Agent skill

Simulation Metrics

by benchflow-ai in benchflow-ai/skillsbench

A skill your agent uses when calculating control system performance metrics such as rise time, overshoot percentage, steady-state error, or settling time for evaluating simulation results.

Apache-2.0Auto-check passedBusiness, Finance & HR

Install Simulation Metrics

skills CLI

$ npx skills add benchflow-ai/skillsbench --skill simulation-metrics -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install benchflow-ai/skillsbench simulation-metrics --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .claude/skills && cp -r skills-src/tasks/adaptive-cruise-control/environment/skills/simulation-metrics .claude/skills/simulation-metrics && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "simulation-metrics" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/adaptive-cruise-control/environment/skills/simulation-metrics into .claude/skills/simulation-metrics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "simulation-metrics", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/benchflow-ai/skillsbench/tree/main/tasks/adaptive-cruise-control/environment/skills/simulation-metrics

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add benchflow-ai/skillsbench --skill simulation-metrics -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install benchflow-ai/skillsbench simulation-metrics --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .agents/skills && cp -r skills-src/tasks/adaptive-cruise-control/environment/skills/simulation-metrics .agents/skills/simulation-metrics && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "simulation-metrics" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/adaptive-cruise-control/environment/skills/simulation-metrics into .agents/skills/simulation-metrics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "simulation-metrics", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add benchflow-ai/skillsbench --skill simulation-metrics -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install benchflow-ai/skillsbench simulation-metrics --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/tasks/adaptive-cruise-control/environment/skills/simulation-metrics .cursor/skills/simulation-metrics && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "simulation-metrics" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/adaptive-cruise-control/environment/skills/simulation-metrics into .cursor/skills/simulation-metrics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "simulation-metrics", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/benchflow-ai/skillsbench.git --path tasks/adaptive-cruise-control/environment/skills/simulation-metrics

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add benchflow-ai/skillsbench --skill simulation-metrics -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install benchflow-ai/skillsbench simulation-metrics --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/tasks/adaptive-cruise-control/environment/skills/simulation-metrics .gemini/skills/simulation-metrics && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "simulation-metrics" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/adaptive-cruise-control/environment/skills/simulation-metrics into .gemini/skills/simulation-metrics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "simulation-metrics", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install benchflow-ai/skillsbench simulation-metrics

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add benchflow-ai/skillsbench --skill simulation-metrics -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .github/skills && cp -r skills-src/tasks/adaptive-cruise-control/environment/skills/simulation-metrics .github/skills/simulation-metrics && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "simulation-metrics" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/adaptive-cruise-control/environment/skills/simulation-metrics into .github/skills/simulation-metrics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "simulation-metrics", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add benchflow-ai/skillsbench --skill simulation-metrics -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install benchflow-ai/skillsbench simulation-metrics --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/tasks/adaptive-cruise-control/environment/skills/simulation-metrics .opencode/skills/simulation-metrics && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "simulation-metrics" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/adaptive-cruise-control/environment/skills/simulation-metrics into .opencode/skills/simulation-metrics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "simulation-metrics", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: simulation-metrics
GitHub stars: 1.8k
Token cost: ~540 tokens
SKILL.md length: 46 words
Files: 1
Skills in repo: 178
Repo updated: Jul 23, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

A skill your agent uses when calculating control system performance metrics such as rise time, overshoot percentage, steady-state error, or settling time for evaluating simulation results.

Calculating control system performance metrics such as rise time
SKILL.md covers Rise Time, Overshoot, Steady-State Error and Settling Time, plus 1 more section
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
Overshoot percentage

What it does

Simulation Metrics is an agent skill from benchflow-ai/skillsbench. 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.

Its SKILL.md is about 540 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Business, Finance & HR, covering OKRs and executive reporting. The repository describes itself as: SkillsBench evaluates how well skills work and how effective agents are at using them. The licence is Apache-2.0.

When your agent uses it

Calculating control system performance metrics such as rise time
Overshoot percentage
Steady-state error
Settling time for evaluating simulation results

Example prompts

“/simulation-metrics”

Requirements

Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 9a1f4dd. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are python).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Simulation Metrics loads about 540 tokens when it runs. Until then it costs about 50 tokens; SKILL.md has 46 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~50
When it runs · the whole SKILL.md, loaded when a task matches: ~540

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from benchflow-ai/skillsbench at commit 9a1f4dd, republished under its Apache-2.0 licence (© benchflow-ai). 46 words, ~540 tokens.

