token-doctor
SolidPersonal diagnosis of where your Claude Code + Cowork spend goes. Reads local transcripts, prints your conversation length distribution, marathon share, cache rebuild costs, and per-project diagnosis (good projects and problem projects) right in the terminal. Then offers a deeper dive that fans out parallel Haiku subagents over your most expensive (and most efficient) sessions and writes a tight Markdown report. Use when the user asks "why is my Claude spend so high", "where am I burning tokens", "diagnose my Claude habits", "audit my Claude usage", or asks for a personal token-cost diagnosis.
Install
Quality Score: 87/100
Skill Content
Details
- Author
- techwolf-ai
- Repository
- techwolf-ai/ai-first-toolkit
- Created
- 7 months ago
- Last Updated
- 4 days ago
- Language
- Python
- License
- MIT
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
context-doctor
Reduces token waste in Claude Code sessions across two axes — context footprint (auto-generates .claudeignore, guides over-read files and /clear timing) and command output (routes to a command-output proxy/hook such as rtk to trim verbose CLI stdout). In hub environments, regularly audits bloated CLAUDE.md/MEMORY.md/memory/*.md files and proposes compression. Usable standalone without a hub clone.
claude-usage
Report ground-truth Claude token consumption and estimated cost by parsing JSONL session files directly. Use when checking API spend, auditing token usage by project/session/model, generating daily/weekly/monthly cost reports, or diagnosing ccusage undercounting. Includes subagent files that ccusage ignores.
ruflo-token-audit
Use when the user asks where their Claude Code usage/tokens are going, is burning through their plan unexpectedly fast, hitting limits, or wants a breakdown of their Claude Code activity. Produces a COMPREHENSIVE usage report from local session transcripts: tokens by day/model/project, tool usage, MCP usage, subagent fan-out, web-tool calls, cache efficiency, busiest sessions, hourly activity, and a runaway-daemon cross-reference — distinguishing interactive work from automation and recommending concrete fixes.