A comprehensive framework for auditing and improving brand visibility within AI-powered search engines and LLM recommendations.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install brw-ai-discoverability-audit
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 brw-ai-discoverability-audit 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 AI Discoverability Audit?
The AI Discoverability Audit is a technical workflow designed to help brands navigate the shift from traditional SEO to AI Engine Optimization (AEO). As more users transition to AI assistants like ChatGPT, Claude, and Perplexity, being discoverable depends on how LLMs synthesize and recommend your entity. This Openclaw Skills tool provides a systematic approach to evaluating your brand's presence, sentiment, and authority across the leading AI platforms.
By following this audit process, developers and marketing architects can identify critical gaps where AI models might be hallucinating data, misattributing founders, or favoring competitors. It provides a structured methodology to move a brand from being invisible to becoming a cited authority in its specific industry category.
AI Discoverability Audit Use Cases
- Assessing brand recognition and accuracy across ChatGPT, Perplexity, and Claude
- Benchmarking category recommendations against top industry competitors
- Identifying and fixing factual errors or misattributions in AI training data models
- Developing an AI Engine Optimization (AEO) roadmap for increased citation frequency
- Measuring brand sentiment and authority within AI-driven search results
How AI Discoverability Audit Works
- Initialize the audit by providing company details, target customers, and primary competitors.
- Perform Direct Brand Queries to analyze how AI platforms currently define and describe the brand.
- Execute Category Queries to see if the brand is featured in "best of" lists and product recommendations.
- Run Expertise Queries to determine if brand founders or assets are cited as industry authorities.
- Conduct a Comparative Analysis to map brand performance against competitors in a structured matrix.
- Apply a Scoring Framework to quantify dimensions like Recognition, Accuracy, and Category Presence.
- Generate a Gap Analysis and prioritized Action Plan for technical and content improvements.
AI Discoverability Audit Setup
To implement this audit within your Openclaw Skills workflow, prepare your brand context and ensure your agent has web search capabilities. If web access is unavailable, manually run the generated queries in the respective AI interfaces.
# Context required for the audit
BRAND_NAME="Your Brand"
WEBSITE="https://example.com"
PRIMARY_SERVICE="SaaS Platform"
COMPETITORS=["Competitor1", "Competitor2"]
AI Discoverability Audit Data Schema & Taxonomy
The audit generates a structured report based on the following data taxonomy:
| Category | Metadata Fields |
|---|---|
| Brand Presence | Recognition (1-5), Accuracy (1-5), Sentiment (Pos/Neu/Neg) |
| Category Benchmarks | Recommendation Rank, Competitor Comparison, AI Recommendation Logic |
| Authority Metrics | Source Citations, Founder Visibility, Knowledge Panel Status |
| Actionable Gaps | Priority Level (Critical/High/Opportunity), Targeted Fixes, Roadmap Timeline |
AI Discoverability Audit Advanced Features
- Entity recognition checks to prevent brand confusion with similar companies
- Multi-LLM cross-referencing for consistent brand messaging analysis
- Automated scoring system to track AI discoverability improvements over time
- Integration with technical schema and structured data recommendations
- Specialized query templates for localized and global Openclaw Skills implementations
SKILL.md
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