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SKILL.md
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name
tokenmeter
description
Track AI token usage and costs across providers. Import sessions, view dashboard, costs breakdown, and compare Max plan savings.
user-invocable
true
metadata
{"openclaw":{"emoji":"๐"}}
tokenmeter - AI Usage & Cost Tracking for OpenClaw
Track your AI token usage and costs across all providers โ locally, privately.
Slash Command Examples
/tokenmeter โ show today's dashboard
/tokenmeter how much did we spend this week? โ weekly cost report
/tokenmeter costs breakdown by model โ model split analysis
/tokenmeter import latest sessions โ pull in new usage data
/tokenmeter compare max plan savings โ show API vs subscription savings
Overview
tokenmeter is a CLI tool that tracks LLM API usage and calculates real-time cost estimates. For OpenClaw users on the Claude Max plan, it helps:
Prove Max plan value - Show what you would have paid on API billing
Monitor usage patterns - Understand which models you use most
Catch overages early - Know if you're using expensive models too much
Unified tracking - Track usage across multiple OpenClaw instances
All data stays local (SQLite at ~/.tokenmeter/usage.db). No telemetry, no cloud sync.
Installation
The bot handles everything automatically.
When first needed, the bot will:
# 1. Clone repo if it doesn't existif [ ! -d ~/clawd/tokenmeter ]; thencd ~/clawd
git clone https://github.com/jugaad-lab/tokenmeter.git
fi# 2. Setup Python venv if it doesn't existcd ~/clawd/tokenmeter
if [ ! -d ".venv" ]; then
python3 -m venv .venv
source .venv/bin/activate
pip install -e .
fi# 3. Activate and usesource .venv/bin/activate
tokenmeter import --auto
After first setup: Bot just activates venv and runs commands.
No admin action needed - the bot clones, installs, and configures automatically when you first ask it to check usage or costs.
How the Bot Uses This Tool
When You Ask: "How much did I spend this week?"
Step 1: Bot reads this SKILL.md
Skill matching triggers on keywords: "spend", "cost", "usage", "tokens"
Bot loads this entire file into context
Step 2: Bot checks if tokenmeter exists
if [ ! -d ~/clawd/tokenmeter ]; thencd ~/clawd
git clone https://github.com/jugaad-lab/tokenmeter.git
cd tokenmeter
python3 -m venv .venv
source .venv/bin/activate
pip install -e .
fi
Step 3: Bot imports latest usage
cd ~/clawd/tokenmeter
source .venv/bin/activate
tokenmeter import --auto
This reads all OpenClaw session files and logs them to the database.
Step 4: Bot runs the appropriate command
tokenmeter costs --period week
Step 5: Bot parses the output
Model Cost % of Total
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
anthropic/claude-opus-4 $741.95 65.0%
anthropic/claude-sonnet-4 $400.26 35.0%
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
Total $1,142.22
Step 6: Bot responds to you in plain English
"You spent $1,142 this week (API-equivalent). Opus cost $742 (65%), Sonnet cost $400 (35%). Your Max plan ($100/month = ~$25/week) saved you $1,117 this week."
Bot Command Reference
Standard pattern:
cd ~/clawd/tokenmeter && source .venv/bin/activate && tokenmeter [command]
Why Cache R is so large: Every time you continue a conversation, Claude reads your entire context from cache instead of you sending it fresh. Over many turns, this adds up to billions of tokens reused.
Cost breakdown:
Regular tokens: Expensive ($3-15 per 1M)
Cache Write: Slightly more expensive (~25% markup)
Cache Read: 90% cheaper ($0.30-1.50 per 1M)
This is why the cost stays low despite huge cache numbers.
