token-kill

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:

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:

text

Have model check orders API every hour
→ Model must understand and judge each time
→ 24 checks per day = huge costs

Correct Approach:

text

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

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

text

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.