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.

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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.

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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)

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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.

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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.

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Step 4: Error Handling

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

Condition	Agent Action
Missing config.json	Tell the user to copy config.example.json and fill it out.
Invalid JSON in config	Notify the user of the parse error location and ask them to fix it.
Missing required fields	List the exact missing fields (brand_name, competitors, category).
No API keys set	Ask the user to export at least 2 of the 4 supported API keys.
1 API key only	Warn the user that results are less reliable, but proceed with the run.
Transient API failure	The script auto-retries. If it fails completely, it skips the provider.
Persistent API failure	The script skips the provider gracefully. Continue the pipeline.
Zero responses	The script exits non-zero. Notify the user to check API keys or config.
Missing upstream data file	Re-run the preceding script in the pipeline (e.g., probe before analyze).

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).