imc-tuning-rules

imc-tuning-rules is a skill for Claude Code, Codex from benchflow-ai/skillsbench. It costs 28 tokens per session (943 once invoked), scanned A, original, Apache-2.0.

A method for choosing PI or PID controller settings from a first-order process model. IMC uses the model's gain and response time plus one chosen speed setting to calculate the controller gains.

In plain words
What is it for?
Use it to calculate proportional and integral gains for a PI controller and understand how the speed setting changes response and robustness.
Why use it?
It replaces trial-and-error tuning with a repeatable calculation for systems that behave approximately like first-order processes.

Skill for Claude CodeCodex

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

Good fit Use it to calculate proportional and integral gains for a PI controller and understand how the speed setting changes response and robustness.

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Install with agentmods
npx agentmods add skills/benchflow-ai/skillsbench/imc-tuning-rules
About the project

SkillsBench is a benchmark for measuring how effectively AI agents use modular skills—folders containing instructions, scripts, and resources—to complete specialized tasks. It helps researchers and developers evaluate both skill quality and agent behavior, including tasks that require combining multiple skills. The catalogue’s skills and instructions are evaluated as part of this workflow.

benchflow-ai/skillsbench · 1,760 stars · on GitHub · skillsbench.ai

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 benchflow-ai/skillsbench --skill imc-tuning-rules
Clone the repo
git clone --depth 1 https://github.com/benchflow-ai/skillsbench

Made for: Claude Code, Codex.

Wrote this? Show the measurements

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agentmods badge for imc-tuning-rules

README.md
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Your own site · 80×15
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Per session 28 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 943 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found 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 $0.00028 $0.00943
Opus 5 $0.00014 $0.00472
Sonnet 5 $0.00006 $0.00189
Haiku 4.5 $0.00003 $0.00094

Measured 7d ago against content hash 2388dcb94f07, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

imc-tuning-rules 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 7d 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

Copies of this mod

1 near-identical copy found in the catalogue:

tasks/hvac-control/environment/skills/imc-tuning-rules/SKILL.md · 132 lines

How it starts

The opening of the file, as written. The whole thing — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

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
    }

Read the full file on GitHub · 132 lines

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. 7d ago First seen · 132 lines · 28 tokens per session scan A 2388dcb94f07

Subscribe to this mod's changes

imc-tuning-rules is a skill published in the GitHub repository benchflow-ai/skillsbench (1,760 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 28 tokens to every session and 943 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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