A binary model routing system that optimizes AI performance by switching between lightweight GLM-4.7 and heavyweight GLM-5 tasks.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install glm-autoroute
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 glm-autoroute 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 GLM Autoroute?
GLM Autoroute is a model orchestration skill designed to balance efficiency and power in AI agent environments. By utilizing a binary routing logic, it sets GLM-4.7 as the default model for lightweight tasks like Q&A and status checks, while reserving the more robust GLM-5 for intensive activities like coding and analysis. This skill is a core component of the Openclaw Skills ecosystem, ensuring that agents use the right amount of compute for every task.
The system is built on the principle of escalation. When the agent detects a request that requires reasoning, deep research, or architecture planning, it spawns a GLM-5 session. Once the complex task is finalized, the agent automatically reverts to the lightweight model, maintaining a streamlined and responsive user experience.
GLM Autoroute Use Cases
- Executing routine heartbeat checks and status confirmations using the low-latency GLM-4.7.
- Spawning high-performance sessions for complex Python development and debugging with GLM-5.
- Performing deep market research and trend analysis while generating detailed markdown reports.
- Automating repetitive file lookups and formatting tasks without overloading reasoning resources.
- Managing long-term memory by distilling complex session outputs into concise insights in MEMORY.md.
How GLM Autoroute Works
- The agent identifies the task complexity based on a predefined set of routing rules.
- For lightweight requests, the agent responds immediately using the GLM-4.7 default model.
- For heavyweight tasks, the agent executes the sessions_spawn command to initialize a GLM-5 sub-agent.
- The sub-agent performs the task, writing full code to files and detailed findings to dedicated research logs.
- Key findings are summarized and added to the curated MEMORY.md file for long-term persistence.
- The sub-agent session terminates, and the main Openclaw Skills agent continues using the default lightweight model.
GLM Autoroute Setup
To implement this skill, ensure your environment is configured to access the ZAI model suite. No separate binary installation is required, as the logic is handled via session management.
# Ensure your agent configuration allows for model switching
# Verify access to zai/glm-4.7 and zai/glm-5 models
GLM Autoroute Data Schema & Taxonomy
The skill organizes data into a hierarchy of importance to prevent information overload:
| Location | Data Type | Description |
|---|---|---|
| MEMORY.md | Insights | Curated, actionable decisions and long-term project context. |
| research/ | Reports | Detailed markdown files containing full research data and analysis. |
| project_dir/ | Source Code | All generated scripts and implementation files. |
| Session Announce | Summary | Brief status updates on what was created and where files are located. |
GLM Autoroute Advanced Features
- Intelligent Escalation: Automatically shifts to GLM-5 whenever the task involves coding or multi-step reasoning.
- Session Isolation: Uses spawned sessions to prevent complex reasoning from cluttering the primary chat history.
- Multi-Layered Memory Management: Distinguishes between ephemeral task data and permanent insights.
- Code-to-File Priority: Enforces a strict policy of saving code to files rather than dumping long blocks into the chat interface.
SKILL.md
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