Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Data Analysis
Token Watch
Track and analyze token usage and costs across AI providers with budget alerts, model cost comparison, optimization tips, and local data storage.
skill openclawclawhub Free
Track and analyze token usage and costs across AI providers with budget alerts, model cost comparison, optimization tips, and local data storage.
โฌ 0 downloads โ
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- LICENSE.md, README.md, manifest.yaml, SKILL.md, tokenwatch.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
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Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.2.3
Provenance
- Publisher
- vedantsingh60
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Documentation
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) โข Zero dependencies โข Works locally โข 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
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" ) # โ 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 ) # โ Budget set: daily=$1.0, weekly=$5.0, monthly=$15.0 # ๐จ BUDGET ALERT fires automatically when threshold is crossed
4. Dashboard
print(monitor.format_dashboard()) ๐ฐ 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) ๐ BUDGET STATUS Daily: [โโโโโโโโโโโโโโโโโโโโ] 42% $0.0042 / $1.00 โ Monthly: [โโโโโโโโโโโโโโโโโโโโ] 37% $0.1847 / $0.50 โ ๏ธ ๐ก OPTIMIZATION TIPS ๐ด Swap Opus โ Sonnet for non-reasoning tasks (save ~$8.20/mo) ๐ก High avg cost/call on gpt-4o โ 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: ${estimate['estimated_cost_usd']:.6f}")
7. Optimization Suggestions
suggestions = monitor.get_optimization_suggestions() for s in suggestions: savings = s.get("estimated_monthly_savings_usd", 0) print(f"[{s['priority'].upper()}] {s['message']}") if savings: print(f" โ Save ~${savings:.2f}/month")
8. Export Reports
monitor.export_report("monthly_report.json", period="month")
Supported Models (Feb 2026)
41 models across 10 providers โ updated Feb 16, 2026. ProviderModelInput/1MOutput/1MAnthropicclaude-opus-4-6$5.00$25.00Anthropicclaude-opus-4-5$5.00$25.00Anthropicclaude-sonnet-4-5-20250929$3.00$15.00Anthropicclaude-haiku-4-5-20251001$1.00$5.00OpenAIgpt-5.2-pro$21.00$168.00OpenAIgpt-5.2$1.75$14.00OpenAIgpt-5$1.25$10.00OpenAIgpt-4.1$2.00$8.00OpenAIgpt-4.1-mini$0.40$1.60OpenAIgpt-4.1-nano$0.10$0.40OpenAIo3$10.00$40.00OpenAIo4-mini$1.10$4.40Googlegemini-3-pro$2.00$12.00Googlegemini-3-flash$0.50$3.00Googlegemini-2.5-pro$1.25$10.00Googlegemini-2.5-flash$0.30$2.50Googlegemini-2.5-flash-lite$0.10$0.40Googlegemini-2.0-flash$0.10$0.40Mistralmistral-large-2411$2.00$6.00Mistralmistral-medium-3$0.40$2.00Mistralmistral-small$0.10$0.30Mistralmistral-nemo$0.02$0.10Mistraldevstral-2$0.40$2.00xAIgrok-4$3.00$15.00xAIgrok-3$3.00$15.00xAIgrok-4.1-fast$0.20$0.50Kimikimi-k2.5$0.60$3.00Kimikimi-k2$0.60$2.50Kimikimi-k2-turbo$1.15$8.00Qwenqwen3.5-plus$0.11$0.44Qwenqwen3-max$0.40$1.60Qwenqwen3-vl-32b$0.91$3.64DeepSeekdeepseek-v3.2$0.14$0.28DeepSeekdeepseek-r1$0.55$2.19DeepSeekdeepseek-v3$0.27$1.10Metallama-4-maverick$0.27$0.85Metallama-4-scout$0.18$0.59Metallama-3.3-70b$0.23$0.40MiniMaxminimax-m2.5$0.30$1.20MiniMaxminimax-m1$0.43$1.93MiniMaxminimax-text-01$0.20$1.10 To add a custom model: add it to PROVIDER_PRICING dict at the top of tokenwatch.py.
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
โ Zero telemetry โ No data sent anywhere โ Local-only storage โ Everything in .tokenwatch/ on your machine โ No API keys required โ The monitor itself needs no credentials โ No authentication โ No accounts or logins needed โ Full transparency โ MIT licensed, source code included
[1.2.3] - 2026-02-16
๐ Updated SKILL.md model table to match code: 41 models across 10 providers
[1.2.0] - 2026-02-16
โจ Added DeepSeek, Meta Llama, MiniMax providers โจ Expanded to 41 models across 10 providers โจ Updated all Anthropic/OpenAI/Google/Mistral pricing to Feb 2026 rates
[1.1.0] - 2026-02-16
โจ Added xAI Grok, Kimi (Moonshot), Qwen (Alibaba) โจ Expanded to 32 models across 7 providers
[1.0.0] - 2026-02-16
โจ Initial release โ TokenWatch โจ Pricing table for 11 models across 5 providers โจ Budget alerts: daily, weekly, monthly, per-call thresholds โจ Model cost comparison, cost estimation, optimization suggestions โจ Auto-hooks for Anthropic and OpenAI response objects โจ Dashboard, JSON export, local-only storage, MIT licensed Last Updated: February 16, 2026 Current Version: 1.2.3 Status: Active & Community-Maintained ยฉ 2026 UnisAI Community
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Docs1 Scripts1 Config
- SKILL.md
- LICENSE.md
- README.md
- tokenwatch.py
- manifest.yaml