OptimoClaw Token Optimizer for Openclaw

A technical advisor for OpenClaw agents that identifies token waste and provides mathematical cost-saving recommendations.

marc4211
v1.0.4
Apr 3, 2026
1
821
0

Install & Download

1. ClawHub CLI

The fastest way to install a skill directly from the registry.

npx clawhub@latest install optimoclaw-skill

2. Manual Installation

Copy the skill folder to one of these locations

Global
~/.openclaw/skills/
Workspace
<project>/skills/

Priority: Workspace > Local > Bundled

3. Prompt Installation

Copy this prompt to OpenClaw to install it automatically.

Help me install optimoclaw-skill using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).

Prefer to download?

Get the raw skill files in a ZIP archive.

What is OptimoClaw Token Optimizer?

OptimoClaw Token Optimizer is a sophisticated diagnostic utility designed to help developers control the operational costs of their AI agents. By interfacing directly with the OpenClaw CLI, this skill analyzes live configuration files and session usage metrics to pinpoint exactly where tokens are being wasted. It serves as an essential bridge between agent performance and budget management, ensuring that users can maintain high-quality outputs while minimizing unnecessary API spend.

As one of the most specialized Openclaw Skills for financial management, it provides a transparent view of model expenses across providers like Anthropic and OpenAI. The skill doesn't just suggest changes; it provides the exact math and logic behind every recommendation, allowing developers to make informed decisions about model routing, heartbeat frequencies, and context window utilization.

OptimoClaw Token Optimizer Use Cases

  • Reducing monthly API bills by optimizing heartbeat models and frequency.
  • Improving cache efficiency to take advantage of provider discounts on repeated inputs.
  • Tuning context compaction thresholds to prevent agents from becoming sluggish or expensive over long sessions.
  • Benchmarking different model tiers to find the most cost-effective routing for routine tasks.

How OptimoClaw Token Optimizer Works

  1. The skill initiates data collection by executing openclaw config get to map current agent settings and model assignments.
  2. It runs openclaw status --usage --json to retrieve real-time telemetry on token counts, cache ratios, and context utilization.
  3. It processes these metrics against a built-in rate card for various LLM providers to calculate current spend.
  4. It evaluates the data against 10 specific optimization levers, such as memory file scope and subagent concurrency.
  5. The skill generates a comprehensive report detailing identified waste, recommended values, and the specific shell commands to apply the updates.

OptimoClaw Token Optimizer Setup

To utilize this skill, ensure the OpenClaw CLI is installed and accessible in your environment. No separate installation is required beyond providing the agent with the skill definition.

# Verify OpenClaw CLI accessibility
openclaw --version

# The agent will automatically use the following commands to gather data:
openclaw config get 'agents' --json
openclaw status --usage --json

OptimoClaw Token Optimizer Data Schema & Taxonomy

The skill organizes and analyzes data based on the standard OpenClaw configuration hierarchy and session metadata:

Data Point Description
Agent Defaults Primary models, heartbeat settings, and search batch limits.
Usage Telemetry Cache reads vs. writes and context window percentages.
Rate Card A reference table of costs per million tokens for input, output, and cache.
Optimization Profiles Pre-defined configurations for Lean, Balanced, and Quality performance modes.

OptimoClaw Token Optimizer Advanced Features

  • Profile-based presets that allow users to toggle between aggressive cost-saving and high-fidelity quality modes.
  • Local model routing recommendations for Ollama to achieve zero-cost heartbeats and summaries.
  • Cache efficiency auditing to identify sessions where context loading settings are causing redundant writes.
  • Per-agent override analysis to distinguish between global defaults and specific agent requirements.

SKILL.md


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