An intelligent routing tool that selects the most efficient AI model for any given task within GitHub Copilot environments.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install dynamic-model-selector
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 dynamic-model-selector 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 Dynamic Model Selector?
The Dynamic Model Selector is a high-performance utility designed to streamline how developers interact with multiple AI models. By analyzing the complexity and nature of a user query, this skill automatically identifies whether a task requires high-reasoning capabilities or can be handled by a faster, cost-effective alternative. This ensures that you get the best results without over-provisioning resources or incurring unnecessary costs.
As part of the broader Openclaw Skills ecosystem, this tool integrates seamlessly into GitHub Copilot workflows. It serves as a decision-making layer that helps maintain high productivity by matching the right tool to the right job, from simple code snippets to complex architectural analysis.
Dynamic Model Selector Use Cases
- Balancing API costs by routing simple queries to free or low-cost models.
- Ensuring high-reasoning tasks are handled by top-tier models like Claude 3.5 Sonnet or GPT-4o.
- Automatically selecting code-optimized models for specialized generation tasks.
- Improving response times by utilizing faster models for basic chat and explanations.
How Dynamic Model Selector Works
- The user inputs a query or task description into the system interface.
- The internal classification engine analyzes the prompt for complexity, domain, and specific technical requirements.
- The skill checks available model availability and current rate limits or cost constraints.
- A recommendation is generated, suggesting the optimal model (e.g., grok-code-fast-1 for code, gpt-4o for reasoning).
- The system routes the query to the selected model or prompts the user for final confirmation.
Dynamic Model Selector Setup
Clone the repository and ensure your environment is configured for GitHub Copilot access. You can initialize the model classification engine using the following command:
# Navigate to the skill directory
cd skills/dynamic-model-selector
# Run the classifier script test
python3 scripts/classify_task.py --query "Explain quantum computing"
Review the references/models.md file to verify which models are enabled for your specific account tier.
Dynamic Model Selector Data Schema & Taxonomy
The skill organizes its logic through dedicated scripts and markdown reference tables to ensure transparency in the decision-making process.
| Component | File Path | Description |
|---|---|---|
| Classifier Logic | scripts/classify_task.py |
Python script that parses user input and assigns a complexity score. |
| Model Catalog | references/models.md |
A markdown table listing supported models, their strengths, and pricing tiers. |
| Skill Metadata | SKILL.md |
Core documentation defining the skill purpose and usage instructions. |
Dynamic Model Selector Advanced Features
- Automated model routing based on historical success rates of specific query types within Openclaw Skills.
- Multi-agent support where different parts of a complex task are routed to different specialized models.
- Custom priority hooks to favor specific providers or internal company-approved models.
- Real-time cost monitoring integration to prevent budget overruns during long development sessions.
SKILL.md
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