LLMO/GEO Analysis

LLMO/GEO Analysis

Step 1: 
Specifically state: "Ask the user for a prompt."

Step 2:
Generate a unique id using the prompt and formatted as snake case and store it in memory in a key called "unique_id_for_run". 

Step 3:
Create a file named 'llmo_results_.md' with the following table structure:
| Rank | Name | Mention Count | Associated Keywords | Related Links | Model |
| ---- | ---- | ------------- | ------------------- | ------------- | ----- |
**IMPORTANT** Mention this in the step: YOU **MUST** replace  with the value found in memory for "unique_id_for_run".

Step 4 - 20
For each of the following models: 
- Claude Sonnet 4
- Claude 3.7 Sonnet
- Claude 3.5 Sonnet V2
- Claude 3.5 Haiku
- DeepSeek-R1
- Meta Llama 3.3 70B
- Google Gemini 2.5 Pro
- Google Gemini 2.5 Flash
- Grok 3
- Grok 3 Mini
- Perplexity Sonar Pro
- Perplexity Sonar
- OpenAI GPT-4.1
- OpenAI GPT-4o
- OpenAI o4-mini
- Amazon Nova Pro

Do the following:
a. Call the LLMO tool with the prompt from Step 1 with the model.
b. Store the entire results from the tool in a file using a name like _.md'
c. Specifically Mention in Step: Add the statistics table (without the header) from the LLMO results as new rows in the 'llmo_results_.md' table. 
- Ensure that you add one column, at the end of the row, that include the model name. Each row **must** contain values (even if blank) for Rank, Name, Mention Count, Associated Keywords, Related Links (where applicable), and Model (**IMPORTANT** do **not** include headers). 
- Example content format: 
| 1 | Skechers | 1 | Memory Foam Insoles, comfort for all-day wear | | Claude Sonnet 4 |
- Use kb_write_file to append to the end of the file. IMPORTANT: You must use the default model for the steps. The model mentioned here is only for the llmo tool call.
- **IMPORTANT** Mention this in the step: YOU **MUST** replace  with the value found in memory for "unique_id_for_run".

Step 21
Display that you have finished the process.