simulation-metrics
Computes standard control system metrics from simulation data.
Install
mkdir -p .claude/skills/simulation-metrics && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/2579" && unzip -o skill.zip -d .claude/skills/simulation-metrics && rm skill.zipInstalls to .claude/skills/simulation-metrics
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Use this skill when calculating control system performance metrics such as rise time, overshoot percentage, steady-state error, or settling time for evaluating simulation results.Key capabilities
- →Calculate rise time from 10% to 90% of target
- →Compute overshoot percentage relative to target
- →Determine steady-state error using final data fraction
- →Calculate settling time within a specified tolerance band
How it works
The skill iterates through time-series data arrays to identify threshold crossings or final state averages based on specific control system formulas. It uses standard Python loops and math operations to derive performance indicators from simulation logs.
Inputs & outputs
When to use simulation-metrics
- →Calculating settling time for a system response
- →Determining overshoot percentage from simulation logs
About this skill
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 <= 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 < 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)}")
When not to use it
- →Processing non-time-series data
- →Analyzing systems without a defined target value
Limitations
- →Requires data to be provided as lists of times and values
- →Steady-state error calculation assumes the final 10% of data represents the settled state
How it compares
It provides pre-defined, reusable functions for specific control metrics instead of manually writing custom logic for each simulation run.
Compared to similar skills
simulation-metrics side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| simulation-metrics (this skill) | 3 | 6mo | No flags | Beginner |
| quant-analyst | 103 | 2mo | No flags | Advanced |
| umap-learn | 6 | 2mo | Review | Intermediate |
| embedding-strategies | 8 | 2mo | No flags | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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