Agent skill

Imc Tuning Rules

by benchflow-ai in benchflow-ai/skillsbench

Calculate PI/PID controller gains using Internal Model Control (IMC) tuning rules for first-order systems.

Apache-2.0Auto-check passed

Install Imc Tuning Rules

skills CLI

$ npx skills add benchflow-ai/skillsbench --skill imc-tuning-rules -a claude-code

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

GitHub CLI

$ gh skill install benchflow-ai/skillsbench imc-tuning-rules --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/hvac-control/environment/skills/imc-tuning-rules .claude/skills/imc-tuning-rules && 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 "imc-tuning-rules" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/hvac-control/environment/skills/imc-tuning-rules into .claude/skills/imc-tuning-rules/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "imc-tuning-rules", 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/hvac-control/environment/skills/imc-tuning-rules

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 imc-tuning-rules -a codex

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

GitHub CLI

$ gh skill install benchflow-ai/skillsbench imc-tuning-rules --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/hvac-control/environment/skills/imc-tuning-rules .agents/skills/imc-tuning-rules && 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 "imc-tuning-rules" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/hvac-control/environment/skills/imc-tuning-rules into .agents/skills/imc-tuning-rules/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "imc-tuning-rules", 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 imc-tuning-rules -a cursor

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

GitHub CLI

$ gh skill install benchflow-ai/skillsbench imc-tuning-rules --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/hvac-control/environment/skills/imc-tuning-rules .cursor/skills/imc-tuning-rules && 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 "imc-tuning-rules" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/hvac-control/environment/skills/imc-tuning-rules into .cursor/skills/imc-tuning-rules/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "imc-tuning-rules", 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/hvac-control/environment/skills/imc-tuning-rules

--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 imc-tuning-rules -a gemini-cli

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

GitHub CLI

$ gh skill install benchflow-ai/skillsbench imc-tuning-rules --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/hvac-control/environment/skills/imc-tuning-rules .gemini/skills/imc-tuning-rules && 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 "imc-tuning-rules" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/hvac-control/environment/skills/imc-tuning-rules into .gemini/skills/imc-tuning-rules/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "imc-tuning-rules", 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 imc-tuning-rules

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 imc-tuning-rules -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/hvac-control/environment/skills/imc-tuning-rules .github/skills/imc-tuning-rules && 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 "imc-tuning-rules" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/hvac-control/environment/skills/imc-tuning-rules into .github/skills/imc-tuning-rules/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "imc-tuning-rules", 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 imc-tuning-rules -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 imc-tuning-rules --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/hvac-control/environment/skills/imc-tuning-rules .opencode/skills/imc-tuning-rules && 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 "imc-tuning-rules" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/hvac-control/environment/skills/imc-tuning-rules into .opencode/skills/imc-tuning-rules/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "imc-tuning-rules", 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: imc-tuning-rules
GitHub stars: 1.8k
Token cost: ~903 tokens
SKILL.md length: 271 words
Files: 1
Skills in repo: 189
Repo updated: Jul 23, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Calculate PI/PID controller gains using Internal Model Control (IMC) tuning rules for first-order systems.

Works in 4 steps: Start conservative: Use lambda = tau… → Decrease lambda carefully: Smaller… → Watch for oscillation: If output… → …
SKILL.md covers Overview, Why IMC?, IMC Tuning for First-Order… and Choosing Lambda (λ), plus 4 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Imc Tuning Rules is an agent skill from benchflow-ai/skillsbench. Calculate PI/PID controller gains using Internal Model Control (IMC) tuning rules for first-order systems.

Its SKILL.md is about 900 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

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.

Example prompts

“/imc-tuning-rules”

Requirements

Python 3

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

Start conservative: Use lambda = tau initially
Decrease lambda carefully: Smaller lambda = larger Kp = faster but riskier
Watch for oscillation: If output oscillates, increase lambda
Anti-windup: Prevent integral wind-up when output saturates

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

Imc Tuning Rules loads about 903 tokens when it runs. Until then it costs about 31 tokens; SKILL.md has 271 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~31
When it runs · the whole SKILL.md, loaded when a task matches: ~903

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). 271 words, ~903 tokens.

Download SKILL.mdSave it as .claude/skills/imc-tuning-rules/SKILL.md (or your agent's skills folder).

name: imc-tuning-rules
description: Calculate PI/PID controller gains using Internal Model Control (IMC) tuning rules for first-order systems.

IMC Tuning Rules for PI/PID Controllers

Overview

Internal Model Control (IMC) is a systematic method for tuning PI/PID controllers based on a process model. Once you've identified system parameters (K and tau), IMC provides controller gains.

Why IMC?

Model-based: Uses identified process parameters directly
Single tuning parameter: Just choose the closed-loop speed (lambda)
Guaranteed stability: For first-order systems, always stable if model is accurate
Predictable response: Closed-loop time constant equals lambda

IMC Tuning for First-Order Systems

For a first-order process with gain K and time constant tau:

Process: G(s) = K / (tau*s + 1)

The IMC-tuned PI controller gains are:

Kp = tau / (K * lambda)
Ki = Kp / tau = 1 / (K * lambda)
Kd = 0  (derivative not needed for first-order systems)

Where:

Kp = Proportional gain
Ki = Integral gain (units: 1/time)
Kd = Derivative gain (zero for first-order)
lambda = Desired closed-loop time constant (tuning parameter)

Choosing Lambda (λ)

Lambda controls the trade-off between speed and robustness:

Lambda	Behavior
`lambda = 0.1 * tau`	Very aggressive, fast but sensitive to model error
`lambda = 0.5 * tau`	Aggressive, good for accurate models
`lambda = 1.0 * tau`	Moderate, balanced speed and robustness
`lambda = 2.0 * tau`	Conservative, robust to model uncertainty

Default recommendation: Start with lambda = tau

For noisy systems or uncertain models, use larger lambda. For precise models and fast response needs, use smaller lambda.

