Audit where a Claude Code session's cost actually went before setting a budget

You are auditing where the cost in this usage data actually went, not estimating it from a general impression of how the session felt. A specific breakdown by category is the only thing that makes a proposed reduction lever trustworthy rather than a guess dressed as an optimization.

USAGE DATA
Session used 340K input tokens, 12K output tokens, cost $4.85; 210K of the input tokens came from tool results, largely repeated reads of a single 8,000-line generated file across 11 separate turns.

WHAT KIND OF TASK THIS SESSION WAS DOING
A multi-file refactor across the reports module, run interactively over about ninety minutes.

CURRENT BUDGET TARGET, IF ONE EXISTS
No formal budget exists yet; sessions like this have informally run $2 to $3 in the past.

WHERE THE COST IS SUSPECTED TO BE CONCENTRATED
Suspect the large generated report fixture file is being re-read on every turn instead of once.

HOW OFTEN THIS KIND OF SESSION RUNS
This kind of refactor session runs a handful of times a month, not daily.

ANALYSIS
1. Break down the usage data into distinct categories: initial context load, tool-result content returned to the model (file reads, command output, search results), the model's own generated output, and any cache-read versus cache-write split if the data distinguishes them. State the actual token count and cost share per category, not just a total.
2. For the largest category, identify the specific mechanism driving it — a single large file read repeatedly across many turns instead of once, a search tool returning far more matched content than was actually needed, a subagent's full internal output getting summarized inefficiently rather than distilled — and cite the specific turn or tool call in the data where this is visible, not a general statement that 'context use was high.'
3. Check Suspect the large generated report fixture file is being re-read on every turn instead of once. against what the data actually shows — confirm or rule out each one specifically rather than assuming the suspicion was correct just because it was already flagged going in.
4. Propose one or two concrete, specific reduction levers tied directly to the mechanism identified in step two — narrowing a glob pattern, moving a noisy verification step into an isolated subagent, caching a repeatedly-read file's content instead of re-reading it, reducing an overly broad search's result count — not a generic suggestion to 'be more concise' that does not name what would actually change.
5. State what cost or token reduction each lever would plausibly achieve, and how confident that estimate is, given the actual data available.

BUDGET
Given No formal budget exists yet; sessions like this have informally run $2 to $3 in the past. and This kind of refactor session runs a handful of times a month, not daily., propose a specific per-session budget or token ceiling going forward, and one clear signal that should trigger a review if that budget is exceeded — not a vague aspiration to spend less, a number and a trigger condition.

CONSTRAINTS
- Do not propose a reduction lever that would degrade the actual task outcome for A multi-file refactor across the reports module, run interactively over about ninety minutes. without naming that tradeoff explicitly — a cheaper session that produces a worse result is not automatically the better outcome.
- If the data does not actually support attributing cost to a specific mechanism, say so rather than assigning a plausible-sounding cause to a category just because the total for it was large.

OUTPUT
The category breakdown, the root-mechanism findings, the proposed levers with estimated impact, and the going-forward budget with its trigger condition.