- name
- token-kill
- description
- Reduce OpenClaw token consumption by 95%+ using three optimization techniques (slash commands, script-first principle, and model tiering)
Token Kill - OpenClaw Token Optimizer
Need help optimizing your OpenClaw token usage costs? This Skill will guide you through three powerful optimization techniques to dramatically reduce token consumption.
Based on real-world case studies, applying these optimization techniques can reduce token consumption from $200+/day to $10/day, achieving a 95%+ cost reduction.
Three Core Token Optimization Techniques
1️⃣ Slash Commands Optimization
/new- Start a fresh conversation and clear old context (saves 50,000+ tokens)/compress- Compress memory by keeping important info and forgetting details (saves 30,000+ tokens)/stop- Immediately stop current task to prevent further token consumption/restart- Restart the system to clear lag and resolve issues
2️⃣ Script-First Principle
Core Philosophy: AI is your brain, not your hands
Automate with scripts instead of using the model for mechanical tasks:
- 📧 Email Checking - Scripts monitor emails; AI only notified of new messages ($100+/month → <$1/month)
- 🌤️ Weather Queries - Direct API calls, zero token consumption
- 📊 Data Fetching - Scripts retrieve data; AI only handles formatting
- ⏰ Scheduled Tasks - Scripts execute; prevent AI from polling
- 🔄 Data Processing - Script handles transformations
3️⃣ Model Tiering Strategy
Use premium models for complex tasks, budget models for simple ones
| Complexity | Recommended Model | Cost | Use Cases | Savings |
|---|---|---|---|---|
| 🔴 High | GPT-4 / Claude | $0.03/1k tokens | Code generation, creative writing, complex reasoning | Baseline |
| 🟡 Medium | GPT-3.5-Turbo / Ernie | $0.0005/1k tokens | General tasks, text editing | 98% |
| 🟢 Low | Qwen, Tongyi (Budget Models) | $0.00001/1k tokens | Data processing, report generation, formatting | 99.97% |
Real-World Cost Reduction Cases
Case 1: Email Monitoring System
Problem: Model checks emails every 5 minutes
| Approach | Monthly Cost |
|---|---|
| ❌ Model Polling | $100+/month |
| ✅ Script + AI Notification | <$1/month |
| Savings | 99% |
Case 2: Daily Report Generation
Scenario: Generate reports every 30 minutes (2000 tokens/call)
| Model | Daily Cost | Monthly Cost | Savings |
|---|---|---|---|
| GPT-4 | $2.88 | $86 | Baseline |
| GPT-3.5 | $0.048 | $1.44 | 98% |
| Qwen | $0.001 | $0.03 | 99.97% |
Examples
Example 1: Compressing Large Memory
Scenario: After many conversations, memory.md has grown to hundreds of thousands of characters
Solution:
- Execute
/compresscommand - System removes trivial details while preserving core information
- Memory size reduced by 30-50%
Result: Reduced context loading on each turn, saves 30,000+ tokens
Example 2: Replacing AI with Scripts
Scenario: Need to check for new orders every hour
Wrong Approach:
Have model check orders API every hour
→ Model must understand and judge each time
→ 24 checks per day = huge costsCorrect Approach:
Script checks order API every hour
Notify model only on new orders
Model handles decision-making onlySavings: Script uses only CPU, saves 90%+ tokens
Example 3: Model Tiering Workflow
Scenario: Handle various complexity levels
Strategy:
- 💻 Code Writing → GPT-4 (worth the investment)
- 📝 Content Editing → GPT-3.5 (good balance)
- 📊 Report Generation → Budget Model (fully sufficient)
Result: 90% cost reduction, zero functionality loss
Guidelines
✅ Best Practices for Token Savings
1. Use Slash Commands Regularly
- Execute
/compressonce daily - Prevent memory bloat - Use
/newfor long conversations - Start fresh after 1+ hours - Use
/stopon wrong tasks - Stop immediately to prevent waste
2. Strictly Follow Script-First Principle
- ✅ Scripts handle: Scheduled checks, data fetching, API calls, data processing
- ❌ Never let AI handle: Polling, mechanical work, repetitive checks, resource-intensive operations
- 💡 Core rule: AI = decision-making and judgment; Scripts = execution and heavy lifting
3. Enforce Model Tiering
| Task Type | Model Choice | Reason |
|---|---|---|
| Code generation, deep analysis | GPT-4 | Complex tasks worth the cost |
| General tasks, text editing | GPT-3.5 | Best value proposition |
| Data processing, reports | Budget Models | Fully capable, lowest cost |
4. Regular Token Usage Audit
- Review billing distribution
- Identify high-cost tasks for optimization
- Adjust model configuration and scripts
❌ Common Token Wastage Patterns
| Bad Practice | Consequence | Solution |
|---|---|---|
| Unlimited conversation history | Growing memory = more tokens | Regular /compress or /new |
| AI polling for updates | Token burn on each check | Use scripts instead |
| Using GPT-4 for simple tasks | Overkill, high cost | Use appropriate model tier |
| Never compressing memory | Linear token cost growth | Establish compression habit |
| Continuing failed tasks | Wasted tokens | Use /stop immediately |
Token Cost Formula
Total Cost = Context Consumption + Task Consumption
Optimization Formula:
New Cost = (Original Context × 30%) + (Task Cost × 20%)
= Original Cost × (0.3 + 0.2)
= Original Cost × 0.5 or lowerCombining all three techniques achieves 95%+ cost reduction.
Key Principle
💡 Remember: High costs don't come from AI itself, but from making it do tasks it shouldn't do and remember information it shouldn't store. Assign the right tasks to the right tools, and AI becomes truly cost-effective.