syk8015/claudeusage

Measure whether your Claude subscription is actually paying off — from local logs, with no upload and no Git integration.
> **Status: finished (October 2026).** This was a personal research project. It works and stays public, but it is no longer developed and issues may go unanswered. The measurements below are from one account over about five weeks (2026-09-01 → 10-08).
*한국어 문서: README.ko.md — the Korean version is the working notebook, with the full measurements and the mistakes we made getting there.*
Why this exists
On a subscription you don't pay per token, so nothing tells you whether you are using it well. Most personal tools count **how much** you used. That is the wrong number: burning tokens in circles scores high. Meta ran an internal leaderboard like that and shut it down — people started idling AI to climb it. A few free profilers do look for waste, but they price it in API dollars — and on a subscription the thing that runs out is the limit gauge, which weighs tokens very differently (see below).
Tools that measure quality do exist, but they are all built for teams: they need your Git repo, your PRs, your issue tracker. That leaves a gap.
| | Team tools | claudeusage |
| --- | --- | --- |
| Who is measured | A lead measures the team | You measure yourself |
| Needs | Git repo, PRs, tracker | One local log directory |
| Measures | Return on team investment | Whether your subscription pays off |
| Price | SaaS or self-hosted | Free, runs locally |
Git-based tools track code with git blame, so anything you never committed is invisible to them. This reconstructs the edit history itself, so experiments and code you rewrote before committing still count.
What it measures
**Lines that survived.** Every edit is folded in time order, so only what was still there at the end of the session counts. Code you rewrote three turns later drops out by itself. You cannot game the score by idling.
**Waste, in money.** Rework, failures and rejections add up to 4.1% here. Money spent because conversations got long: **12.3%**. Both are API-dollar figures. Whether starting fresh conversations actually saves *limit* was never proven — a new conversation pays to re-read its context — so treat the 12.3% as a pointer, not a promise.
**What the rate limit actually eats.** Your real constraint is the limit gauge, not dollars. Cache reads are most of the cost but weigh about 1/70 of fresh input against the limit. **Saving money and saving limit are different skills.**
**What chat ate.** Limits apply to the whole account, so claude.ai chat and the mobile app drain the same gauge — while leaving no local trace. Any log-only tool undercounts. This one subtracts what the logs explain from what the gauge actually did, and checks that estimate against the per-product breakdown Anthropic returns.
What one account's data showed
Measured from about 52,000 status-line samples and 33,000 logged requests. Every multiplier names what it is divided by.
- **Cache reads weigh about 1/70 of fresh input against the limit** (per token, fitted on 107 five-hour windows). They are 97% of all tokens and most of the dollar cost, yet the limit ends up split into similar shares between output, fresh input and cache reads. Dollar-based waste reports overstate them.
- **A weekly-limit change shows up in the gauges alone.** Weekly rise ÷ five-hour rise moved from 0.099 (40 windows, 09-01→13) to 0.125 (41 windows, 09-15→10-02): 1.26×. Anthropic's announced change (bonus +50% → +25%) predicts 1.20×. This only confirms a published change; one account gives about ±3%.