skills.rest
jakechen1
jakechen1COMMUNITY
Explore all skills by this author →
WebsiteGitHub

token-optimizer

Route OpenClaw tasks to cost-efficient models and manage context loading.

Updated Apr 9, 2026
Search Tags:#budget#cost-optimization#lazy-loading#heartbeat#model-routing#openclaw#token-optimizer
One-click install
npx skills add https://github.com/jakechen1/echo-research-framework --skill token-optimizer-jakechen1
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: token-optimizer
Source: https://github.com/jakechen1/echo-research-framework/tree/main/skills-available/openclaw-token-optimizer
Command: npx skills add https://github.com/jakechen1/echo-research-framework --skill token-optimizer-jakechen1

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

High token costs and inefficient resource usage in OpenClaw deployments due to loading large context and using expensive models. This Skill minimizes token consumption by routing tasks to cost-effective models and enabling lazy context loading, heartbeat optimization, and budget tracking for agents at scale.

Core Features & Use Cases

  • Smart model routing to minimize costs across single-provider and multi-provider setups.
  • Lazy context loading to load only necessary files per task.
  • Native OpenClaw 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment) integrated via config patches.
  • Budget tracking and heartbeat optimization to reduce unnecessary API calls.

Quick Start

Run the four included scripts to enable lazy context loading, model routing, heartbeat optimization, and budget tracking in your OpenClaw setup.

Frequently Asked Questions about token-optimizer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I reduce OpenClaw token costs when running agents at scale?▼

Reduce OpenClaw token costs by routing tasks to cost-efficient models, enabling lazy context loading to load only necessary files, and applying heartbeat optimization to prevent unnecessary API calls.

What is lazy context loading and how does it minimize token usage?▼

Lazy context loading minimizes token usage by loading only the specific files required per task instead of injecting the entire context window, drastically cutting down input tokens during agent execution.

Does token routing work with multi-provider OpenClaw deployments?▼

Yes, smart model routing supports both single-provider and multi-provider OpenClaw setups, automatically directing tasks to the most cost-efficient model available across your configured providers.

How do I track API budgets and manage heartbeats in OpenClaw?▼

Track API budgets and manage heartbeats using integrated Python scripts that monitor token consumption in real-time and optimize heartbeat intervals to reduce unnecessary API calls for agents at scale.

Can I apply session pruning and bootstrap size limits to OpenClaw sessions?▼

Yes, you can apply native OpenClaw features like session pruning, bootstrap size limits, and cache TTL alignment by deploying the included config patches to optimize context window management.

Related Skills

Explore More Software Engineering Skills→
alibabaalibaba
9.1k

higress-clawdbot-integration

Configure Higress AI Gateway as a model provider for Clawdbot/OpenClaw.

Official
Advanced
SeanChenRSeanChenR

cost-aware-llm-pipeline

Route LLM API tasks to cost-appropriate models and track spend.

Community
Advanced
netanel-abergelnetanel-abergel
5

token-optimizer

Reduce OpenClaw token usage through lazy context loading and cost-aware model routing.

Community
Advanced
Asif2BDAsif2BD
7

token-optimizer

Optimize OpenClaw token usage and API costs through model routing and context management.

Community
Advanced