AI SEO Skill workflow skill. Use this skill when the user needs 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 content,' 'competitor alternative pages,' 'AI Overviews,' 'search optimization,' 'DataForSEO,' 'content at scale,' 'keyword strategy,' or 'organic traffic.' This skill covers AI-powered SEO strategy from keyword research through programmatic page generation. Do NOT use for technical implementation, code review, or software architecture and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
AI SEO Skill workflow skill. Use this skill when the user needs 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 content,' 'competitor alternative pages,' 'AI Overviews,' 'search optimization,' 'DataForSEO,' 'content at scale,' 'keyword strategy,' or 'organic traffic.' This skill covers AI-powered SEO strategy from keyword research through programmatic page generation. Do NOT use for technical implementation, code review, or software architecture and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
SKILL.md
Starts with the smallest copied file that materially changes execution
Supporting context
SKILL.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
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.
Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
Read the overview and provenance files before loading any copied upstream support files.
Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
Validate the result against the upstream expectations and the evidence you can point to in the copied files.
Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.
Imported Workflow Notes
Imported: Before Starting
Confirm the user's current SEO stack (CMS, analytics, keyword tools, content tools)
Identify their primary goal: programmatic pages at scale, competitor alternative play, AI Overviews visibility, or full-stack AI SEO
Ask about their domain authority range (DR/DA) - this changes which strategies are viable
Understand their content production capacity (solo founder vs. team vs. agency)
Check if they have Google Search Console access and DataForSEO/Semrush API credentials
Identify their top 3-5 competitors for alternative page targeting
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.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/n/a
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
assets/n/a
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
Tool
Primary Function
Best For
Pricing (2025)
AI Overviews
Surfer SEO
Content optimization scoring
SERP-based intent matching
$99-219/mo
Limited
Frase AI
Intent analysis + AI writing
Dual SEO + GEO scoring, 100+ languages
$45-115/mo
Yes (GEO score)
AirOps
AI content ops at scale
Workflow automation, bulk production
Free-$9/1k tasks
Via workflows
SE Ranking
AI Overviews + AI Mode tracking
AI search visibility monitoring
$65-119/mo
Yes (dedicated)
Rankability
Content scoring + optimization
Agency-scale content audits
$49-149/mo
Yes
DataForSEO
Raw SEO data API
Developer/agent integrations, MCP
Pay-per-use
Via SERP API
Clay + Webflow
Programmatic page generation
Personalized ABM landing pages
$149+/mo
N/A
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)
SEO audits, competitor analysis via natural language
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:
DataForSEO MCP: find top 50 keywords for [competitor] with volume > 500
GSC MCP: show current rankings for those keywords
Identify gaps where competitor ranks top 10 but you do not
Generate content briefs using Frase or Surfer
Produce content via AirOps workflows
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.
"[Competitor] vs [Your Product]" comparison pages: 5-12% conversion rate
Target Keyword Patterns (Priority Order)
Pattern
Example
Intent
Difficulty
[Competitor] alternatives
"Mailchimp alternatives"
Very High
Medium
[Competitor] vs [Your Product]
"Mailchimp vs ConvertKit"
Very High
Low-Medium
Best [category] for [wedge]
"Best email tool for creators"
High
Medium
[Competitor] pricing
"Mailchimp pricing 2026"
High
Low
Switch from [Competitor]
"Switch from Mailchimp"
Very High
Low
[Competitor] vs [Competitor]
"Mailchimp vs Constant Contact"
High
Medium
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:
Set Google Alerts for acquisitions in your category
Within 48 hours: draft "[Acquired Tool] alternatives" page
Within 2 weeks: publish full comparison + migration guide
Target the 3-6 month window of peak search volume
Imported: 3. Programmatic SEO - Scaling Pages with AI
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:
Priority
Criteria
Action
P0
BOFU, volume > 200, KD < 40
Create this week
P1
BOFU, volume > 200, KD 40-60
Create this month
P2
MOFU, volume > 500, KD < 50
Create next month
P3
AI Overview gap (ranked, not cited)
Optimize existing page
P4
TOFU, volume > 2000, KD < 40
Backlog
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
Metric
Tool
Cadence
Target
Organic traffic
GSC / GA4
Weekly
MoM growth
Rankings (top 10)
DataForSEO / SE Ranking
Weekly
20%+ of targets
AI Overview citations
SE Ranking AI Tracker
Weekly
30%+ of ranked keywords
Indexed pages
GSC Coverage
Weekly
95%+ published
Content score
Surfer / Frase
Per publish
80+
BOFU conversion rate
GA4
Monthly
3-8% alternative pages
LLM mention rate
SE Ranking LLM Tracker
Bi-weekly
Growing share of voice
Monthly Iteration Cycle
Pull GSC data via MCP: top growing + declining pages
Declining pages: check for AI Overview appearance, update freshness, re-optimize scores, add missing entities
