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Token Counter

该工具用于统计分析OpenClaw的token用量，核心功能为解析本地会话数据、元数据等，按类别、客户端、工具、模型多维度统计消耗。适用于查询token去向、生成日/周报告、token成本优化等场景。通过命令行参数指定时段、分析维度或会话ID，可输出文本报告，支持导出JSON或保存每日快照。

mkhaytman87
mkhaytman87
热度 85.62
最近更新 2026-05-11 16:28:05
分类：数据依赖：代码执行 / 读写文件应用场景：数据处理 / 报告撰写

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add token-counter
Metadata
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.