geo-gap-fixer

name: geo-gap-fixer description: "Audit how often LLMs recommend your brand vs competitors and generate a GEO action plan." category: "GTM Intelligence" version: "1.0.0"

GEO Gap Fixer

Agent skill that audits LLM brand visibility and converts gaps into a concrete GEO content action plan.

When to Use

Use this skill when a user wants to audit their Generative Engine Optimization (GEO) share-of-voice to know which LLM prompts their brand is losing, understand why competitors are recommended instead, and get a specific content fix plan.

Do NOT use this skill for: general SEO audits, paid ad optimization, or continuous social media monitoring. This is a point-in-time LLM visibility audit.

Step 1: Inputs

To run the audit, the user must provide API keys and a configuration file. Ensure the following are set up:

1. API Keys: At least 2 of 4 keys must be set in the environment or .env file (OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY, PERPLEXITY_API_KEY).
2. Dependencies: pip install openai anthropic google-genai
3. Config File: config.json (copied from config.example.json) must contain:
   • brand_name (string, required)
   • competitors (list of strings, required, 1-10 entries)
   • category (string, required)
   • buyer_intent_prompts (list of strings, optional. If empty, 20 prompts are auto-generated)
   • target_llms (list of strings, optional)
   • website_url (string, optional)

Step 2: Execution Pipeline

Run the following scripts in order. Stop and ask for clarification if any script fails.

1. python scripts/probe_llms.py (Optional: append --dry-run to test config without API calls)

   • Sends buyer-intent prompts to the configured LLM APIs.
   • Saves responses to data/raw_responses.json.

2. python scripts/analyze_results.py

   • Analyzes raw responses for brand mentions, ranking, sentiment, and cited domains.
   • Saves structured analysis to data/analysis.json.

3. python scripts/build_report.py

   • Assembles the final 5-section GEO audit report.
   • Saves to report/geo_audit_report.md and report/geo_audit_report.json.

Step 3: Outputs & Interpretation

The primary output is report/geo_audit_report.md. Present its findings to the user.

Key Sections to Interpret:

1. Share-of-Voice Table: A mention rate below 30% is critical. Mention rate is the % of prompts where the brand is recommended.
2. Prompt-Level Loss Log: Which exact prompts the brand lost and to whom.
3. Competitor Language Patterns: The specific adjectives LLMs use for competitors.
4. Citation Gap List: Domains LLMs cite that the brand is missing from.
5. GEO Action Plan: Prioritized fixes (🔴 Critical, 🟡 High Priority, 🟢 Growth Plays).

Direct the user to the GEO Action Plan first, as it contains the concrete steps to fix the gaps identified in the audit.

Step 4: Error Handling

If you encounter issues while executing the pipeline, follow these rules:

Limitations to keep in mind:

• This is a point-in-time audit, not a background monitor.
• Sentiment analysis uses keyword proximity, not deep NLP.
• API costs apply for each run (typically ~$0.50–$2.00).