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analyze-ai-topics

Skill

Analyzes what users ask AI agents about and how well each topic is served. Only use when the user has Amplitude Agent Analytics instrumented in their project. Use when the user asks "what are people asking the AI", "top AI topics", "where is the AI struggling", "AI coverage gaps", "what should we im

30Analytics

Install

git clone https://github.com/amplitude/mcp-marketplace.git ~/.claude/skills/analyze-ai-topics

What is analyze-ai-topics?

Analyzes what users ask AI agents about and how well each topic is served. Only use when the user has Amplitude Agent Analytics instrumented in their project. Use when the user asks "what are people asking the AI", "top AI topics", "where is the AI struggling", "AI coverage gaps", "what should we improve in our AI", or wants product insights from AI conversation patterns.

What this can do

Capabilities declared in this component's own frontmatter — not inferred.

Inherit all session tools

Declares no tool restrictions — inherits every session tool

~94 tokens of context used while enabled, before you invoke anything

Trust

83/100 · Excellent

2 factors scored below maximum

Documentation

README · ~6 min read

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.

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