tokenscope ⏣

See what your AI-coding session actually cost — and what's eating your context. A local, read-only CLI that parses your Claude Code session logs and shows where the money goes: model output vs. context being re-sent every turn (the hidden 60%+ of most bills).

Why this exists: I put an AI agent on a timer and it burned 136M tokens overnight, most of it re-reading its own context. tokenscope is how I found that. Read the postmortem →

$ npx @wartzar-bee/tokenscope

  tokenscope ⏣  latest session
  ──────────────────────────────────────────────────────
  Total cost   $868.84   over 967 model turns

  Where the money went
  output (model writing)     ████░░░░░░░░░░░░░░░░░░░░  16%  $137.24
  cache read (re-sent ctx)   ████████████████░░░░░░░░  66%  $577.59
  cache write (new ctx)      ████░░░░░░░░░░░░░░░░░░░░  18%  $153.67

  Context size per turn  (peak 822k · avg 404k · now 822k tokens)
  ▁▁▁▁▁▂▂▂▂▂▂▂▃▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▆▆▆▆▇▇▇▇▇▇█

  Insights
  • Re-sent (cached) context cost $577.59 (66% of spend) — context re-read every turn.
  • Peak context ~822k tokens — /compact or a fresh session would cut per-turn cost.
  • Only 16% of spend is the model's actual output.

(A real session, default Opus pricing. Your numbers will differ — prices are overridable.)

Why

Agentic coding (Claude Code, etc.) produces surprise bills, and the cause is mundane: as a session grows, the whole context is re-sent every turn, so cost balloons even when the model writes little. Existing dashboards show totals; tokenscope shows the attribution — output vs. cache-read vs. cache-write vs. fresh input, the per-turn context-growth curve, cost by model, subagent spend, and which tools fill your context — with concrete "trim this" insights.

Install / run

No install — runs via npx:

npx @wartzar-bee/tokenscope               # your most recent Claude Code session
npx @wartzar-bee/tokenscope --all         # aggregate every session
npx @wartzar-bee/tokenscope <file|dir>    # a specific session .jsonl
npx @wartzar-bee/tokenscope --json        # machine-readable
npx @wartzar-bee/tokenscope --share       # privacy-safe shareable summary (markdown + SVG card)
npx @wartzar-bee/tokenscope --share-svg   # just the SVG "cost report card"

Reads ~/.claude/projects/**/*.jsonl. Read-only, local, no network, no telemetry — open the source; nothing leaves your machine.

Share your bill (privacy-safe)

--share emits a compact summary built from aggregate numbers only — no file paths, no prompt/response content — so it's safe to paste in public:

• Markdown for Reddit / Discord / a GitHub issue (total, the output/cache-read/cache-write/fresh split with %, peak/avg context, and the headline "X% of spend was re-sent context").

• A self-contained SVG "cost report card" (--share-svg) — no binary deps; renders inline on GitHub and is trivially shareable.

• How you compare — both forms now answer "is my session unusual?" against a shipped, offline reference set of real sessions (e.g. "more cache-efficient than ~80% of measured sessions; median session re-sends 24%"). It's a reference yardstick, not a census — full honest distribution at tokenscope.pages.dev/benchmark.

Prefer not to touch a terminal flag? The same render runs entirely in your browser at the web surface in web/: paste your --json output and it draws the full report + the SVG card locally — nothing is uploaded.

Use it from an AI agent (MCP server)

There's an MCP server that exposes the same engine to AI agents / MCP clients (Claude Desktop, Claude Code, etc.) as tools: analyze_claude_cost, get_cost_benchmark, and tokenscope_share_summary. Add it to your MCP config:

{ "mcpServers": { "tokenscope": { "command": "npx", "args": ["-y", "@wartzar-bee/tokenscope-mcp"] } } }

Then ask your agent "use tokenscope to analyze my last Claude Code session." It's the same local, read-only engine — see mcp/README.md.

Pricing

Uses documented default prices (Anthropic cache multipliers: write 1.25×/2×, read 0.1× of input). Verify and override for your exact model/tier via ./.tokenscope.json:

{ "pricing": { "claude-opus-4": { "in": 15, "out": 75 } } }

Unknown models are flagged (never silently counted as $0). Token counts are read straight from the logs; cost = those counts × the prices shown.

Status / roadmap

• v0.1: Claude Code session cost + context attribution + insights. 20/20 unit tests on the cost math (npm test).

• Next (evidence-driven): per-tool/-file token attribution; daily/budget alerts; a --watch live meter; OpenAI/Codex log support.

MIT. Not affiliated with Anthropic.