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analyzing-expensive-users

Skill

Analyze the most expensive users in AI observability and explain why they cost so much. Use when the user asks about top spenders, expensive users, per-user LLM cost, user-level cost drivers, or patterns behind high AI observability spend.

65Machine Learning

Install

git clone https://github.com/PostHog/ai-plugin.git ~/.claude/skills/analyzing-expensive-users

What is analyzing-expensive-users?

Analyze the most expensive users in AI observability and explain why they cost so much. Use when the user asks about top spenders, expensive users, per-user LLM cost, user-level cost drivers, or patterns behind high AI observability spend.

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

~60 tokens of context used while enabled, before you invoke anything

Trust

73/100 · Good

3 factors scored below maximum

No license

Documentation

README · ~7 min read

Analyzing expensive users

Use this skill when the user wants to understand the most expensive users in AI observability. The job is not just to rank users by cost. The useful answer explains what makes the top users expensive: volume, model choice, prompt size, output size, cache behavior, retries/errors, trace type, feature or tenant dimensions, and representative trace examples.

For general cost rollups, also use exploring-llm-costs. For reading individual traces, also use exploring-llm-traces.

Tools

| Tool | Purpose |

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