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tokburn

Data community intermediate

Description

**See where your AI coding tokens actually go.**

Installation

This entry records only its repository, not the path inside it, so there is no exact command to give. Open the source below and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

README

🔥 TokBurn

**See where your AI coding tokens actually go.**

[](https://pypi.org/project/tokburn/) [](https://pypi.org/project/tokburn/) [](https://opensource.org/licenses/MIT)

The problem

You use Claude Code every day. You hit usage limits constantly. You have no idea why.

Anthropic's `/cost` command shows your current session. That's it. No history, no trends, no breakdown of what's burning your tokens. You're flying blind.

TokBurn reads the session files Claude Code already stores on your machine, calculates what your usage would cost at API rates, and shows you exactly where the waste is.

**What I found in my own data:** only **0.7%** of my 28.2M tokens were Claude actually writing code. The other 99.3% was re-reading conversation history.


Install

uvx tokburn serve

That's it. Opens a dashboard in your browser. Nothing is installed permanently.

`uvx` is the Python equivalent of `npx`. Don't have it? Run `curl -LsSf https://astral.sh/uv/install.sh | sh` (macOS/Linux) or `powershell -c "irm https://astral.sh/uv/install.ps1 | iex"` (Windows). Takes 1 second.

**Want it permanently?**

uv tool install tokburn   # or: pip install tokburn
tokburn serve

What you get

**Five numbers at the top:** equivalent API cost, total sessions, total tokens, average cost per session, and what percentage of tokens are actual Claude output (spoiler: it's less than 1%).

**Daily cost trend:** bar chart of spend over the last 30 days. See which days burned the most.

**Cost by project:** doughnut chart showing which repos are eating your budget.

**Waste insights** (four types detected):

  • 🔴 Cost outliers: sessions that cost 3x+ the median. Something went wrong.
  • 🟡 Repeated file reads: same file read 3+ times in one session. Wasted tokens.
  • 🟡 Floundering: agent spent 60%+ of tokens on tool results, not generating. It was searching, not building.

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