Getting it into your agent
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npx skills add xuansenpa1/skillrevise --skill imc-tuning-rulesgit clone --depth 1 https://github.com/xuansenpa1/skillreviseWrote this? Show the measurements
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[](https://agentmods.dev/skills/xuansenpa1/skillrevise/imc-tuning-rules)<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/imc-tuning-rules"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/imc-tuning-rules/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.
<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/imc-tuning-rules"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/imc-tuning-rules.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once 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 |
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 8d 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.
This is a copy
100% identical to imc-tuning-rules — 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.
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 gainKi= 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
}
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.
- 8d ago First seen · 132 lines · 28 tokens per session scan A 2388dcb94f07
imc-tuning-rules is a skill published in the GitHub repository xuansenpa1/skillrevise (56 stars, last pushed 6d ago), licensed MIT. 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. It is 100% identical to imc-tuning-rules, differing in 0 lines, and is treated as a copy.
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