TokenWatch

Track, analyze, and optimize token usage and costs across AI providers. Set budgets, get alerts, compare models, and reduce your spend.

Free and open-source (MIT License) • Zero dependencies • Works locally • No API keys required

Why This Skill?

After OpenAI's acquisition of OpenClaw, token costs are the #1 concern for power users. This skill gives you full visibility into what you're spending, where it's going, and exactly how to reduce it.

Problems it solves:

You don't know how much you're spending until the bill arrives
No way to compare costs across providers before choosing a model
No alerts when you're approaching your budget
No actionable suggestions for reducing spend

Features

1. Record Usage & Auto-Calculate Costs

from tokenwatch import TokenWatch

monitor = TokenWatch()

monitor.record_usage(
    model="claude-haiku-4-5-20251001",
    input_tokens=1200,
    output_tokens=400,
    task_label="summarize article"
)
# ✅ Recorded: $0.00192

2. Auto-Record from API Responses

from tokenwatch import record_from_anthropic_response, record_from_openai_response

# Anthropic
response = client.messages.create(model="claude-haiku-4-5-20251001", ...)
record_from_anthropic_response(monitor, response, task_label="my task")

# OpenAI
response = client.chat.completions.create(model="gpt-4o-mini", ...)
record_from_openai_response(monitor, response, task_label="my task")

3. Set Budgets with Alerts

monitor.set_budget(
    daily_usd=1.00,
    weekly_usd=5.00,
    monthly_usd=15.00,
    per_call_usd=0.10,
    alert_at_percent=80.0   # Alert at 80% of budget
)
# ✅ Budget set: daily=$1.0, weekly=$5.0, monthly=$15.0
# 🚨 BUDGET ALERT fires automatically when threshold is crossed

4. Dashboard

print(monitor.format_dashboard())

💰 SPENDING SUMMARY
  Today:   $0.0042  (4 calls, 13,600 tokens)
  Week:    $0.0231  (18 calls, 67,200 tokens)
  Month:   $0.1847  (92 calls, 438,000 tokens)

📋 BUDGET STATUS
  Daily:   [████░░░░░░░░░░░░░░░░] 42% $0.0042 / $1.00 ✅
  Monthly: [███████░░░░░░░░░░░░░] 37% $0.1847 / $0.50 ⚠️

💡 OPTIMIZATION TIPS
  🔴 Swap Opus → Sonnet for non-reasoning tasks (save ~$8.20/mo)
  🟡 High avg cost/call on gpt-4o — reduce prompt length

5. Compare Models Before Calling

# For 2000 input + 500 output tokens:
for m in monitor.compare_models(2000, 500)[:6]:
    print(f"{m['model']:<42} ${m['cost_usd']:.6f}")

gemini-2.5-flash                           $0.000300
gpt-4o-mini                                $0.000600
mistral-small-2501                         $0.000350
claude-haiku-4-5-20251001                  $0.003600
mistral-large-2501                         $0.007000
gemini-2.5-pro                             $0.007500

6. Estimate Before You Call

estimate = monitor.estimate_cost("claude-sonnet-4-5-20250929", input_tokens=5000, output_tokens=1000)
print(f"Estimated cost: ${estimate['estimated_cost_usd']:.6f}")

7. Optimization Suggestions

suggestions = monitor.get_optimization_suggestions()
for s in suggestions:
    savings = s.get("estimated_monthly_savings_usd", 0)
    print(f"[{s['priority'].upper()}] {s['message']}")
    if savings:
        print(f"  → Save ~${savings:.2f}/month")

8. Export Reports

monitor.export_report("monthly_report.json", period="month")

Supported Models (Feb 2026)

41 models across 10 providers — updated Feb 16, 2026.

Provider	Model	Input/1M	Output/1M
Anthropic	claude-opus-4-6	$5.00	$25.00
Anthropic	claude-opus-4-5	$5.00	$25.00
Anthropic	claude-sonnet-4-5-20250929	$3.00	$15.00
Anthropic	claude-haiku-4-5-20251001	$1.00	$5.00
OpenAI	gpt-5.2-pro	$21.00	$168.00
OpenAI	gpt-5.2	$1.75	$14.00
OpenAI	gpt-5	$1.25	$10.00
OpenAI	gpt-4.1	$2.00	$8.00
OpenAI	gpt-4.1-mini	$0.40	$1.60
OpenAI	gpt-4.1-nano	$0.10	$0.40
OpenAI	o3	$10.00	$40.00
OpenAI	o4-mini	$1.10	$4.40
Google	gemini-3-pro	$2.00	$12.00
Google	gemini-3-flash	$0.50	$3.00
Google	gemini-2.5-pro	$1.25	$10.00
Google	gemini-2.5-flash	$0.30	$2.50
Google	gemini-2.5-flash-lite	$0.10	$0.40
Google	gemini-2.0-flash	$0.10	$0.40
Mistral	mistral-large-2411	$2.00	$6.00
Mistral	mistral-medium-3	$0.40	$2.00
Mistral	mistral-small	$0.10	$0.30
Mistral	mistral-nemo	$0.02	$0.10
Mistral	devstral-2	$0.40	$2.00
xAI	grok-4	$3.00	$15.00
xAI	grok-3	$3.00	$15.00
xAI	grok-4.1-fast	$0.20	$0.50
Kimi	kimi-k2.5	$0.60	$3.00
Kimi	kimi-k2	$0.60	$2.50
Kimi	kimi-k2-turbo	$1.15	$8.00
Qwen	qwen3.5-plus	$0.11	$0.44
Qwen	qwen3-max	$0.40	$1.60
Qwen	qwen3-vl-32b	$0.91	$3.64
DeepSeek	deepseek-v3.2	$0.14	$0.28
DeepSeek	deepseek-r1	$0.55	$2.19
DeepSeek	deepseek-v3	$0.27	$1.10
Meta	llama-4-maverick	$0.27	$0.85
Meta	llama-4-scout	$0.18	$0.59
Meta	llama-3.3-70b	$0.23	$0.40
MiniMax	minimax-m2.5	$0.30	$1.20
MiniMax	minimax-m1	$0.43	$1.93
MiniMax	minimax-text-01	$0.20	$1.10

