neqsim-regression-baselines

neqsim-regression-baselines is a skill for Claude Code, Codex from equinor/neqsim. It costs 51 tokens per session (1,792 once invoked), scanned A, original, Apache-2.0.

A method for preserving expected NeqSim calculation results as test fixtures before changing solver, property, database, or equipment code.

In plain words
What is it for?
Use it to capture reference values, write regression tests, and check changes to flash calculations, phase properties, component data, mixing rules, equipment calculations, or design correlations.
Why use it?
It catches accuracy drift that automated build checks might otherwise miss, such as one property improving while another becomes substantially less accurate.

Skill for Claude CodeCodex

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

Good fit Use it to capture reference values, write regression tests, and check changes to flash calculations, phase properties, component data, mixing rules, equipment calculations, or design correlations.

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Install with agentmods
npx agentmods add skills/equinor/neqsim/neqsim-regression-baselines
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 equinor/neqsim --skill neqsim-regression-baselines
Clone the repo
git clone --depth 1 https://github.com/equinor/neqsim

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 neqsim-regression-baselines

README.md
[![agentmods](https://agentmods.dev/badge/skills/equinor/neqsim/neqsim-regression-baselines.svg)](https://agentmods.dev/skills/equinor/neqsim/neqsim-regression-baselines)
Your own site
<a href="https://agentmods.dev/skills/equinor/neqsim/neqsim-regression-baselines"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/neqsim-regression-baselines.svg" alt="Measured on agentmods" height="20"></a>
Per session 51 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,792 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 $0.00051 $0.01792
Opus 5 $0.00026 $0.00896
Sonnet 5 $0.00010 $0.00358
Haiku 4.5 $0.00005 $0.00179

Measured 4d ago against content hash a8ae624389af, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

neqsim-regression-baselines 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 4d 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.

.github/skills/neqsim-regression-baselines/SKILL.md · 195 lines

How it starts

The opening of the file, as written. The whole thing — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.

NeqSim Regression Baseline Management

Preventing silent accuracy drift in a physics engine requires committed baseline values that CI validates on every build.

Why Baselines Matter

A property correlation change that improves methane density by 0.1% might degrade ethane viscosity by 5%. Without baselines, these regressions are invisible until a downstream user reports wrong results months later.

Baseline Workflow

When to Create Baselines

Create baselines before modifying:

  • Flash calculation algorithms (flashops/)
  • Phase property calculations (phase/, physicalproperties/)
  • Component parameter databases (COMP.csv, mbwr32param.csv)
  • Mixing rule implementations
  • Process equipment calculations that depend on thermodynamic properties
  • Cost estimation or mechanical design correlations

Step 1: Capture Current Values

Run the existing code and record results in a JSON fixture file:

@Test
void captureBaseline_SRK_methane_density() {
    SystemInterface fluid = new SystemSrkEos(273.15 + 25.0, 60.0);
    fluid.addComponent("methane", 1.0);
    fluid.setMixingRule("classic");

    ThermodynamicOperations ops = new ThermodynamicOperations(fluid);
    ops.TPflash();
    fluid.initProperties();

    // Record these values as the baseline
    double density = fluid.getDensity("kg/m3");     // e.g., 45.23
    double Cp = fluid.getCp("J/molK");              // e.g., 38.5
    double Z = fluid.getZ();                        // e.g., 0.892

    assertEquals(45.23, density, 0.5, "Methane density at 25C/60bar");
    assertEquals(38.5, Cp, 0.5, "Methane Cp at 25C/60bar");
    assertEquals(0.892, Z, 0.005, "Methane Z-factor at 25C/60bar");
}

Step 2: Choose Appropriate Tolerances

Property Type Typical Tolerance Rationale
Density 0.5-1.0% relative Well-predicted by cubic EOS
Z-factor 0.5% absolute (0.005) Directly from EOS
Viscosity 2-5% relative Correlation-dependent, more variable
Thermal conductivity 5-10% relative Least accurate transport property
Phase fractions 1% absolute (0.01) Phase split sensitivity varies
Enthalpy/Cp 1-2% relative Derived from EOS
Bubble/dew point 0.5-1.0 K or 0.5-1.0 bar Phase boundary sensitivity
Compressor power 1-2% relative Depends on enthalpy accuracy
Separator compositions 2% relative per component Depends on K-value accuracy

Read the full file on GitHub · 195 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. 4d ago First seen · 195 lines · 51 tokens per session scan A a8ae624389af

Subscribe to this mod's changes

neqsim-regression-baselines is a skill published in the GitHub repository equinor/neqsim (150 stars, last pushed today), licensed Apache-2.0. It adds 51 tokens to every session and 1,792 once invoked, about $0.0003 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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