GEO With OpenClaw: How to Run AI Search Visibility Analytics From Your Terminal

The Problem: AI Search Is Eating Traditional SEO - And Most Brands Are Flying Blind

Here's a number that should worry you: over 40% of searches now trigger an AI-generated answer before the user ever sees a traditional link. Google AI Overviews, Google AI Mode, Perplexity, ChatGPT with browsing, Gemini, DeepSeek - the list of AI engines shaping what users see is growing every quarter.

Yet most SEO professionals still rely on tools built for a world of ten blue links. They track keyword rankings, monitor backlinks, analyze SERP features - all important - but none of it tells you whether ChatGPT actually recommends your product when someone asks "What's the best CRM for small businesses?"

That's the gap Generative Engine Optimization (GEO) fills. And that's what Rankscale was built to measure.

But what if you could take it further? What if your AI assistant could pull your GEO data, analyze it, flag risks, and suggest content strategies - all through a simple conversation in your terminal or messaging app?

That's exactly what the Rankscale GEO Analytics Skill for OpenClaw does.

What Is OpenClaw?

OpenClaw is an open-source AI assistant framework that runs on your own machine. Think of it as a personal AI co-pilot that connects to your tools, APIs, and workflows through modular skills - installable capabilities that extend what your assistant can do.

Skills are published on ClawHub, a public registry with thousands of community-built extensions. You install them with one command, and they just work.

OpenClaw supports multiple AI models (Claude, GPT, Gemini, etc.), runs on Mac, Linux, and Windows, and integrates with Telegram, Discord, Slack, and more. Your data stays on your machine. Your API keys stay in your config. No third-party SaaS layer in between.

Introducing the Rankscale GEO Analytics Skill

The rs-geo-analytics skill connects OpenClaw directly to the Rankscale API, giving your AI assistant full access to your brand's GEO performance data. Once installed, you can ask questions like:

• "How is my brand performing across AI engines?"
• "Which engines are we weakest on?"
• "What content gaps should I prioritize this week?"
• "Show me my reputation score and risk areas."
• "Find PR opportunities based on citation gaps."

Your assistant fetches the data, normalizes it across different API response formats, runs interpretation rules, and delivers formatted, actionable reports - complete with insights and recommendations.

No dashboard clicking. No CSV exports. No context switching. Just ask.

Getting Started: Installation in 3 Minutes

Prerequisites

• OpenClaw installed and running (setup guide)
• Rankscale PRO, AGENCY GROWTH or ENTERPRISE account (trial or essential accounts don't include REST API access)
• REST API access activated - standard for Agency and Enterprise plans. If on PRO, write an issue to our support team to request activation (usually within 24 hours)

Step 1: Install the ClawHub CLI

npm i -g clawhub

Step 2: Install the Skill

clawhub install rs-geo-analytics

That's it. The skill lands in your workspace's skills/ directory, and OpenClaw picks it up on the next session.

Step 3: Configure Your API Key

Add your Rankscale API key to your OpenClaw Gateway config:

RANKSCALE_API_KEY=rk_your_key_here

Tip: Your API key format is rk_<hash>_<brandId>. The Brand ID is embedded in the key and extracted automatically - you usually don't need to set RANKSCALE_BRAND_ID separately.

Step 4: Verify Setup

Ask your assistant to discover your brands:

"Show me my Rankscale brands."

Or run it directly:

node rankscale-skill.js --discover-brands

You'll see something like:

=======================================================
                   AVAILABLE BRANDS
=======================================================
  1. HubSpot
     ID: 1uDM0fVcOJvxvOhAnX7a
  2. Salesforce
     ID: 2ZabBNWah1drKxy9NuMB
  3. Zoho
     ID: 3CG7qW3rPTNmYZIqK2Fs
-------------------------------------------------------
  Set: export RANKSCALE_BRAND_ID=<id>
=======================================================

Feature Deep Dive: What You Can Do

1. Full GEO Report - Your Brand's AI Search Health at a Glance

The default command gives you the complete picture:

"Give me my Rankscale GEO overview."

