A sophisticated data analysis tool that refines AI agent dispatching rules by analyzing real-world hit rates and routing accuracy.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install routing-optimization
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install routing-optimization 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 Routing Optimization Skill?
The Routing Optimization Skill is a core technical framework designed to improve the intelligence and efficiency of agent orchestration. By leveraging dispatch logs, it identifies which routing rules are successfully connecting users to the right AI agents and which patterns require refinement. As a critical component within the Openclaw Skills ecosystem, it ensures that multi-agent systems remain performant and scalable by minimizing misrouting and latency.
This skill goes beyond static configuration by offering a dynamic feedback loop. It processes usage data to provide actionable insights, allowing developers to fine-tune keyword taxonomies and regex patterns. Whether you are managing a small team of agents or a complex enterprise bot swarm, this skill provides the analytical backbone necessary for high-precision task delegation.
Routing Optimization Skill Use Cases
- Refining agent selection for complex queries by analyzing historical success rates.
- Implementing A/B testing to compare different routing strategies before full deployment.
- Automatically identifying low-performing routing rules that cause system fallbacks.
- Dynamically adjusting agent priorities based on real-time traffic and satisfaction metrics.
How Routing Optimization Skill Works
- The system collects detailed dispatch logs including matched rules, triggered patterns, and user satisfaction outcomes.
- Hit rate analysis identifies frequently used rules and evaluates their effectiveness versus failure rates.
- Accuracy algorithms correlate routing decisions with task completion success to find the most reliable paths.
- A/B testing modules run parallel simulations to determine which configuration produces the highest user satisfaction.
- Optimization suggestions are generated, offering specific keyword additions or priority adjustments to the configuration file.
Routing Optimization Skill Setup
To integrate this into your agent environment, ensure the configuration file is present in your workspace.
# Navigate to your Openclaw workspace
cd ~/.openclaw/workspace-main/skills/agent-dispatch/
# Create or edit the optimization config
touch routing-optimization-config.json
Ensure your agent logs are being exported to a format readable by the analysis functions provided in the skill.
Routing Optimization Skill Data Schema & Taxonomy
The skill organizes its analysis using the following metrics and metadata taxonomy:
| Metric | Description | Target Threshold |
|---|---|---|
| routing_accuracy | Ratio of correct routes to total routes | > 0.9 |
| routing_coverage | Ratio of tasks routed to total tasks received | > 0.95 |
| avg_routing_time | Average latency for the dispatch decision | < 2s |
| success_rate | Percentage of successful outcomes per rule | > 0.85 |
Data is structured by rule_name, containing hits, successes, and a set of matched_patterns used for optimization.
Routing Optimization Skill Advanced Features
- Automated A/B testing with sample size and confidence interval controls.
- Dynamic priority adjustment that lowers the priority of failing rules in real-time.
- Multi-language keyword support for routing agents in diverse linguistic environments.
- Extensible PowerShell and Python implementations for cross-platform system administration.
- Future-ready integration hooks for reinforcement learning and context-aware routing.
SKILL.md
Loading
Related Openclaw Skills
Official Account Article Creation Guide (Daxiang Style)
A professional 7-step content framework designed to transform technical information into engaging, pain-point driven articles for social platforms.
Tech Feasibility Assessor
An expert AI agent that evaluates academic research for commercial viability using a rigorous five-dimensional assessment model and Technology Readiness Levels.
A-Share Quant Backtesting (AkShare)
A Python-based backtesting engine for simulating A-share quantitative strategies using AkShare market data and technical indicator analysis.
AI Market Research & Competitive Analysis
An AI-driven assistant for researchers and entrepreneurs to conduct rapid, systematic market validation and competitive intelligence.
Vercel Sandbox Browser Automation
A high-performance browser automation skill that runs agent-browser inside isolated Vercel Sandbox microVMs for scalable, serverless Chrome execution.
Feishu Meeting Call & Urgent Reminder
A powerful Feishu integration skill for sending urgent app notifications and automated phone call alerts to ensure critical messages are heard.
Featured*
