simulation-metrics

simulation-metrics is a skill for Claude Code, Codex from xuansenpa1/skillrevise. It costs 34 tokens per session (546 once invoked), scanned A, a copy of simulation-metrics, MIT.

A set of calculations for measuring how a control system responds to a target over time.

In plain words
What is it for?
Use it to calculate rise time, overshoot percentage, steady-state error, and settling time from simulation data.
Why use it?
It provides consistent definitions for judging whether a simulated response reaches its target quickly, accurately, and without excessive overshoot.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to calculate rise time, overshoot percentage, steady-state error, and settling time from simulation data.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/xuansenpa1/skillrevise/simulation-metrics
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add xuansenpa1/skillrevise --skill simulation-metrics
Clone the repo
git clone --depth 1 https://github.com/xuansenpa1/skillrevise

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for simulation-metrics

README.md
[![agentmods](https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/simulation-metrics/github.svg)](https://agentmods.dev/skills/xuansenpa1/skillrevise/simulation-metrics)
Your own site
<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/simulation-metrics"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/simulation-metrics/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for simulation-metrics

Your own site · 80×15
<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/simulation-metrics"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/simulation-metrics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 34 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 546 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00034 $0.00546
Opus 5 $0.00017 $0.00273
Sonnet 5 $0.00007 $0.00109
Haiku 4.5 $0.00003 $0.00055

Measured 9d ago against content hash 9e145fd19a2c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

simulation-metrics scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to simulation-metrics — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

data/skillsbench/tasks/adaptive-cruise-control/environment/skills/simulation-metrics/SKILL.md · 86 lines

What it actually says

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)}")
Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 86 lines · 34 tokens per session scan A 9e145fd19a2c

Subscribe to this mod's changes

simulation-metrics is a skill published in the GitHub repository xuansenpa1/skillrevise (55 stars, last pushed 3d ago), licensed MIT. It adds 34 tokens to every session and 546 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to simulation-metrics, differing in 0 lines, and is treated as a copy.

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