Real output (HubSpot, March 2026):

=======================================================
                 RANKSCALE GEO REPORT
            Brand: HubSpot | 2026-03-06
=======================================================
  GEO SCORE:      70.3 / 100   [+0.2 vs last week]
  SENTIMENT:      74.2 / 100   [-0.3 vs last week]
  MENTIONS:       441           [-1 vs last week]
  CITATIONS:      659           [-20 vs last week]
  DETECTION RATE: 81.1%        [+0.3 vs last week]
  AVG. POSITION:  #3.5          [→ no change]
  ENGINES:        Mistral:82.9 | DeepSeek:80.5 |
                  ChatGPT:75.6 | Perplexity:50.5
-------------------------------------------------------
  COMPETITOR COMPARISON
  Salesforce          :  35.5 🟢 [+98% vs us]
  Zoho CRM            :  29.4 🟢 [+139% vs us]
  Pipedrive           :  23.8 🟢 [+195% vs us]
-------------------------------------------------------
  TOP AI SEARCH TERMS (139 tracked)
  1. "What are the top-rated tools for    (24 mentions)
      automating business processes?"
  2. "What is the best CRM software for   (12 mentions)
      managing customer interactions?"
  3. "Which CRM system is easiest to      (12 mentions)
      use for a small business?"
  4. "What are the top-rated marketing    (12 mentions)
      automation platforms?"
  5. "What is sales automation and how    (12 mentions)
      can it help my business grow?"
-------------------------------------------------------
  GEO INSIGHTS  [2 actions]
  [WARN] Engine visibility spread >30 pts detected.
  Root cause: Mistral scores 82.9 while Perplexity
  scores 50.5 — a 32-point gap. Engines favor
  different content signals.
  Action: Audit Perplexity's citation sources;
  optimize for their indexing signals.
  Timeline: 3–6 weeks.
=======================================================

What you see instantly:

• Your overall GEO score (70.3) and all key metrics with weekly trends
• Sentiment score (74.2/100) - how positively AI engines describe you
• 659 citations from 2,197 unique sources across the web
• Detection rate: 81.1% of AI queries include your brand
• How you compare to your top 3 competitors (with percentage delta)
• 139 tracked AI search terms with the top queries
• Prioritized action items with timelines

This is the report your weekly SEO standup needs.

2. Engine Strength Profile - Where You Win, Where You Lose

Not all AI engines treat your brand the same. ChatGPT might love you while Perplexity barely knows you exist. The engine strength profile reveals the gaps:

"Which AI engines am I strongest and weakest on?"

Real output:

-------------------------------------------------------
                ENGINE STRENGTH PROFILE
-------------------------------------------------------
  Engine       Visibility            Score
  Average      ───────────────────    70.6
-------------------------------------------------------
  Mistral Large        ██████████████████████ 82.9 ✦
  DeepSeek V3          █████████████████████  80.5 ✦
  ChatGPT              ████████████████████   75.6 ✦
  Google AI Mode       ████████████████████   74.2
  Google AI Overview   ████████████████████   73.7
  Gemini 2.5           ██████████████████     67.4
  Perplexity Sonar     ██████████████████     66.8 ▼
  GPT-5                █████████████████      63.6 ▼
  Perplexity           █████████████          50.5 ▼
-------------------------------------------------------
  ✦ Top-3 engines  ▼ Bottom-3 engines

The insight here is powerful: HubSpot scores 82.9 on Mistral Large but only 50.5 on Perplexity - a 32-point spread. That's the kind of disparity that tells you different engines weight different content signals. Perplexity, for example, heavily indexes web sources and citations. If you're underperforming there, it's likely a citation coverage issue, not a content quality issue.

The skill's built-in interpretation engine automatically flags spreads >30 points as a "significant engine disparity" and recommends targeted optimization.

3. Content Gap Analysis - What to Write Next

Stop guessing which topics to prioritize. The gap analysis cross-references your search term visibility to surface the biggest opportunities:

"What content gaps should I prioritize?"

