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simulation-metrics

by wu-uk · GitHub ↗ · v0.1.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install adaptive-cruise-control-simulation-metrics
Description
Use this skill when calculating control system performance metrics such as rise time, overshoot percentage, steady-state error, or settling time for evaluati...
README (SKILL.md)

Control System Performance Metrics

Rise Time

Time for system to go from 10% to 90% of target value.

def rise_time(times, values, target):
    """Calculate rise time (10% to 90% of target)."""
    t10 = t90 = None

    for t, v in zip(times, values):
        if t10 is None and v >= 0.1 * target:
            t10 = t
        if t90 is None and v >= 0.9 * target:
            t90 = t
            break

    if t10 is not None and t90 is not None:
        return t90 - t10
    return None

Overshoot

How much response exceeds target, as percentage.

def overshoot_percent(values, target):
    """Calculate overshoot percentage."""
    max_val = max(values)
    if max_val \x3C= target:
        return 0.0
    return ((max_val - target) / target) * 100

Steady-State Error

Difference between target and final settled value.

def steady_state_error(values, target, final_fraction=0.1):
    """Calculate steady-state error using final portion of data."""
    n = len(values)
    start = int(n * (1 - final_fraction))
    final_avg = sum(values[start:]) / len(values[start:])
    return abs(target - final_avg)

Settling Time

Time to stay within tolerance band of target.

def settling_time(times, values, target, tolerance=0.02):
    """Time to settle within tolerance of target."""
    band = target * tolerance
    lower, upper = target - band, target + band

    settled_at = None
    for t, v in zip(times, values):
        if v \x3C lower or v > upper:
            settled_at = None
        elif settled_at is None:
            settled_at = t

    return settled_at

Usage

times = [row['time'] for row in results]
values = [row['value'] for row in results]
target = 30.0

print(f"Rise time: {rise_time(times, values, target)}")
print(f"Overshoot: {overshoot_percent(values, target)}%")
print(f"SS Error: {steady_state_error(values, target)}")
Usage Guidance
This skill is low-risk and self-contained, but test it before using in safety-critical workflows: check behavior on edge cases (empty arrays, very small datasets), handle target == 0 (overshoot function divides by target), and consider measurement noise and sampling rate when computing settling time and steady-state error. Because it’s instruction-only, it won’t install software or access credentials, but always validate outputs and add input validation (e.g., check array lengths, target nonzero) before relying on results.
Capability Analysis
Type: OpenClaw Skill Name: adaptive-cruise-control-simulation-metrics Version: 0.1.0 The skill bundle contains standard mathematical functions for calculating control system performance metrics such as rise time, overshoot, and steady-state error. The code in SKILL.md is purely computational, lacks any external dependencies, network access, or file system interactions, and contains no indicators of malicious intent or prompt injection.
Capability Assessment
Purpose & Capability
Skill name and description match the content: SKILL.md contains straightforward Python implementations of rise time, overshoot, steady-state error, and settling time — no unrelated capabilities, binaries, or credentials are requested.
Instruction Scope
Instructions contain only pure-Python helper functions and a small usage example operating on provided time/value lists and a target; they do not read files, access environment variables, or transmit data externally.
Install Mechanism
No install spec and no code files beyond SKILL.md (instruction-only). Nothing is downloaded or written to disk by the skill itself.
Credentials
The skill requires no environment variables, credentials, or config paths — this is proportional for a local metric-calculation helper.
Persistence & Privilege
always is false and the skill does not request persistent presence or modify system settings; autonomous invocation is allowed by platform default but presents no extra risk given the skill's narrow scope.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install adaptive-cruise-control-simulation-metrics
  3. After installation, invoke the skill by name or use /adaptive-cruise-control-simulation-metrics
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.0
Bulk register from skillsbench tasks.
Metadata
Slug adaptive-cruise-control-simulation-metrics
Version 0.1.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is simulation-metrics?

Use this skill when calculating control system performance metrics such as rise time, overshoot percentage, steady-state error, or settling time for evaluati... It is an AI Agent Skill for Claude Code / OpenClaw, with 101 downloads so far.

How do I install simulation-metrics?

Run "/install adaptive-cruise-control-simulation-metrics" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is simulation-metrics free?

Yes, simulation-metrics is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does simulation-metrics support?

simulation-metrics is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created simulation-metrics?

It is built and maintained by wu-uk (@wu-uk); the current version is v0.1.0.

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