geo-gap-fixer

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.

1. python scripts/analyze_results.py

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

1. 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).