Real output:

-------------------------------------------------------
                 CONTENT GAP ANALYSIS
-------------------------------------------------------
  LOW-VISIBILITY TERMS (<50%) — 10 found:
  What is the best CRM software...              0%
  Which CRM system is easiest to use...          0%
  What are the top-rated marketing tools...      0%
  What is the best software for...               0%
  What are the best AI tools for...              0%

  RECOMMENDATIONS:
  1. Create content targeting top 3 gap terms:
     • "What is the best CRM software for managing
        customer interactions?"
     • "Which CRM system is easiest to use for a
        small business?"
     • "What are the top-rated marketing tools?"
-------------------------------------------------------

Why this matters: These aren't hypothetical keywords from a keyword planner. These are actual queries that real users are typing into AI engines - and your brand isn't showing up. Each one is a missed conversion. The skill tells you exactly which questions to answer with new content.

4. Reputation Score - Brand Health Beyond Rankings

GEO isn't just about showing up - it's about how you show up. Sentiment analysis tells you whether AI engines describe your brand positively, negatively, or neutrally:

"What's my brand reputation score?"

Real output (HubSpot, 571 sentiment data points analyzed):

-------------------------------------------------------
              REPUTATION SCORE & SUMMARY
-------------------------------------------------------
  Score:  ██████████████████████░░░░░░░░ 74.3/100
  Status: Good   Trend: → stable

  Sentiment (571 mentions analyzed):
    Avg score: 74.3 / 100

  Top positive signals (what AI engines say):
    user-friendly (35x)
    user-friendly interface (26x)
    ease of use (23x)
    scalable (15x)
    all-in-one (14x)
    lead scoring (13x)
    all-in-one solution (13x)

  Risk areas (negative signals):
    limited customization (8x)
    can get expensive (7x)
    advanced features require paid plans (4x)
    can be expensive (3x)

  Good (74.3/100) → stable
  Monitor: pricing perception, feature gating
-------------------------------------------------------

The reputation algorithm weights three factors: base sentiment ratio (positive vs. negative mentions), engine-weighted scores (engines like Google and ChatGPT carry more weight), and severity penalties (high-frequency negative keywords penalize harder).

Here's what's powerful: look at the negative signals - "limited customization" (8x) and "can get expensive" (7x). These aren't abstract complaints. These are the exact phrases ChatGPT, Perplexity, and Gemini use when describing HubSpot to potential customers. That's actionable PR intelligence - you know exactly which narratives to counter with content, case studies, and pricing comparison pages.

Risk areas bubble up automatically. If "expensive" or "steep learning curve" start spiking in AI responses, you'll know before it becomes a PR problem.

5. Citation Intelligence - The PR Playbook

This is the crown jewel. Citation analysis tells you which external sources AI engines are pulling your brand information from, where the gaps are, and where to focus your PR and content outreach:

"Run a full citation intelligence report."

Real data (HubSpot - 5,719 total citations, 2,197 unique sources):

Top Citation Sources (what AI engines link to):
  1. techradar.com      177 citations   ★ High authority
  2. zapier.com         136 citations   ★ High authority
  3. reddit.com         103 citations   Community
  4. stacksync.com       98 citations   Integration
  5. gartner.com         98 citations   ★ Analyst
  6. monday.com          91 citations   Competitor!
  7. salesforce.com      83 citations   Competitor!
  8. forbes.com          78 citations   ★ Tier-1 media
  9. youtube.com         68 citations   Video content
  10. g2.com             62 citations   ★ Reviews
  11. linkedin.com       53 citations   Professional
  12. solutionsreview.com 42 citations  ★ Reviews
  13. uschamber.com      38 citations   Authority

Look at that list carefully. Two of HubSpot's top citation sources are competitor domains - monday.com (91) and salesforce.com (83). That means when AI engines answer CRM-related questions, they're pulling information from competitor sites and presenting it alongside HubSpot. That's a content strategy insight you won't get from traditional SEO tools.

The skill delivers five analysis sections:

A. Citation Authority Sources - Ranked by a composite score (frequency × 0.4 + engine coverage × 0.6). Your top citation sources are your most valuable content partners.

B. Citation Gaps vs. Competitors - High-authority domains that cite competitors but not you. Each one is an outreach target.

C. Engine Citation Preferences - Which sources does each engine prefer? ChatGPT and Perplexity often pull from completely different domains.

D. Citation ↔ Visibility Correlation - Does more citation coverage actually translate to better visibility? Sometimes you have citations but low visibility (content quality issue) or low citations but high visibility (you're getting lucky - it won't last).

E. PR Opportunity Targets - A prioritized list of high-authority domains to pitch, categorized by authority score, category, and suggested angle.

