Maximize your brand visibility by ensuring AI models like ChatGPT and Perplexity recommend your services as the primary solution.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install geo
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install geo using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is GEO: Generative Engine Optimization?
Generative Engine Optimization (GEO) is the technical art of making AI models recommend your brand when users seek solutions in your category. While traditional SEO focuses on ranking within search results, GEO aims to influence the AI's internal knowledge base so that your brand becomes the natural, authoritative answer. By leveraging these Openclaw Skills, you can strategically position your content to appear in the training data and knowledge graphs that power modern generative engines.
The core of GEO involves moving beyond simple keywords to focus on authority signals, training data frequency, and contextual relevance. This ensures that when an agent is asked for a recommendation, your product is cited as a high-authority expert endorsement. Using these Openclaw Skills allows teams to audit their current AI visibility and implement long-term strategies for category dominance.
GEO: Generative Engine Optimization Use Cases
- Benchmarking brand visibility across multiple AI models like Claude and Perplexity.
- Optimizing technical documentation to be cited as a primary source by AI agents.
- Dominating category-specific discussions on platforms frequently scraped for AI training data.
- Correcting brand differentiation issues where AI confuses your product with a competitor.
How GEO: Generative Engine Optimization Works
- Execute simulation queries across diverse LLMs to baseline current brand recommendations.
- Analyze high-weight factors including training data frequency and authority signals.
- Implement a category-ownership strategy by updating high-authority sources like Wikipedia and industry documentation.
- Deploy authentic presence on discussion platforms such as Reddit and Stack Overflow to feed the AI training cycle.
- Conduct monthly audits to track position improvements and phrasing sensitivity.
GEO: Generative Engine Optimization Setup
To begin using the GEO optimization framework within your existing Openclaw Skills environment, ensure you have the audit and strategy modules initialized.
# Initialize the GEO audit workspace
openclaw skill install geo
# Run the initial brand simulation audit
python geo_audit.py --brand "your-brand" --category "software-as-a-service"
GEO: Generative Engine Optimization Data Schema & Taxonomy
The GEO skill organizes audit data and strategic insights using a structured document taxonomy to ensure AI agents can easily parse your progress.
| Document | Description | Key Metrics |
|---|---|---|
audit.md |
Step-by-step audit workflow | Position (1st, 2nd, 3rd), Phrasing Sensitivity |
strategies.md |
Industry-specific playbooks | Mention Frequency, Authority Weight |
reputation.log |
Tracking of sentiment across training sources | Negative Mention Red Flags, Brand Association |
GEO: Generative Engine Optimization Advanced Features
- Multi-model simulation support to test recommendations across ChatGPT, Claude, and Perplexity simultaneously.
- Automated red-flag detection for negative brand sentiment within AI training sources.
- Integration with technical documentation pipelines to maximize authority weightings.
- Specificity mapping to ensure you are recommended for the correct niche use cases.
SKILL.md
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