Track the Right Prompts Before Measuring Brand Visibility on AI.
- ✓ 7-task AEO prompt audit pipeline
- ✓ Classifies, generates, and validates prompts against your keyword + GSC data
- ✓ 6-category framework: Problem, Brand, Features, Services, Industries, Personas
- ✓ Output is a deduplicated set ready for any LLM tracking tool
Get the LLM Agent
Free .md file delivered to your inbox
Check Your Inbox!
We've sent the LLM Agent .md file with full setup instructions for Claude.
4.2
Built on a 7-task audit pipeline — not guesswork.
The same structured methodology LeadWalnut uses to build prompt sets for enterprise B2B SaaS brands tracking visibility across LLM monitoring tools. Here's what runs across the audit.
Classify & Filter
- Existing prompts classified as Relevant or Non-Relevant
- Trimmed to 7–13 words for LLM tracking accuracy
- Tagged across the 6-category framework
- Keyword & GSC data filtered (MSV, KD, impressions)
Generate New Prompts
- 5–6 prompts per category, ICP-led generation
- 3 mandatory generic discovery prompts every audit
- Naturalness, redundancy & vertical keyword QA gate
- Funnel coverage built in: TOFU + MOFU + BOFU
Validate, Curate & Finalize
- Every prompt mapped against your keyword & GSC data
- Validation tiers: Double Signal, Single, Strategic, Weak
- Overlap analysis — drop prompts triggering same response
- Final deduplicated set ready for any LLM tracking tool
From Download to Tracked Prompt Set in 4 Steps
No coding required. No complex setup. Just follow the process.
Get the Agent
Enter your details above. We'll email you the .md agent file.
Open in Claude
Upload the .md file to Claude.ai or Claude Desktop. No MCP integration required.
Upload Your 3 Files
Existing tracked prompts, keyword export (Ahrefs / Semrush / any tool), and GSC query export.
Plug Into Your Tracker
Get a multi-sheet Excel with the final validated prompt set. Plug into any LLM tracking tool.
This Agent Must Run on Claude — Not ChatGPT, Gemini, or Other AI Tools
The agent is built for Anthropic's Claude and depends on its long-context reasoning to handle hundreds of prompts and large keyword files in a single audit. Running it elsewhere will produce truncated classification, broken validation tiers, and inconsistent category coverage.
Wrong prompts measure nothing. Right prompts measure everything.
Most LLM brand visibility programs start with prompts pulled from gut feel — missing categories, missing funnel coverage, ignoring real GSC search behavior. The agent applies the same 6-category, validation-tiered methodology LeadWalnut uses for enterprise B2B SaaS clients tracking visibility across LLM monitoring tools.
| Dimension | Manual / Gut-Feel Prompt Selection | LeadWalnut LLM Agent |
|---|---|---|
| Approach | Subjective, ad-hoc list | 7-task structured audit pipeline |
| Validation | None — tracking blind | Keyword volume + GSC impressions tiers |
| Category Coverage | Skewed, often missing 2–3 categories | Forced 6-category coverage with min thresholds |
| Funnel Coverage | Mostly TOFU, BOFU often missed | TOFU + MOFU + BOFU per category guaranteed |
| Prompt Hygiene | Long, unnatural, redundant | 7–13 words, naturalness + redundancy QA gate |
| Output | Spreadsheet of guesses | Multi-sheet Excel, ready for any LLM tracking tool |
What happens when prompt tracking is built on real data.
Prompt audits LeadWalnut has run for enterprise B2B SaaS clients tracking visibility across LLM monitoring tools.
- Prompt audits across SASE, Quantum Security, AI in Cybersecurity, OT Security & NGFW
- Existing prompts trimmed from 18–25 words to 7–13 word LLM-tracking format
- Final prompt sets plugged directly into the client's LLM tracking tool
- Prompt audits across Remote Access for Education, Healthcare & Patch Management
- Persona logic standardized across CISO, Decision Maker & Recommender roles
- Vertical-specific prompts mapped to keyword clusters per industry
Built for the teams driving rankings.
Teams shipping content built to win brand visibility across ChatGPT, Perplexity, AI Overview, and Gemini.