token-optimizer

What This Skill Does

The Token Optimizer for OpenClaw is a comprehensive cost-management utility designed to dramatically reduce the financial overhead of running AI agents. By implementing intelligent model routing, the tool ensures that you aren't using high-cost models like Claude 3.5 Sonnet or Opus for simple, repetitive tasks that the more cost-effective Haiku model can handle efficiently. Beyond routing, it offloads recurring API heartbeats to local or budget-friendly providers like Ollama, LM Studio, or Groq, effectively nullifying the "keep-alive" costs that often go unnoticed in cloud-native agent workflows. The skill also introduces aggressive session management and prompt caching, which trims context window bloat and optimizes token reuse, resulting in a documented cost reduction of up to 97%.

Installation

To integrate the Token Optimizer, ensure you have the OpenClaw environment initialized. Run the following command in your terminal: clawhub install openclaw/skills/skills/smartpeopleconnected/token-optimizer. Once the installation is complete, use python cli.py analyze to assess your current usage patterns and python cli.py optimize to apply the recommended settings. Always verify your configuration after changes using python cli.py verify.

Use Cases

• Cost-Constrained Development: Teams or solo developers building agents that run 24/7 and need to minimize monthly API bills from hundreds of dollars to under $50.
• Optimizing Long-Running Agents: Agents designed to monitor systems, check for file changes, or maintain status heartbeats that would otherwise consume expensive premium tokens for simple pings.
• Production Environments: Organizations needing to enforce strict budget caps per agent, with automated failsafes and rollbacks for configuration safety.

Example Prompts

1. "OpenClaw, run the Token Optimizer analysis to show me which agents are currently wasting the most tokens and suggest a migration to Haiku."
2. "Update my heartbeat configuration to use Ollama with local llama3.2 to eliminate all cloud costs for system pings."
3. "Restore my configuration from the last backup because the current model routing is too aggressive for my specific reasoning tasks."

Tips & Limitations

• Diff Preview: Always run the --dry-run flag before applying optimizations to ensure your agents retain their functional capability.
• Model Limitations: While Haiku is excellent for high-volume tasks, ensure complex multi-step reasoning tasks remain assigned to more capable models to prevent performance degradation.
• Local Providers: When switching to local heartbeats like Ollama, ensure your local system resources (RAM/GPU) are sufficient to handle concurrent agent tasks without impacting host machine performance.