To add a custom model: add it to PROVIDER_PRICING dict at the top of tokenwatch.py.

API Reference

`TokenWatch(storage_path)`

Initialize monitor. Data stored in .tokenwatch/ by default.

`record_usage(model, input_tokens, output_tokens, task_label, session_id)`

Record a single API call. Returns TokenUsageRecord with calculated cost.

`set_budget(daily_usd, weekly_usd, monthly_usd, per_call_usd, alert_at_percent)`

Configure spending limits. Alerts fire automatically when thresholds are crossed.

`get_spend(period)`

Get aggregated spend. Period: "today", "week", "month", "all", or "YYYY-MM-DD".

`get_spend_by_model(period)`

Spending breakdown by model, sorted by cost descending.

`get_spend_by_provider(period)`

Spending breakdown by provider.

`compare_models(input_tokens, output_tokens)`

Compare costs across all known models. Returns list sorted cheapest first.

`estimate_cost(model, input_tokens, output_tokens)`

Estimate cost before making a call.

`get_optimization_suggestions()`

Analyze usage and return ranked suggestions with estimated monthly savings.

`format_dashboard()`

Human-readable spending dashboard with budget bars and tips.

`export_report(output_file, period)`

Export full report to JSON.

`record_from_anthropic_response(monitor, response, task_label)`

Helper to auto-record from Anthropic SDK response object.

`record_from_openai_response(monitor, response, task_label)`

Helper to auto-record from OpenAI SDK response object.

Privacy & Security

✅ Zero telemetry — No data sent anywhere
✅ Local-only storage — Everything in .tokenwatch/ on your machine
✅ No API keys required — The monitor itself needs no credentials
✅ No authentication — No accounts or logins needed
✅ Full transparency — MIT licensed, source code included

Changelog

[1.2.3] - 2026-02-16

📋 Updated SKILL.md model table to match code: 41 models across 10 providers

[1.2.0] - 2026-02-16

✨ Added DeepSeek, Meta Llama, MiniMax providers
✨ Expanded to 41 models across 10 providers
✨ Updated all Anthropic/OpenAI/Google/Mistral pricing to Feb 2026 rates

[1.1.0] - 2026-02-16

✨ Added xAI Grok, Kimi (Moonshot), Qwen (Alibaba)
✨ Expanded to 32 models across 7 providers

[1.0.0] - 2026-02-16

✨ Initial release — TokenWatch
✨ Pricing table for 11 models across 5 providers
✨ Budget alerts: daily, weekly, monthly, per-call thresholds
✨ Model cost comparison, cost estimation, optimization suggestions
✨ Auto-hooks for Anthropic and OpenAI response objects
✨ Dashboard, JSON export, local-only storage, MIT licensed

Last Updated: February 16, 2026 Current Version: 1.2.3 Status: Active & Community-Maintained

Token Watch OpenClaw Plugin & Skill | ClawHub

What this skill does

Install

Full SKILL.md

TokenWatch

Why This Skill?

Problems it solves:

Features

1. Record Usage & Auto-Calculate Costs

2. Auto-Record from API Responses

3. Set Budgets with Alerts

4. Dashboard

5. Compare Models Before Calling

6. Estimate Before You Call

7. Optimization Suggestions

8. Export Reports

Supported Models (Feb 2026)

API Reference

TokenWatch(storage_path)

record_usage(model, input_tokens, output_tokens, task_label, session_id)

set_budget(daily_usd, weekly_usd, monthly_usd, per_call_usd, alert_at_percent)

get_spend(period)

get_spend_by_model(period)

get_spend_by_provider(period)

compare_models(input_tokens, output_tokens)

estimate_cost(model, input_tokens, output_tokens)

get_optimization_suggestions()

format_dashboard()

export_report(output_file, period)

record_from_anthropic_response(monitor, response, task_label)

record_from_openai_response(monitor, response, task_label)