Download SKILL.mdSave it as .claude/skills/simulation-metrics/SKILL.md (or your agent's skills folder).

name: 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 evaluating simulation results.

Control System Performance Metrics

Rise Time

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

python

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.

python

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.

python

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.

python

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

python

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)}")

Files

Just SKILL.md in tasks/adaptive-cruise-control/environment/skills/simulation-metrics of benchflow-ai/skillsbench.

Open the folder on GitHubat commit 9a1f4dd

Compare with similar skills

Simulation Metrics next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Simulation Metrics compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Simulation Metrics this skillbenchflow-ai/skillsbench	1.8k	—	~540	Automated safety check: Pass	Apache-2.0	2 mo ago
Analytics Strategyrampstackco/claude-skills	940	1 repos	~2.4k	Automated safety check: Pass	MIT	yesterday
Pine BacktesterTradersPost/pinescript-agents	170	1 repos	~3.9k	Automated safety check: Pass	None	2 mo ago
Onboarding Plannerbpinheiroms/dotfiles	108	—	~5.4k	Automated safety check: Pass	None	4 mo ago
Replit Decksanqiufong/slides-from-anything	132	1 repos	~2.9k	Automated safety check: Pass	Apache-2.0	yesterday
Building Streamlit Dashboardsiusztinpaul/designing-real-world-ai-agents-workshop	513	—	~1.1k	Automated safety check: Pass	Apache-2.0	4 mo ago

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Questions about Simulation Metrics

What does Simulation Metrics do?

A skill your agent uses when calculating control system performance metrics such as rise time, overshoot percentage, steady-state error, or settling time for evaluating simulation results. Simulation Metrics is an agent skill from benchflow-ai/skillsbench. 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.

When should I use Simulation Metrics?

Simulation Metrics fits situations like: calculating control system performance metrics such as rise time; overshoot percentage; steady-state error; settling time for evaluating simulation results.

How do I install Simulation Metrics in Claude Code?

Run `npx skills add benchflow-ai/skillsbench --skill simulation-metrics -a claude-code`. Or copy the skill folder (tasks/adaptive-cruise-control/environment/skills/simulation-metrics in benchflow-ai/skillsbench) into .claude/skills/simulation-metrics in your project. Claude Code loads it when a task matches its description.

How do I install Simulation Metrics in Codex?

Run `npx skills add benchflow-ai/skillsbench --skill simulation-metrics -a codex`. Or copy the skill folder (tasks/adaptive-cruise-control/environment/skills/simulation-metrics in benchflow-ai/skillsbench) into .agents/skills/simulation-metrics in your project. Codex loads it when a task matches its description.

Can I use Simulation Metrics in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add benchflow-ai/skillsbench --skill simulation-metrics -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/simulation-metrics, .gemini/skills/simulation-metrics, .github/skills/simulation-metrics and .opencode/skills/simulation-metrics in your project.

What does Simulation Metrics need to run?

SKILL.md names no scripts, command-line tools or credentials: Simulation Metrics is instructions for the agent only. Our summary lists: Python 3.

Does Simulation Metrics access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Simulation Metrics safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Simulation Metrics use?

Simulation Metrics is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Simulation Metrics use?

About 540 tokens (SKILL.md is roughly 2.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Simulation Metrics?

Skills that share tags, products or a category with Simulation Metrics: Analytics Strategy (rampstackco/claude-skills, 940 stars), Pine Backtester (TradersPost/pinescript-agents, 170 stars), Onboarding Planner (bpinheiroms/dotfiles, 108 stars) and Replit Deck (sanqiufong/slides-from-anything, 132 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Simulation Metrics?

benchflow-ai (a GitHub organization) maintains it in benchflow-ai/skillsbench, which has 1,832 GitHub stars. The repository holds 178 skills in this directory. The repository was last updated on July 23, 2026.

Source: benchflow-ai/skillsbench on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Simulation Metrics

Install Simulation Metrics

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Control System Performance Metrics

Rise Time

Overshoot

Steady-State Error

Settling Time

Usage

Files

Compare with similar skills

Similar skills

Analytics Strategy

Pine Backtester

Onboarding Planner

Replit Deck

Building Streamlit Dashboards

Massing Bim

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Questions about Simulation Metrics

Install Simulation Metrics

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Control System Performance Metrics

Rise Time

Overshoot

Steady-State Error

Settling Time

Usage

Files

Compare with similar skills

Similar skills

More from benchflow-ai/skillsbench

Related

Questions about Simulation Metrics