---

name: token-optimizer description: Reduce OpenClaw token usage and API costs by 85-95% through smart model routing, lazy context loading, heartbeat optimization, multi-provider support, Supports Anthropic, OpenAI, Google, and OpenRouter. version: 1.2.4 homepage: https://github.com/Asif2BD/OpenClaw-Token-Optimizer metadata: {"openclaw":{"emoji":"🪙","homepage":"https://github.com/Asif2BD/OpenClaw-Token-Optimizer","requires":{"bins":["python3"]}}}

🪙 Token Optimizer

Reduce OpenClaw token usage and API costs by 85-95%

One-Line Installation

git clone https://github.com/Asif2BD/OpenClaw-Token-Optimizer.git ~/.openclaw/skills/token-optimizer

That's it! The skill is now available. Tell your agent:

"I have the token-optimizer skill installed. Use it to optimize my token usage."

Or manually run the scripts to start saving immediately.

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What This Skill Does

Feature	Savings	Command
Context Optimization	70-90%	Loads only needed files, not everything
Model Routing	60-98%	Uses cheap models for simple tasks
Heartbeat Optimization	90-95%	Smart intervals, quiet hours
Multi-Provider	Variable	Falls back to cheaper providers
Local Fallback	100%	Zero cost when cloud APIs fail

Quick Start Commands

1. Generate Optimized AGENTS.md (Biggest Win!)

python3 ~/.openclaw/skills/token-optimizer/scripts/context_optimizer.py generate-agents
# Review AGENTS.md.optimized and replace your current AGENTS.md

2. Route Tasks to Appropriate Models

# Simple greeting → Use cheap model (Haiku/Nano/Flash)
python3 ~/.openclaw/skills/token-optimizer/scripts/model_router.py "thanks!"

# Complex task → Use smart model (Opus/GPT-4.1/Pro)  
python3 ~/.openclaw/skills/token-optimizer/scripts/model_router.py "design a microservices architecture"

3. Install Optimized Heartbeat

cp ~/.openclaw/skills/token-optimizer/assets/HEARTBEAT.template.md ~/.openclaw/workspace/HEARTBEAT.md

4. Check Token Budget

python3 ~/.openclaw/skills/token-optimizer/scripts/token_tracker.py check

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Scripts Reference

context_optimizer.py

Recommends minimal context files based on prompt complexity.

# Recommend context for a prompt
context_optimizer.py recommend "hi"
# → Load only: SOUL.md, IDENTITY.md (savings: ~80%)

context_optimizer.py recommend "analyze our codebase"
# → Load: SOUL.md, IDENTITY.md, MEMORY.md, memory/TODAY.md (savings: ~30%)

# Generate optimized AGENTS.md
context_optimizer.py generate-agents
# Creates AGENTS.md.optimized with lazy loading instructions

# View usage statistics
context_optimizer.py stats

model_router.py

Routes tasks to appropriate model tiers. Supports multiple providers.

# Auto-detect provider and route
model_router.py "read the config file"
# → cheap tier (Haiku/Nano/Flash)

model_router.py "write a Python function"
# → balanced tier (Sonnet/Mini/Flash)

model_router.py "design system architecture"
# → smart tier (Opus/GPT-4.1/Pro)

# Force specific provider
model_router.py "thanks" --provider openai
# → openai/gpt-4.1-nano

# Compare all providers
model_router.py compare

# List providers
model_router.py providers

Supported Providers:

Provider	Cheap	Balanced	Smart
Anthropic	claude-haiku-4	claude-sonnet-4-5	claude-opus-4
OpenAI	gpt-4.1-nano	gpt-4.1-mini	gpt-4.1
Google	gemini-2.0-flash	gemini-2.5-flash	gemini-2.5-pro
OpenRouter	gemini-2.0-flash	claude-sonnet-4-5	claude-opus-4

heartbeat_optimizer.py

Manages heartbeat check intervals with quiet hours.

# Plan which checks should run now
heartbeat_optimizer.py plan

# Check if specific type should run
heartbeat_optimizer.py check email
heartbeat_optimizer.py check calendar

# Record that a check was performed
heartbeat_optimizer.py record email

# Adjust interval (seconds)
heartbeat_optimizer.py interval email 7200  # 2 hours

# Reset all state
heartbeat_optimizer.py reset

Default Intervals:

• Email: 60 minutes
• Calendar: 2 hours
• Weather: 4 hours
• Social: 2 hours
• Monitoring: 30 minutes

Quiet Hours: 23:00-08:00 (skips non-urgent checks)

token_tracker.py

Monitors daily token budget and usage.

