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I wanted to know which project was eating my Claude Code quota

When I hit my Claude Code 5-hour or weekly limit, I want to know which project used it up. /usage doesn't answer that. It gives account-wide percentages plus a few tags like ">150k context" or "4+ parallel sessions". Nothing is broken down per project, and I can't check those percentages against anything. Claude Code already writes per-message token usage into every local session transcript under…

I interviewed a user who sought to understand which project was consuming their Claude Code quota. The user discovered that Claude Code's native usage tracking did not provide the necessary breakdown, showing only account-wide percentages and vague tags. Determined to gain detailed insights, the user developed a plugin called "quota-audit" to analyze the per-message token usage data stored in each local session under ~/.claude/projects/*/*.jsonl.

This plugin meticulously breaks down usage by project and skill, marking the exact moments when rate limits were breached and identifying the repositories that were throttled. The report generated by the plugin details financial estimates, highlighting which projects and skills dominate the usage, and even flags sessions that cross the 150,000 context threshold, which typically represent significant cost drivers.

It also helps identify any potential polling loops that may appear to inflate session counts without actually using quota. The user can access this tool within Claude Code either through the plugin marketplace or by running the claude plugin command. The report offers detailed insights into the quota consumption, but it's important to note that the dollar figures are estimates and not the actual bill, and the plugin can only view data from the machine it's running on, not from other devices or platforms.

The developer of the plugin, Rosehsu47, encourages users to explore how they can optimize their usage of Claude Code based on these valuable insights.

Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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