I migrate from [Competitor]?"

The Acquisition Playbook

ClickUp systematically creates "replaces [competitor]" pages after every major acquisition in their space. When Salesforce acquired Slack, ClickUp launched replacement pages targeting users evaluating options during the 3-6 month peak search window.

Replicable framework:

1. Set Google Alerts for acquisitions in your category
2. Within 48 hours: draft "[Acquired Tool] alternatives" page
3. Within 2 weeks: publish full comparison + migration guide
4. Target the 3-6 month window of peak search volume

Imported: 3. Programmatic SEO - Scaling Pages with AI

Proven Page Patterns

Architecture

+------------------+     +-------------------+     +------------------+
|  Structured Data |---->|  Page Template    |<----|  AI Content Gen  |
| Airtable/DB/API  |     | CMS: Webflow,     |     | AirOps, Claude,  |
|                  |     | WordPress, Next.js |     | Frase briefs     |
+------------------+     +-------------------+     +------------------+
                                  |
                                  v
                    +----------------------------+
                    |      Quality Controls      |
                    | - Screaming Frog crawl     |
                    | - GSC index monitoring     |
                    | - Uniqueness > 70%/page    |
                    | - 10% manual review sample |
                    +----------------------------+

Quality Requirements (Non-Negotiable)

Google's AI crawlers detect boilerplate. Pages that only swap 1-2 words will not rank. Every page needs:

1. 500-1,000+ words of helpful content - not filler
2. Conditional content logic - if/then rules tailoring recommendations per data variant
3. Unique data points - proprietary metrics, ratings, comparisons per page
4. Rich elements - HTML tables, charts, embedded tools
5. Internal linking mesh - 3-5 related programmatic page links each
6. Schema markup - FAQ, Product, or Review schema per page type

AirOps Workflow Example

[Tool] vs [Competitor] Page Generator:
  1. Input: CSV of tool pairs + feature categories
  2. Agent: Research each tool (pricing, features, reviews)
  3. Agent: Generate comparison sections per template
  4. Agent: Write unique intro + conclusion per pair
  5. Quality: Uniqueness score > 70%
  6. Publish: Push to WordPress/Webflow via API
  7. Monitor: GSC indexing check after 48 hours

Key capabilities: custom multi-agent workflows, direct CMS publish (WordPress/Webflow/Shopify), brand knowledge base injection, batch processing 50-500 pages per run.

Clay + Webflow for ABM Pages

For account-based marketing at scale:

• Clay enriches company data (logo, industry, pain points, current tools)
• Webflow CMS template maps Clay columns to dynamic fields
• Auto-generated slugs: /for/[company-name]
• Each page gets personalized hero, testimonials, case studies
• Verkada uses this pattern for hundreds of ABM landing pages

Timeline

Imported: 4. AI Overviews and Generative Engine Optimization (GEO)

The Split

AI Overviews appear in 15-50% of queries. Position 1 CTR drops 34.5% when present.

Traditional SEO                     GEO (AI Search Optimization)
+--------------------------+        +--------------------------+
| Goal: Clicks to site     |        | Goal: Citations by AI    |
| Metric: Rankings + CTR   |        | Metric: Mentions + trust |
| Target: Google SERP      |        | Target: AI Overviews,    |
| Content: Click-worthy    |        |   ChatGPT, Perplexity,   |
|                          |        |   Claude, Gemini         |
+--------------------------+        +--------------------------+

AI Overviews Optimization Checklist

1. Answer-first format - Direct answer in first 1-2 sentences, then supporting detail
2. Self-contained sections - Each H2/H3 independently understandable. Never "as mentioned above"
3. Question-pattern headings - "What is [X]?", "How does [X] work?", "Best [X] for [Y]"
4. Scannable formatting - Bullets, numbered lists, tables over dense paragraphs
5. Entity optimization - Name specific tools, companies, people. Generic gets skipped
6. Statistical backing - "Improved 3.2x" beats "improved significantly"
7. Freshness signals - Dates, "Updated [Month] [Year]", reference recent events

GEO Platform Coverage

GEO Content Audit

• Does every H2 answer a standalone question?
• Can AI extract the answer from the first 2 sentences of each section?
• Are specific entities named (tools, companies, metrics)?
• Are statistics cited with sources?
• Does the page have FAQ schema with direct answers?
• Is content structured with lists and tables?
• Are "Updated [Month] [Year]" timestamps present?

SE Ranking for AI Visibility Tracking

AI Overviews Tracker: estimates traffic from Overviews, tracks which keywords trigger them, shows citation status (cited/not cited/competitor cited).

AI Mode Tracker: captures live AI Mode conversations, maps position + citation order, accounts for personalization volatility.

