sessions

Measure token spend from this machine's session transcripts over the last $ARGUMENTS days (30 if none given).

1. Run tokendoctor-sessions --json --days <n> and tokendoctor-replay --json --days <n>. Both are read-only and read only usage figures, timestamps, tool names and file paths from the transcripts, never message text.
2. Costs are dollars at each model's Claude API list price (lib/prices.mjs). What Claude Code bills but does not write into transcripts (summary compactions' own requests, titles, WebFetch page reads) comes from its own session totals where they hold, else an estimate. On a Pro or Max plan these are what the same work would cost on the API, not what it took from the plan's limits: lead with shares (percent), and say "at API list prices" with any dollar figure.
3. Open with the person's own number: what share of the period's cost a smaller compaction window would have saved, from the replay (the 400K row, and 200K or 300K where they save much more). Call it an upper bound: the replay cannot see the work a model does again after a summary. If the window is already at or under 400K, or almost no session passed it, say the window would change little for them, rather than leading with a small number. Then lead with the two or three largest shares: main thread against subagents, the share spent with more than 400K tokens in context, and the subagent types that cost the most. Then present each finding in one or two plain sentences with its doc link.
4. Connect findings to settings where they apply: late-compaction to autoCompactWindow, subagents-on-top-model to CLAUDE_CODE_SUBAGENT_MODEL and the Explore override. Offer /tokendoctor:audit to check and fix them. Never edit files from this skill.
5. Then read usage and draw your own conclusions from it: which tools fill the context (calls and characters of output, main thread and subagents apart), Bash calls that only printed a file (fileReads) or slept in a loop (waits), the files read most often, and how large subagent runs grow (requests, how many passed 200K context). Recommend what would cut the spend you see, in plain words with the number behind it. A recommendation is either a setting or an instruction:
   • a setting: give the exact /tokendoctor <name> <value> line, which applies it as typed (/tokendoctor alone lists them). Among them: bash-output <n> caps what one Bash call puts in context, slide on, window <n>, subagents <model>;
   • an instruction for Claude (how subagents are briefed and sized, what they report back, when to read docs, how to wait on a process): draft the few lines and say where they would go (the project's memory or CLAUDE.md). Offer /tokendoctor:optimize to apply several at once: it shows each change and applies only what the person approves. Leave choices the person made on purpose (in memory or settings) to them, with what they cost.
6. When asked about a trend or whether a change helped, run tokendoctor-sessions --by day (or --by week, with --days <n>) and compare the periods before and after. Days older than the transcripts come from the history the plugin's SessionStart hook records.
7. Say what this cannot see: per-model prices, sessions on other machines, and transcripts Claude Code has already cleaned up.