evo-r2r-linearization

evo-r2r-linearization is a skill for Claude Code, Codex from OpenLAIR/OpenSkill. It costs 45 tokens per session (324 once invoked), scanned A, original, Apache-2.0.

A control-systems calculator for a six-section roll-to-roll machine, which moves a continuous web of material between rollers. It finds steady operating conditions, converts the model for computer use, and calculates an LQR feedback gain.

In plain words
What is it for?
It is for modelling web tension and roller speed, preparing a discrete-time system, and designing LQR control for a roll-to-roll process.
Why use it?
It removes the need to derive these state-space matrices and controller values by hand. This gives simulations and controllers the model data they need.

Skill for Claude CodeCodex

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

Good fit It is for modelling web tension and roller speed, preparing a discrete-time system, and designing LQR control for a roll-to-roll process.

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Install with agentmods
npx agentmods add skills/openlair/openskill/evo-r2r-linearization
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 OpenLAIR/OpenSkill --skill evo-r2r-linearization
Clone the repo
git clone --depth 1 https://github.com/OpenLAIR/OpenSkill

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 evo-r2r-linearization

README.md
[![agentmods](https://agentmods.dev/badge/skills/openlair/openskill/evo-r2r-linearization/github.svg)](https://agentmods.dev/skills/openlair/openskill/evo-r2r-linearization)
Your own site
<a href="https://agentmods.dev/skills/openlair/openskill/evo-r2r-linearization"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-r2r-linearization/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 evo-r2r-linearization

Your own site · 80×15
<a href="https://agentmods.dev/skills/openlair/openskill/evo-r2r-linearization"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-r2r-linearization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 45 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 324 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 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 $0.00045 $0.00324
Opus 5 $0.00023 $0.00162
Sonnet 5 $0.00009 $0.00065
Haiku 4.5 $0.00005 $0.00032

Measured yesterday against content hash 0e4d483ed986, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

evo-r2r-linearization 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 yesterday.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/utils.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

tasks-evolved/r2r-mpc-control/environment/skills/evo-r2r-linearization/SKILL.md · 31 lines

What it actually says

evo-r2r-linearization

Computes linearized state-space model for R2R web handling systems.

Usage

import sys
sys.path.insert(0, '/app/environment/skills/evo-r2r-linearization/scripts')
from utils import (
    compute_steady_state_velocities,
    compute_steady_state_torques,
    build_continuous_AB,
    discretize_system,
    compute_lqr_gain,
    get_full_reference_state
)

Key Functions

  • compute_steady_state_velocities(T_ref, EA, v0) - Cascade velocities using (EA-T) formula
  • compute_steady_state_torques(T_ref, v_ref, R, fb) - Compute equilibrium torques
  • build_continuous_AB(T_ss, v_ss, EA, L, R, J, fb, v0) - Build 12x12 A and 12x6 B Jacobians
  • discretize_system(A_cont, B_cont, dt) - ZOH discretization via scipy
  • compute_lqr_gain(Ad, Bd, Q, R_mat) - Solve DARE, return K_lqr and P
  • get_full_reference_state(T_ref, EA, v0, R, fb) - Get full x_ref and u_ref
Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. yesterday First seen · 31 lines · 45 tokens per session scan A 0e4d483ed986

Subscribe to this mod's changes

evo-r2r-linearization is a skill published in the GitHub repository OpenLAIR/OpenSkill (90 stars, last pushed 2d ago), licensed Apache-2.0. It adds 45 tokens to every session and 324 once invoked, about $0.0002 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 2026-09-11.

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