A precision utility to measure and compare the internal processing latency of various AI models.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ping-model
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 ping-model 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 Ping Model?
Ping Model is an essential diagnostic tool within the Openclaw Skills library, designed to provide developers with transparent performance metrics for their AI agents. It calculates the exact duration between a request's receipt and the model's response readiness, helping users select the most responsive LLM for their specific needs. This skill is particularly useful for debugging performance bottlenecks and ensuring a smooth, low-latency development experience by focusing on internal processing time rather than external network lag.
Ping Model Use Cases
- Monitoring real-time response speeds of active AI models to ensure productivity.
- Comparing latency across multiple providers like Kimi, Minimax, and DeepSeek.
- Deciding which model to use for time-sensitive coding tasks based on current performance.
- Benchmarking model performance during peak usage periods within Openclaw Skills workflows.
How Ping Model Works
- The user initiates a check via a slash command or a direct script call using Node.js.
- The skill captures the initial timestamp (T1) the moment the message is received by the agent.
- If a specific model is requested that is not active, the skill saves the current context and switches to the target model.
- Upon generating the 'Pong' response, the skill captures the final completion timestamp (T2).
- It calculates the latency delta (T2 - T1) and applies smart formatting rules (ms, seconds, or minutes).
- The original model is restored if a switch occurred, and the formatted metrics are displayed to the user.
Ping Model Setup
To use Ping Model within your Openclaw Skills environment, ensure Node.js is installed and run the following commands:
# Run a simple ping for the current model
node {baseDir}/ping-model.js
# Ping a specific model (e.g., minimax)
node {baseDir}/ping-model.js --model minimax
# Compare multiple models side-by-side
node {baseDir}/ping-model.js --compare kimi,minimax,deepseek
Ping Model Data Schema & Taxonomy
The skill organizes performance data using a clear, timestamp-based taxonomy:
| Property | Description | Format |
|---|---|---|
| Sent | The exact time the command was received | HH:MM:SS.mmm |
| Received | The time the response was ready to send | HH:MM:SS.mmm |
| Latency | The calculated processing duration | ms, s, or min |
| Model Name | The identifier of the model being tested | String |
Ping Model Advanced Features
- Automated context switching and restoration for testing non-active models without losing your place.
- Comprehensive comparison mode that ranks multiple models by speed and identifies the fastest option.
- Smart duration formatting that dynamically scales from milliseconds to minutes for high-accuracy reporting.
- Specialized output structure designed to be easily read within the Openclaw Skills interface.
SKILL.md
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