How It Works Under the Hood

The skill isn't just a thin API wrapper. Here's what happens when you ask for a report:

1. Four API endpoints fire in parallel: Report, Citations, Sentiment, and Search Terms. Each has independent error handling - if one fails, the rest still return.
2. Response normalization: Rankscale's API can return data in multiple formats (the API evolves). The skill handles all of them - flat fields, nested objects, floats vs. percentages, different key names. You never see a parsing error.
3. Interpretation engine: 10 built-in rules analyze your data against thresholds:
   • Citation rate below 20%? → CRITICAL - content blitz needed
   • Negative sentiment above 25%? → CRITICAL - audit and respond
   • GEO score below 40? → Full GEO audit
   • Engine spread >30 points? → Engine-specific optimization
   • Competitor ahead by >15 points? → Competitive analysis
4. Smart fallbacks: If the dedicated citations endpoint fails, the skill falls back to report-embedded citation data. If the sentiment endpoint fails, it synthesizes sentiment from the composite score. Graceful degradation, not hard failure.
5. Rate limiting and retry logic: Exponential backoff with jitter on 429s and 5xx errors. Up to 3 retries per request. Your API quota stays safe.

Real Workflow: Weekly GEO Standup in 60 Seconds

Here's how a marketing team might use this in practice:

Every Monday morning, via Telegram or Slack:

You: "Give me my weekly GEO overview for HubSpot."

OpenClaw: (runs the full report)

• GEO Score: 70.3 (+0.2)
• Detection Rate: 81.1%
• Competitors: Salesforce trailing at 35.5
• 2 action items flagged

You: "Which engines dropped this week?"

OpenClaw: (runs engine movers)

• Perplexity down 3.2 points
• GPT-5 down 1.8 points
• Mistral Large up 4.1 points ◆

You: "What should we write about to fix the Perplexity gap?"

OpenClaw: (runs content gap analysis)

• 3 high-priority terms identified
• Recommends dedicated landing pages optimized for citation inclusion

That's your weekly GEO standup. No dashboard. No slides. No 30-minute meeting. One conversation, three questions, done.

Automate It: Scheduled Reports via Cron

OpenClaw supports cron jobs, so you can automate your GEO monitoring:

Schedule a weekly GEO report every Monday at 9am.

Your assistant creates a cron job that:

1. Pulls the full GEO report
2. Runs the engine strength analysis
3. Checks for content gaps
4. Sends you a formatted summary via Telegram, Slack, or Discord

No human in the loop until action is needed.

For Agencies: Multi-Brand Monitoring

If you're an agency managing multiple brands on Rankscale, the skill supports brand switching via the --brand-id flag or RANKSCALE_BRAND_ID environment variable. Run the same analysis across all your clients:

# Morning client sweep
for brand in 1uDM0fVcOJvxvOhAnX7a 2ZabBNWah1drKxy9NuMB 3CG7qW3rPTNmYZIqK2Fs; do
  RANKSCALE_BRAND_ID=$brand node rankscale-skill.js --engine-profile
done

Or ask your assistant: "Run GEO reports for all my brands and flag any with scores below 50."

We Want Your Use Cases

This skill is open-source, published on ClawHub, and we're actively developing it. Here's what's on the roadmap:

• Competitor comparison view - side-by-side delta scores
• Scheduled report automation - auto-run weekly summaries via OpenClaw cron
• Export to PDF/CSV - shareable reports for team or client delivery
• Multi-brand dashboards - aggregate views across brand portfolios

We'd love to hear how you use it. If you're running GEO analytics through OpenClaw, share your workflow with us. The best use cases will shape the next version of the skill.

• 📧 info@rankscale.ai - feature requests, bug reports, ideas
• 🌐 rankscale.ai - sign up for a Rankscale account
• 🐙 ClawHub - install the skill: clawhub install rs-geo-analytics

TL;DR

GEO is the new SEO. And with Rankscale + OpenClaw, you don't need to learn a new dashboard - you just need to ask.

Published on the Rankscale Blog. Rankscale is the leading AI search visibility platform, tracking brand performance across ChatGPT, Perplexity, Gemini, Claude, DeepSeek, Google AI Overviews, and more.