neqsim-separator-modelling

neqsim-separator-modelling is a skill for Claude Code, Codex from equinor/neqsim-community-skills. It costs 37 tokens per session (1,127 once invoked), scanned A, original, Apache-2.0.

An educational screening model for gas-liquid separator capacity. It uses gas and liquid flow conditions to produce simple indicators for gas loading and liquid residence time.

In plain words
What is it for?
Use it to screen gas load and liquid residence time, identify possible capacity concerns, and prepare an early separator study.
Why use it?
It helps structure early separator questions when detailed geometry, project data, or proprietary design rules are unavailable. Its warnings are preliminary and should be followed by validated simulation or design.

Skill for Claude CodeCodex

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

Good fit Use it to screen gas load and liquid residence time, identify possible capacity concerns, and prepare an early separator study.

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

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-separator-modelling

README.md
[![agentmods](https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/separator-modelling/github.svg)](https://agentmods.dev/skills/equinor/neqsim-community-skills/separator-modelling)
Your own site
<a href="https://agentmods.dev/skills/equinor/neqsim-community-skills/separator-modelling"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/separator-modelling/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 neqsim-separator-modelling

Your own site · 80×15
<a href="https://agentmods.dev/skills/equinor/neqsim-community-skills/separator-modelling"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/separator-modelling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 37 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,127 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 $0.00037 $0.01127
Opus 5 $0.00018 $0.00563
Sonnet 5 $0.00007 $0.00225
Haiku 4.5 $0.00004 $0.00113

Measured 10d ago against content hash 63fa3043a7d8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

neqsim-separator-modelling 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 10d ago.

The scan reads SKILL.md. This mod also ships 4 executable files (examples/basic_separator_screening.py, src/separator_modelling/__init__.py, src/separator_modelling/model.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.

skills/process/separator-modelling/SKILL.md · 109 lines

How it starts

The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Separator Modelling Screening

Use this skill for public, educational gas/liquid separator screening examples. It provides simple indicators that help agents structure early questions before moving to validated process simulation or detailed design.

When to Use

  • When a user asks for a simple separator capacity screening example.
  • When an agent needs a public placeholder model for gas load and liquid residence time.
  • When examples must run without confidential separator geometry, project data, or company design rules.

Inputs

  • gas_flow: gas volumetric flow rate in a consistent public unit, default examples use m3/h.
  • liquid_flow: liquid volumetric flow rate in the same time basis, default examples use m3/h.
  • pressure: operating pressure in bar.
  • temperature: operating temperature in C.
  • gas_density: gas density in kg/m3.
  • liquid_density: liquid density in kg/m3.

Outputs

  • gas_load_indicator: dimensionless screening load where values above 1.0 indicate high gas load for the placeholder basis.
  • residence_time_indicator: dimensionless screening residence indicator where values below 1.0 indicate low liquid residence time for the placeholder basis.
  • capacity_warning: ok, watch, or high.
  • assumptions: public assumptions used by the placeholder model.

Engineering Method

The Python class SeparatorModel uses open placeholder calculations only:

  • gas load indicator increases with gas flow and gas-density square root
  • liquid residence time is estimated from a configurable public holdup volume and liquid flow
  • warnings are rule-based thresholds on the two indicators

This is educational and screening-only logic. It is not a design standard, separator sizing method, vendor method, or replacement for a validated NeqSim process model.

Python Usage Pattern

from separator_modelling import SeparatorModel

model = SeparatorModel()
result = model.evaluate(
    gas_flow=18_000.0,
    liquid_flow=120.0,
    pressure=55.0,
    temperature=35.0,
    gas_density=18.0,
    liquid_density=720.0,
)

print(result.capacity_warning)
print(result.gas_load_indicator)
print(result.residence_time_indicator)

Read the full file on GitHub · 109 lines

Files

What ships with it

6 files 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. 10d ago First seen · 109 lines · 37 tokens per session scan A 63fa3043a7d8

Subscribe to this mod's changes

neqsim-separator-modelling is a skill published in the GitHub repository equinor/neqsim-community-skills (2 stars, last pushed yesterday), licensed Apache-2.0. It adds 37 tokens to every session and 1,127 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 2026-08-31.

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