An intelligent routing layer that analyzes query intent to select the most cost-effective and capable LLM for any given task.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install 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 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 Semantic Model Orchestrator?
The Semantic Model Orchestrator acts as a sophisticated middle layer for AI agents, designed to solve the challenge of balancing performance with cost. By leveraging semantic analysis and vector embeddings, it evaluates the complexity of incoming queries to determine the appropriate model tier. This ensures that high-stakes reasoning is handled by top-tier models while routine tasks are offloaded to more economical alternatives within the ecosystem of Openclaw Skills.
This skill is particularly valuable for developers building complex agentic workflows where utilizing a high-end model for every single interaction would be prohibitively expensive or inefficient. By categorizing intent into three distinct tiers, the orchestrator streamlines execution and maximizes the utility of modern LLM providers.
Semantic Model Orchestrator Use Cases
- Determining if a complex architectural coding task requires a high-reasoning model like Claude 3.5 Sonnet or a faster model.
- Reducing API expenditure by automatically routing simple chat queries to lower-cost tiers like DeepSeek.
- Scaling AI agent workflows by dynamically selecting models based on real-time task complexity.
- Improving response latency by avoiding overkill on simple informational requests through smaller, faster models.
How Semantic Model Orchestrator Works
- The AI agent receives a user query and passes it to the analyze_and_route tool.
- The orchestrator uses vector embeddings to perform semantic intent recognition against known complexity patterns.
- The query is classified into one of three specific tiers: Elite, Balanced, or Basic.
- The skill returns a JSON object containing the suggested model identifier and the assigned tier.
- The agent proceeds to execute the primary task using the suggested model, ensuring maximum efficiency within the Openclaw Skills framework.
Semantic Model Orchestrator Setup
To integrate this skill into your environment, ensure your provider API keys are correctly configured in your environment variables. Install the skill using the following command:
openclaw install semantic-model-orchestrator
You can then configure your preferred models for each tier:
openclaw config set elite_model "anthropic/claude-3-5-sonnet-latest"
openclaw config set basic_model "deepseek/deepseek-chat"
Semantic Model Orchestrator Data Schema & Taxonomy
The skill maintains a structured output format for every routing decision to ensure compatibility with downstream tools.
| Field | Type | Description |
|---|---|---|
| tier | String | The classification level (ELITE, BALANCED, or BASIC) |
| suggested_model | String | The specific model string for the selected tier (e.g., gpt-4o-mini) |
| intent_category | String | The identified intent type based on semantic analysis |
| efficiency_score | Float | A calculated value representing the cost-to-performance ratio |
Semantic Model Orchestrator Advanced Features
- Rolling Adjustment: Built-in logic that refines intent keywords and routing thresholds based on user interaction history.
- Multi-Provider Support: Native integration with OpenAI, Anthropic, Gemini, and DeepSeek providers.
- ClawHub Optimization: Pre-configured defaults specifically tuned for common developer workflows in Openclaw Skills.
- Custom Complexity Thresholds: Power users can define their own semantic markers to trigger specific model upgrades.
SKILL.md
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