Startclaw Optimizer

What This Skill Does

The OpenClaw Optimizer is a performance-driven architectural layer for Clawdbot, designed to drastically reduce operational overhead while maintaining high-quality subagent outputs. It functions as an intelligent middleware that intercepts task requests, assesses their computational complexity, and routes them to the most cost-effective LLM model—balancing between Haiku, Sonnet, and Opus. Beyond model routing, the skill manages the entire lifecycle of an AI task: from scheduling retries with exponential backoff and managing browser tab concurrency through a Governor, to compacting long-running session contexts by summarizing data once they hit the 50,000-token threshold. By implementing these structural safeguards, the OpenClaw Optimizer can reduce daily expenditure from $90 down to $3-$5, effectively achieving a 95% cost reduction without sacrificing performance. It acts as an automated governance layer for your autonomous agents.

Installation

To integrate this skill into your environment, use the command-line interface via your Clawhub terminal:

clawhub install openclaw/skills/skills/idanmann10/startclaw-optimizer

Alternatively, for local node projects, you can install the core optimizer package using npm:

npm install @startclaw/openclaw-optimizer

Ensure your local configuration file reflects the correct API keys for model access and allows the dashboard script to report to your system logs.

Use Cases

This skill is ideal for teams running high-volume web scraping tasks, complex data processing agents, or automated QA testing cycles. It excels in scenarios where agents typically waste tokens on overly complex models for simple tasks or fail due to context overflow during extended browser sessions.

Example Prompts

1. "OpenClaw, execute the end-to-end user flow test on the staging environment and optimize the model distribution to keep costs under $5 today."
2. "Optimizer, summarize the existing session context and clear the redundant browser tabs to prepare for a new research task on competitive pricing."
3. "Show me the status of the current budget and provide a breakdown of how the Task Router has allocated models for the last 24 hours."

Tips & Limitations

• Token Thresholds: The 50,000 token limit is hard-coded for stability; ensure your most critical data is prioritized, as summarization will truncate minor details.
• Monitoring: Always keep the dashboard.py script running in a secondary window to maintain visibility on circuit-breaker status.
• Preflight Hooks: Utilize custom preflight hooks to validate inputs before the Task Router takes over, as this prevents unnecessary costs for invalid task strings.