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.
npx skills add equinor/neqsim-community-skills --skill separator-modellinggit clone --depth 1 https://github.com/equinor/neqsim-community-skillsWrote 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-community-skills/separator-modelling)<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.
<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>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 | $0.00037 | $0.01127 |
| Opus 5 | $0.00018 | $0.00563 |
| Sonnet 5 | $0.00007 | $0.00225 |
| Haiku 4.5 | $0.00004 | $0.00113 |
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.
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 — 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, orhigh.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)
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.
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.
- 10d ago First seen · 109 lines · 37 tokens per session scan A 63fa3043a7d8
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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