Cache read is charged per API round-trip on the whole conversation so far, so a
long session's cost is driven by TURN COUNT, not message length. One measured
session spent 267M cache-read against 856k output — a ratio of 290:1. The report
priced tokens but never counted the turns spending them.
Across the 263 stored transcripts this now shows 33,508 tool turns at an 8%
batching rate, of which 4,437 (13%) decided nothing: polling a counter, running
a linter, checking whether a push landed. At 606k average context re-read per
turn that is ~2.7B cache-read tokens on turns a script could have taken.
- turns.py classifies a command as poll / lint_test / vcs_check / substantive,
deliberately conservative: state-changing commands (git commit, push) count as
substantive even though they are scriptable, so the reported waste is a floor.
A turn is only mechanical when EVERY command in it is.
- Batching is grouped by requestId, not by transcript line: one API response can
span several lines, and counting per line reports every turn as single-tool
(0% batching where the real rate is 12%).
- records.py gains TurnEfficiency (schema v3, additive — old records read as
zeros) with derived rates written into to_dict so readers need not recompute.
- report.py gains a "Turn efficiency" section pricing each mechanical kind.
- _TurnTracker owns both the totals and the open turn, after pylint flagged
_Accumulator over its attribute budget; _Accumulator.turns is now a property,
so every existing caller is unchanged.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
