An intelligent routing system that automatically selects the optimal AI model based on task complexity to balance speed, cost, and response quality.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install model-router-hook
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-router-hook 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 Model Router Hook?
Model Router Hook is a sophisticated orchestration layer designed to optimize the efficiency of AI agents. It acts as a smart gateway that analyzes user intent and task difficulty in real-time to determine whether a query requires a high-reasoning 'thinking' model or a cost-effective 'fast' model. By utilizing 14 depth signals, this system ensures that developers can deliver high-quality responses without overspending on expensive API tokens.
As a core component of the Openclaw Skills ecosystem, it provides a structured P0-P5 architecture covering everything from intent recognition to automated cost control. The system doesn't just route; it learns from every interaction, refining its dynamic thresholds based on user preferences and session history to provide an increasingly tailored experience.
Model Router Hook Use Cases
- Automatically switching to high-reasoning models for complex algorithm design or architectural analysis.
- Routing simple status checks or text summaries to lightweight models to minimize latency and cost.
- Enforcing strict daily API budgets to prevent billing spikes in production environments.
- Refining model selection logic through automated post-response quality reflection and user feedback loops.
How Model Router Hook Works
- The system captures user input and performs V2 Intent Recognition using 14 deep signals to evaluate complexity.
- Dynamic thresholds are calculated by factoring in user history, topic success rates, and global profile data.
- The router selects the most appropriate model mode (fast vs. thinking) based on the calculated complexity score.
- Integration with Openclaw Skills triggers the actual model switch within the session status.
- Post-response analysis evaluates the quality of the answer and records actual token costs for budget tracking.
- The system updates the global user profile to improve future routing decisions through self-reflection.
Model Router Hook Setup
To deploy this skill, include the main script in your environment and initialize the router with your specific budget constraints. Use the following bash command to ensure your workspace is ready:
mkdir -p ~/.openclaw/workspace/memory/model-router/
Then, initialize the router in your Python application:
from model_router_hook import create_router
router = create_router(
user_id="user_001",
daily_budget=5.0
)
Model Router Hook Data Schema & Taxonomy
The skill organizes its persistent data within the OpenClaw workspace using a structured JSON-based taxonomy:
| Data Component | File Pattern | Purpose |
|---|---|---|
| Global User Profile | user_{id}_profile.json |
Stores long-term user preferences and learned thresholds. |
| Session Memory | session_{id}_memory.json |
Maintains short-term context for active conversations. |
| Cost Tracking | cost_{user}_{month}.jsonl |
Provides itemized records of input/output tokens and actual spend. |
Model Router Hook Advanced Features
- A/B testing framework to quantify and compare the effectiveness of different routing strategies.
- Real-time monitoring dashboard for tracking learning progress, latency, and model distribution.
- Concurrent-safe operations using atomic locks for high-traffic multi-agent environments.
- Automated fallback mechanisms that trigger lower-cost models or secondary providers if primary reasoning models fail.
SKILL.md
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