tokenhabit 1.2.1

Scan your Claude Code logs and find the habits silently burning your tokens. No LLM calls, no dependencies, runs offline.

tokenhabit

What's leaking your Claude Code tokens? Scan your local logs and find out in one command.

ccusage tells you how much you spent. tokenhabit tells you which habits spent it — and how to stop.

No LLM calls. No dependencies. Runs fully offline on your own ~/.claude logs.

한국어 README →

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_{Sample run on synthetic logs — your real numbers will differ.}

$ uvx tokenhabit

════════════════════════════════════════════════════════════════
tokenhabit — habit scan   2026-06-15 17:29
Window: last 7d  |  session files: 236  |  analyzed: 236
════════════════════════════════════════════════════════════════

[Totals]  tokens: 9,166,840  |  input: 4,775,023  |  output: 4,390,643
          cache hits: 1,251,474,403 (95.3%)

  Token Waste Score: C  —  ~14% of your tokens were likely wasted (8,370,759 tok)

[Detected habits]  (by catalog ID, most frequent first)
────────────────────────────────────────────────────────────────

  [H1-03] Compaction overrun (token pile-up)  ×217
  est. waste: ~3,255,000 tokens
  fix: Run /compact [focus] manually before you hit ~50K tokens.

  [H1-01] Topic drift / marathon session  ×221
  est. waste: ~2,210,000 tokens
  fix: At ~35 min / ~50K tokens, /compact or /clear and start a fresh session.

  [H4-03] Cache-kill switch (cache hit-rate crash)  ×78
  est. waste: ~1,638,000 tokens
  fix: Avoid switching model/effort mid-session. Open a new session when you must switch.

  ... (3 more)

  Share: I was wasting ~14% of my Claude Code tokens. Top leak: Compaction overrun. — tokenhabit

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Quick start

No install needed:

uvx tokenhabit            # with uv  (recommended)
pipx run tokenhabit       # with pipx

Or install it:

uv tool install tokenhabit
# or
pip install tokenhabit

Then just run tokenhabit. It scans ~/.claude/projects/*.jsonl for the last 7 days and prints your report.

Prefer the bleeding edge? Run straight from the repo: uvx --from git+https://github.com/epoko77-ai/tokenhabit tokenhabit

Usage

tokenhabit                      # last 7 days, all projects
tokenhabit --days 14            # last 14 days
tokenhabit --project /path      # a single project directory
tokenhabit --session run.jsonl  # a single session file
tokenhabit --lang ko            # Korean report
tokenhabit --json               # machine-readable (CI / piping)
tokenhabit --ccusage            # also show `npx ccusage daily` totals

What it detects

tokenhabit reads your raw session logs and flags the habits that quietly burn tokens. The ten it can measure directly from logs:

ID	Habit	Fix
H1-01	Topic drift / marathon session	/clear or /compact at topic switches
H1-03	Compaction overrun (token pile-up)	Manual /compact [focus] before ~50K
H2-01	Re-reading the same file	Reference what's already in context
H2-02	Dumping full logs / stdout flood	Filter with grep/head first
H2-04	Stranded web results (signal)	Delegate research to a subagent
H4-03	Cache-kill switch (hit-rate crash)	Don't switch model/effort mid-session
H5-04	Inviting verbose output	Cap output ("in 2 lines")
H8-01	Main-thread exploration	Delegate sweeps to a subagent
H8-02	stdout flood (large Bash output)	Pipe to head/save to file
H8-03	Subagent overuse (signal)	Delegate only big independent work

These are 10 of a larger 28-pattern habit catalog (signal = frequency-only, not scored into the waste total). The remaining patterns (prompt clarity, CLAUDE.md hygiene, MCP setup, …) can't be judged from logs alone — for full interactive coaching, see the Claude Code skill below.

How the score works

The Token Waste Score is the share-worthy headline: estimated wasted tokens as a percentage of your billable work tokens (input + output + cache creation). Cache reads are deliberately excluded from the denominator — they're cheap and so voluminous they'd dilute every score to ~1%.

All numbers are trend-spotting approximations, not exact billing. The point is to surface which habit dominates, not to reconcile your invoice.

How it differs from ccusage

	ccusage	tokenhabit
Question	How much did I spend?	Which habits spent it?
Output	Cost/token totals	Ranked habits + copy-paste fixes
LLM calls	none	none
Use them together	tokenhabit --ccusage shows both

They're complementary. ccusage measures; tokenhabit diagnoses and prescribes.

The Claude Code skill

tokenhabit also ships as a Claude Code skill for interactive coaching across the full 28-pattern catalog (session triage, prompt rewriting, runtime guard hooks). See skill/. The CLI is the fast offline scan; the skill is the deeper coach.

Privacy

Everything runs locally. tokenhabit only reads your own ~/.claude log files and never sends anything anywhere. (The optional --ccusage flag shells out to npx ccusage, which is also local.)

License

MIT © Seunghyun Lee