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_context to identify projects and user role.
2. Get AI schema. Call Amplitude:get_agent_analytics_schema with include: ["filter_options", "taxonomy"] 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:query_agent_analytics_metrics with metrics: ["topics"], limit: 50. This returns each topic with session count, average quality score, average sentiment, and failure rate. This is the core dataset.

2. Agent-by-topic matrix. Call Amplitude:query_agent_analytics_sessions with groupBy: ["agent_name", "primary_topic"], limit: 100. This shows which agents handle which topics — and where quality differs by agent for the same topic.