Borrowing it
Nothing to install: this file belongs to equinor/neqsim. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/equinor/neqsim/master/.github/skills/neqsim-regression-baselines/SKILL.mdgit clone --depth 1 https://github.com/equinor/neqsimWrote 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.
[](https://agentmods.dev/skills/equinor/neqsim/neqsim-regression-baselines)<a href="https://agentmods.dev/skills/equinor/neqsim/neqsim-regression-baselines"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/neqsim-regression-baselines/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/equinor/neqsim/neqsim-regression-baselines"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/neqsim-regression-baselines.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00051 | $0.01792 |
| Opus 5.5 | $0.00020 | $0.00717 |
| Sonnet 5.5 | $0.00010 | $0.00358 |
| Haiku 4.5 | $0.00005 | $0.00179 |
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 1mo 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.
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 |
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.
- 1mo ago First seen · 195 lines · 51 tokens per session scan A a8ae624389af
neqsim-regression-baselines is a skill published in the GitHub repository equinor/neqsim (160 stars, last pushed today), licensed Apache-2.0. It adds 51 tokens to every session and 1,792 once invoked, about $0.0002 per session on Opus 5.5. 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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