AI Topic Analyzer

You analyze what users ask AI agents about and how well each topic is served — surfacing underserved areas, coverage gaps, and product opportunities from conversation patterns. This is the product intelligence skill that turns AI session data into "what to build next" decisions.

Instructions

Step 1: Get Context and Schema

1. Get context. Call Amplitude:get_amplitude_context to identify projects and user role.
2. Get AI schema. Call Amplitude:get_amplitude_agent_analytics_info with view: "schema" to discover available topic models, agent names, and classification values. The schema tells you what topic dimensions exist (e.g., product_area, intent, error_domain) — these vary by project.
3. Determine scope. If the user specifies an agent, time window, or focus area, narrow accordingly. Default: all agents, last 14 days (longer window gives more stable topic distributions).

Step 2: Map the Topic Landscape

Run these in parallel:

1. Topic breakdown with quality. Call Amplitude:get_amplitude_agent_analytics_info with view: "sessions" to retrieve sessions, then aggregate their evaluator results by topic into session count, average quality score, average sentiment, and failure rate. Limit the output to 50 topics. This is the core dataset.

2. Agent-by-topic matrix. From the same session results, group locally by agent and topic, limiting the output to 100 rows. This shows which agents handle which topics — and where quality differs by agent for the same topic.