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simulation-metrics

作者 wu-uk · GitHub ↗ · v0.1.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install adaptive-cruise-control-simulation-metrics
功能描述
Use this skill when calculating control system performance metrics such as rise time, overshoot percentage, steady-state error, or settling time for evaluati...
使用说明 (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)}")
安全使用建议
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.
功能分析
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.
能力评估
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.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install adaptive-cruise-control-simulation-metrics
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /adaptive-cruise-control-simulation-metrics 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v0.1.0
Bulk register from skillsbench tasks.
元数据
Slug adaptive-cruise-control-simulation-metrics
版本 0.1.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

simulation-metrics 是什么?

Use this skill when calculating control system performance metrics such as rise time, overshoot percentage, steady-state error, or settling time for evaluati... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 101 次。

如何安装 simulation-metrics?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install adaptive-cruise-control-simulation-metrics」即可一键安装,无需额外配置。

simulation-metrics 是免费的吗?

是的,simulation-metrics 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

simulation-metrics 支持哪些平台?

simulation-metrics 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 simulation-metrics?

由 wu-uk(@wu-uk)开发并维护,当前版本 v0.1.0。

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