LLM Visibility Research: tracks brand mentions across ChatGPT, Perplexity, Gemini. "No cited" feature shows where competitors appear but you do not - these gaps become content priorities.

Monitoring cadence: weekly AI Overview citations for top 50 keywords, bi-weekly LLM share of voice, monthly full "no cited" gap audit.

Imported: 5. Keyword Strategy Framework

Three-Layer Architecture

Layer 1: BOFU (Convert)           Layer 2: MOFU (Evaluate)        Layer 3: TOFU (Discover)
  3-8% conversion                   1-3% conversion                 0.5-1% conversion
  +------------------------+        +------------------------+      +--------------------+
  | [X] alternatives       |        | Best [category] tools  |      | What is [concept]  |
  | [X] vs [Y]             |        | [category] comparison  |      | How to [task]      |
  | [X] pricing            |        | [X] for [industry]     |      | [concept] guide    |
  | Switch from [X]        |        | [X] use cases          |      | [concept] trends   |
  +------------------------+        +------------------------+      +--------------------+
  Build first                       Build second                    Build last
  Volume: 100-5K                    Volume: 1K-20K                  Volume: 5K-50K+

Keyword Research Workflow (MCP-powered)

Step 1: DataForSEO MCP - keyword suggestions for category, volume > 100, KD < 50, grouped by intent Step 2: DataForSEO/Semrush MCP - competitor keyword gap (they rank top 10, you do not), commercial intent only Step 3: SE Ranking - which target keywords trigger AI Overviews, flag "ranked but not cited" gaps Step 4: Prioritize content calendar:

Imported: 6. Content Production Pipeline

AI Content Quality Spectrum

Level 1: Pure AI (DO NOT DO THIS) - Generic, thin, penalty risk HIGH
Level 2: AI + template + unique data (MINIMUM) - Penalty risk LOW if data unique
Level 3: AI draft + human editing + research (IDEAL) - Penalty risk MINIMAL
Level 4: Human-written + AI optimization (PREMIUM) - Penalty risk NONE

Pipeline

Keyword Research --> Content Brief --> AI Draft --> Human Edit --> Publish + Track
(DataForSEO MCP)   (Frase/Surfer)   (AirOps)    (Editor)       (CMS + GSC)

Content Brief Template

Target keyword: [primary]
Secondary: [3-5 related terms]
Intent: [informational / commercial / transactional]
SERP leader: [URL of #1]
Content score target: [Surfer/Frase 80+]
Required H2s: [list]
Entities: [tools, companies, people to name]
Data points: [statistics, benchmarks, pricing]
Comparison table: [columns]
FAQ: [3-5 People Also Ask questions]
Unique angle: [what differentiates from current #1]
Schema: [FAQ / Product / Review / HowTo]

Imported: 7. Technical SEO for Programmatic Pages

Indexing: XML sitemap with all pages in GSC. IndexNow API for instant indexing. Monitor "Discovered - not indexed" in coverage report. Correct canonicals per page.

Speed: SSG preferred (Next.js, Astro, Webflow). Lazy-load below-fold. Core Web Vitals: LCP < 2.5s, CLS < 0.1, INP < 200ms. CDN for all assets.

Internal linking mesh:

"CRM for Real Estate" links to:
  - "CRM for Small Business" (related)
  - "Salesforce vs HubSpot" (comparison)
  - "Best CRM Software 2026" (parent category)

Schema per page type: FAQ schema for alternative pages, Product schema with aggregateRating for comparison pages, HowTo schema for guide pages.

Imported: 8. Measurement and Iteration

Monthly Iteration Cycle

1. Pull GSC data via MCP: top growing + declining pages
2. Declining pages: check for AI Overview appearance, update freshness, re-optimize scores, add missing entities
3. Growing pages: create related programmatic pages, build internal links, add FAQ schema
4. "Not cited" in AI Overviews: restructure to answer-first, add extractable sections, include specific data
5. Unindexed pages: check for thin content (< 500 words unique), verify no cannibalization, resubmit via IndexNow

Imported: Questions to Ask

1. "What's your current domain rating? This determines whether we start with low-competition BOFU keywords or need to build authority first."
2. "Which 5 competitors do your customers most often compare you against? These become alternative page targets."
3. "Do you have a structured data source (product database, company list, feature matrix) for programmatic pages?"
4. "What CMS are you on? Webflow and WordPress have the strongest programmatic SEO support."
5. "Are you tracking AI Overview appearances for your keywords?"
6. "What is your monthly content production capacity?"
7. "Do you have DataForSEO or Semrush API access?"
8. "Is your content structured for AI extraction - self-contained sections, answer-first format, specific data?"