ping-model

What This Skill Does

The ping-model skill provides an essential diagnostic utility for OpenClaw AI agents, enabling precise measurement of model response latency. By capturing the time delta between the receipt of a command and the generation of an AI response, this tool allows developers and users to benchmark the performance of different LLM backends. The skill implements intelligent duration formatting, automatically switching between milliseconds (ms), seconds (s), and minutes (min) to ensure human-readable performance data. Beyond simple status checks, it supports a sophisticated comparison mode, allowing users to benchmark multiple models side-by-side to determine which best fits their latency requirements for specific tasks.

Installation

To integrate the ping-model skill into your environment, use the OpenClaw management utility. Run the following command in your terminal: clawhub install openclaw/skills/skills/dofbi/ping-model Ensure that your environment has the necessary permissions to execute node processes, as this skill interacts with the model layer directly to capture accurate inference timing.

Use Cases

This skill is indispensable for:

• Performance Benchmarking: Comparing the inference speed of different providers like Kimi, Minimax, or DeepSeek to optimize workflow efficiency.
• System Monitoring: Regularly checking if the current active model is experiencing high latency during peak usage times.
• Developer Optimization: Identifying whether latency bottlenecks are caused by internal model processing or network congestion, as this tool specifically isolates the T1 (received) to T2 (response ready) interval.
• Model Selection: Choosing the right model for real-time applications where response speed is critical versus complex reasoning tasks where speed might be sacrificed for accuracy.

Example Prompts

• "/ping"
• "/ping minimax"
• "/ping all"

Tips & Limitations

• Precision: Note that this tool measures the time spent within the agent's logic, excluding transit time across the network, providing an accurate measure of model-side latency.
• Context Preservation: The ping-model skill is designed to be non-intrusive. When performing cross-model tests, it automatically restores the original model context, ensuring that your workflow is not interrupted.
• Comparison Mode: Utilize the --compare flag sparingly in automated scripts, as repeated model switching may incur additional resource overhead on your inference provider.