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

Real-World Cost Reduction Cases

Case 1: Email Monitoring System

Problem: Model checks emails every 5 minutes

Case 2: Daily Report Generation

Scenario: Generate reports every 30 minutes (2000 tokens/call)

Examples

Example 1: Compressing Large Memory

Scenario: After many conversations, memory.md has grown to hundreds of thousands of characters

Solution:

1. Execute /compress command
2. System removes trivial details while preserving core information
3. 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 costs

Correct Approach:

Script checks order API every hour
Notify model only on new orders
Model handles decision-making only

Savings: 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 /compress once daily - Prevent memory bloat
• Use /new for long conversations - Start fresh after 1+ hours
• Use /stop on 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

4. Regular Token Usage Audit

• Review billing distribution
• Identify high-cost tasks for optimization
• Adjust model configuration and scripts

❌ Common Token Wastage Patterns

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 lower

Combining 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.