# Check current usage
token_tracker.py check

# Get model suggestions for task type
token_tracker.py suggest general

# Reset daily tracking
token_tracker.py reset

Status Levels:

• ok — Below 80% of daily limit
• warning — 80-99% of daily limit
• exceeded — Over limit, switch to cheaper models

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Configuration

Environment Variables

Set your preferred provider's API key:

# Anthropic (default)
export ANTHROPIC_API_KEY="sk-ant-..."

# OpenAI
export OPENAI_API_KEY="sk-proj-..."

# Google
export GOOGLE_API_KEY="AIza..."

# OpenRouter (unified API)
export OPENROUTER_API_KEY="sk-or-v1-..."

The model router auto-detects which provider to use based on available keys.

Customization

Edit patterns in scripts/model_router.py:

• COMMUNICATION_PATTERNS — Patterns that always use cheap tier
• BACKGROUND_TASK_PATTERNS — Heartbeat/cron patterns
• ROUTING_RULES — Task classification rules
• PROVIDER_MODELS — Model mappings per provider

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Integration Patterns

Before Every Response

# 1. Get context recommendation
from context_optimizer import recommend_context_bundle
rec = recommend_context_bundle(user_prompt)

# 2. Load only recommended files
if rec["context_level"] == "minimal":
    load_only(["SOUL.md", "IDENTITY.md"])
    # Skip everything else!

# 3. Get model recommendation
from model_router import route_task
routing = route_task(user_prompt)

# 4. Use recommended model
model = routing["recommended_model"]

In HEARTBEAT.md

# Check if we should run any checks
result=$(python3 scripts/heartbeat_optimizer.py plan)
should_run=$(echo $result | jq -r .should_run)

if [ "$should_run" = "false" ]; then
    echo "HEARTBEAT_OK"
    exit 0
fi

# Run only planned checks
# ...

In Cronjobs

Always specify the cheapest model that can handle the task:

# Good: Use Haiku for routine tasks
cron add --schedule "0 * * * *" \
  --payload '{"kind":"agentTurn","message":"Check server health","model":"anthropic/claude-haiku-4"}' \
  --sessionTarget isolated

# Bad: Using Opus for simple checks (60x more expensive!)

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Expected Savings

Why 85-95% Savings? (v1.2.0 Analysis)

Combined effect is multiplicative:

• Context reduction: ~78% (loads 22% of original)
• Model cost reduction: ~64% (pays 36% of original rate)
• Combined: 1 - (0.22 × 0.36) = 92% savings

Example: 100K tokens/day workload

Strategy	Context	Model	Monthly Cost	Savings
Baseline (no optimization)	50K	Sonnet	$9.00	0%
Context optimization only	11K	Sonnet	$2.00	78%
Model routing only	50K	Mixed	$3.20	64%
Both (this skill)	11K	Mixed	$0.72	92%
+ Local fallback (offline)	Any	Local	$0.00	100%

Cronjob Savings

Using Haiku instead of Opus for 10 daily cronjobs:

• Opus: 10 × 5K tokens × $15/MTok = $0.75/day = $22.50/month
• Haiku: 10 × 5K tokens × $0.25/MTok = $0.0125/day = $0.38/month
• Savings: $22/month per agent (98% reduction)

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Troubleshooting

Scripts fail with "module not found" → Ensure Python 3.7+ is installed. Scripts use stdlib only.

State files not persisting → Check ~/.openclaw/workspace/memory/ exists and is writable.

Routing suggests wrong tier → Customize patterns in scripts/model_router.py.

Budget shows $0.00 → Token tracker needs manual usage recording or integration with session_status.

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Files Included

token-optimizer/
├── SKILL.md              # This file
├── README.md             # Quick start guide
├── CHANGELOG.md          # Version history
├── LICENSE               # MIT License
├── scripts/
│   ├── context_optimizer.py   # Context loading optimization
│   ├── model_router.py        # Multi-provider model routing
│   ├── heartbeat_optimizer.py # Heartbeat interval management
│   └── token_tracker.py       # Budget monitoring
├── assets/
│   ├── HEARTBEAT.template.md  # Drop-in heartbeat template
│   ├── cronjob-model-guide.md # Cronjob model selection guide
│   └── config-patches.json    # Advanced config examples
├── docs/
│   └── RESEARCH-NOTES.md      # Research and methodology
└── references/
    └── PROVIDERS.md           # Provider comparison guide

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Requirements

• Python 3.7+ (stdlib only, no external dependencies)
• OpenClaw installation
• Write access to ~/.openclaw/workspace/memory/

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Credits

Part of the SuperSkills collection for OpenClaw.

Created by:

• Oracle — Research, analysis, and documentation
• Morpheus — Code review and publication

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"The best token is the one you don't spend." 🪙