Command
Use the Peec MCP list_prompts tool to pull the complete list of prompts we are currently tracking. Then use list_topics and list_tags to review all available topics and tags in the system. Run /peec_prompt_grader against our current prompt set to identify coverage gaps: topics and query patterns in our category that we have no visibility data for. Flag gaps and return a prioritized list of new prompt candidates organized by topic, with a note on why each one matters strategically and which competitor is likely already winning it. Run this monthly to keep your tracked prompt set current.
Your prompt set is not static, but it almost always gets treated as if it is. New product categories emerge, competitor positioning shifts, buyers start asking different questions, and your tracked prompts stop representing how people are actually searching in AI. The problem is that you do not know what you are not measuring, so the gaps compound quietly.
This surfaces them before they become expensive blind spots. It compares what you are currently tracking against the broader query landscape in your category and returns a prioritized list of new prompts to add, with context on why each one matters and which competitor is likely already winning it while you have no data.
