A specialized skill for calculating critical control system performance metrics from simulation data arrays.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install adaptive-cruise-control-simulation-metrics
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 adaptive-cruise-control-simulation-metrics 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 Simulation Metrics?
The Simulation Metrics skill provides a standardized set of mathematical utilities for evaluating the performance and stability of control systems. By processing time-series data, it allows developers to quantify how a system responds to inputs, ensuring that simulation results meet engineering specifications. This component of the Openclaw Skills ecosystem is essential for anyone building automated testing pipelines for hardware-in-the-loop or software-defined control logic.
Whether you are tuning a PID controller or validating a complex robotic trajectory, this skill automates the extraction of key indicators. It transforms raw simulation logs into actionable insights, helping identify if a system is too slow, oscillates excessively, or fails to reach its target value accurately.
Simulation Metrics Use Cases
- Evaluating PID controller tuning parameters during automated simulations.
- Validating aerospace or robotic control logic against strict performance requirements.
- Benchmarking system stability by measuring settling time and steady-state error.
- Generating automated performance reports within Openclaw Skills workflows.
How Simulation Metrics Works
- The skill ingests two primary data arrays: a list of time timestamps and a corresponding list of observed values.
- A target value (setpoint) is defined to serve as the reference for all performance calculations.
- The Rise Time function identifies the interval between 10% and 90% of the target value.
- Overshoot logic scans for the maximum peak to calculate the percentage exceeding the setpoint.
- Settling Time is determined by finding the point where the signal remains within a specified tolerance band (e.g., 2%).
- Steady-State Error is calculated by averaging the final portion of the data to find the offset from the target.
Simulation Metrics Setup
To integrate this capability into your Openclaw Skills environment, ensure your agent has access to the python runtime and the metric definitions.
# Example of how to structure the skill utility within your agent workspace
mkdir -p skills/simulation-metrics
touch skills/simulation-metrics/metrics.py
Add the logic from the SKILL.md into your local agent configuration to begin processing simulation results.
Simulation Metrics Data Schema & Taxonomy
The skill expects structured time-series data, typically formatted as lists or dictionary rows. Below is the expected schema for processing:
| Input Parameter | Type | Description |
|---|---|---|
times |
List[float] | Sequential time stamps from the simulation. |
values |
List[float] | The measured output values of the system. |
target |
float | The desired setpoint or reference value. |
tolerance |
float | The percentage band (default 0.02) for settling time. |
final_fraction |
float | The portion of data (default 0.1) used for steady-state error. |
Simulation Metrics Advanced Features
- Custom Tolerance Bands: Adjust the settling time sensitivity for high-precision or noisy environments.
- Dynamic Final Averaging: Modify the
final_fractionto calculate steady-state error over longer or shorter windows. - Percentage-Based Scaling: Built-in overshoot calculations automatically handle normalization against the target value.
- Integration Ready: Designed to work seamlessly with other Openclaw Skills for automated data visualization or alerting.
SKILL.md
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