---

name: token-counter description: Track and analyze OpenClaw token usage across main, cron, and sub-agent sessions with category, client, model, and tool attribution. Use when the user asks where tokens are being spent, wants daily/weekly token reports, needs per-session drilldowns, or is planning token-cost optimizations and needs evidence from transcript data.

Token Counter

Overview

Use this skill to produce token usage reports from local OpenClaw data. It parses session transcripts (.jsonl), session metadata, and cron definitions, then reports usage by category, client, tool, model, and top token consumers.

Quick Start

Run:

$OPENCLAW_SKILLS_DIR/token-counter/scripts/token-counter --period 7d

Common Commands

1. Basic report:

$OPENCLAW_SKILLS_DIR/token-counter/scripts/token-counter --period 7d

1. Focus on selected breakdowns:

$OPENCLAW_SKILLS_DIR/token-counter/scripts/token-counter \
  --period 1d \
  --breakdown tools,category,client

1. Analyze one session:

$OPENCLAW_SKILLS_DIR/token-counter/scripts/token-counter \
  --session agent:main:cron:d3d76f7a-7090-41c3-bb19-e2324093f9b1

1. Export JSON:

$OPENCLAW_SKILLS_DIR/token-counter/scripts/token-counter \
  --period 30d \
  --format json \
  --output $OPENCLAW_WORKSPACE/token-usage/token-usage-30d.json

1. Persist daily snapshot:

$OPENCLAW_SKILLS_DIR/token-counter/scripts/token-counter \
  --period 1d \
  --save

This writes JSON to: $OPENCLAW_WORKSPACE/token-usage/daily/YYYY-MM-DD.json

Defaults and Data Sources

• Sessions index: $OPENCLAW_DATA_DIR/agents/main/sessions/sessions.json
• Session transcripts: $OPENCLAW_DATA_DIR/agents/main/sessions/*.jsonl
• Cron definitions: $OPENCLAW_DATA_DIR/cron/jobs.json

The parser reads assistant usage fields for token counts and uses tool-call records for attribution.

Notes on Attribution

• Tool token attribution is heuristic: assistant-message tokens are split across tool calls in that message.
• Session totalTokens may come from either session index metadata or transcript usage sums (max is used).
• Client detection is rules-based (personal, bonsai, mixed, unknown) using path/domain/email markers.

Validation

Run:

python3 $OPENCLAW_SKILLS_DIR/skill-creator/scripts/quick_validate.py \
  $OPENCLAW_SKILLS_DIR/token-counter

References

See:

• references/classification-rules.md for category/client detection logic and keyword mapping.