Implementation

python

def calculate_imc_gains(K, tau, lambda_factor=1.0):
    """
    Calculate IMC-tuned PI gains for a first-order system.

    Args:
        K: Process gain
        tau: Time constant
        lambda_factor: Multiplier for lambda (default 1.0 = lambda equals tau)

    Returns:
        dict with Kp, Ki, Kd, lambda
    """
    lambda_cl = lambda_factor * tau

    Kp = tau / (K * lambda_cl)
    Ki = Kp / tau
    Kd = 0.0

    return {
        "Kp": Kp,
        "Ki": Ki,
        "Kd": Kd,
        "lambda": lambda_cl
    }

PI Controller Implementation

python

class PIController:
    def __init__(self, Kp, Ki, setpoint):
        self.Kp = Kp
        self.Ki = Ki
        self.setpoint = setpoint
        self.integral = 0.0

    def compute(self, measurement, dt):
        """Compute control output."""
        error = self.setpoint - measurement

        # Integral term
        self.integral += error * dt

        # PI control law
        output = self.Kp * error + self.Ki * self.integral

        # Clamp to valid range
        output = max(output_min, min(output_max, output))

        return output

Expected Closed-Loop Behavior

With IMC tuning, the closed-loop response is approximately:

y(t) = y_setpoint * (1 - exp(-t / lambda))

Key properties:

Rise time: ~2.2 * lambda to reach 90% of setpoint
Settling time: ~4 * lambda to reach 98% of setpoint
Overshoot: Minimal for first-order systems
Steady-state error: Zero (integral action eliminates offset)

Tips

Start conservative: Use lambda = tau initially
Decrease lambda carefully: Smaller lambda = larger Kp = faster but riskier
Watch for oscillation: If output oscillates, increase lambda
Anti-windup: Prevent integral wind-up when output saturates

Files

Just SKILL.md in tasks/hvac-control/environment/skills/imc-tuning-rules of benchflow-ai/skillsbench.

Open the folder on GitHubat commit 9a1f4dd

Compare with similar skills

Imc Tuning Rules 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.

Imc Tuning Rules compared with similar skills
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Internal Control Frameworkrevfactory/harness-100	1.3k	—	~889	Automated safety check: Pass	Apache-2.0	6 mo ago
Internal Commsalirezarezvani/claude-skills	28k	—	~3.4k	Automated safety check: Pass	MIT	1 mo ago
Control UIopenclaw/openclaw	392k	—	~1.8k	Automated safety check: Pass	MIT	today
Internal Communicationsickn33/agentic-awesome-skills	47k	1 repos	~3.4k	Automated safety check: Pass	MIT	today
Internal Controls And Auditcbrock84/headcount	2k	—	~1.3k	Automated safety check: Pass	MIT	21 days ago

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Questions about Imc Tuning Rules

What does Imc Tuning Rules do?

Calculate PI/PID controller gains using Internal Model Control (IMC) tuning rules for first-order systems. Imc Tuning Rules is an agent skill from benchflow-ai/skillsbench. Calculate PI/PID controller gains using Internal Model Control (IMC) tuning rules for first-order systems.

How do I install Imc Tuning Rules in Claude Code?

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

How do I install Imc Tuning Rules in Codex?

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

Can I use Imc Tuning Rules 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 imc-tuning-rules -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/imc-tuning-rules, .gemini/skills/imc-tuning-rules, .github/skills/imc-tuning-rules and .opencode/skills/imc-tuning-rules in your project.

What does Imc Tuning Rules need to run?

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

Does Imc Tuning Rules 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 Imc Tuning Rules 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 Imc Tuning Rules use?

Imc Tuning Rules 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 Imc Tuning Rules use?

About 903 tokens (SKILL.md is roughly 3.6k 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 Imc Tuning Rules?

Skills that share tags, products or a category with Imc Tuning Rules: Internal Control Framework (revfactory/harness-100, 1.3k stars), Internal Comms (alirezarezvani/claude-skills, 28k stars), Control UI (openclaw/openclaw, 392k stars) and Internal Communication (sickn33/agentic-awesome-skills, 47k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Imc Tuning Rules?

benchflow-ai (a GitHub organization) maintains it in benchflow-ai/skillsbench, which has 1,834 GitHub stars. The repository holds 189 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.

Install Imc Tuning Rules

Facts

At a glance

What it does

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

IMC Tuning Rules for PI/PID Controllers

Overview

Why IMC?

IMC Tuning for First-Order Systems

Choosing Lambda (λ)

Implementation

PI Controller Implementation

Expected Closed-Loop Behavior

Tips

Files

Compare with similar skills

Similar skills

More from benchflow-ai/skillsbench

Questions about Imc Tuning Rules