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 pressure-drop-screeninggit 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/pressure-drop-screening)<a href="https://agentmods.dev/skills/equinor/neqsim-community-skills/pressure-drop-screening"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/pressure-drop-screening/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/pressure-drop-screening"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/pressure-drop-screening.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 | $0.00062 | $0.01268 |
| Opus 5 | $0.00031 | $0.00634 |
| Sonnet 5 | $0.00012 | $0.00254 |
| Haiku 4.5 | $0.00006 | $0.00127 |
Grade A, and why
neqsim-pressure-drop-screening 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 11d 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pressure Drop Screening
Use this skill for public, educational line pressure-drop screening. It estimates a single-phase Darcy-Weisbach pressure gradient and compares it to a recommended pressure-gradient guideline so an agent can flag lines that may exceed standard hydraulic limits before moving to validated line sizing.
When to Use
- When a user asks whether a line pressure drop sits within recommended limits.
- When an agent needs a quick pressure-gradient triage to scope a hydraulic or debottlenecking study.
- When examples must run without confidential piping classes, project line lists, or company piping specs.
Inputs
fluid_velocity: actual flowing velocity in the line in m/s.mixture_density: flowing mixture density in kg/m3.viscosity: flowing dynamic viscosity in Pa.s.pipe_inner_diameter: pipe inner diameter in m.length: line length used for the total pressure drop in m, default 100.0.roughness: absolute pipe roughness in m, default 4.6e-5 (commercial steel).guideline_bar_per_100m: recommended maximum pressure gradient in bar per 100 m, default 0.5.
Outputs
reynolds_number: flow Reynolds number.friction_factor: Darcy friction factor from laminar or Haaland turbulent form.dp_per_100m_bar: screening pressure gradient in bar per 100 m.dp_total_bar: screening total pressure drop over the line length in bar.guideline_ratio: ratio of the pressure gradient to the recommended guideline.pressure_drop_warning:ok,watch, orhigh.assumptions: public assumptions used by the placeholder model.
Engineering Method
The Python class PressureDropModel uses open, published correlations only:
- the Reynolds number uses the standard
Re = rho v D / muform. - the friction factor uses
f = 64 / Rein laminar flow and the public Haaland explicit approximation of the Colebrook equation in turbulent flow. - the pressure gradient uses the Darcy-Weisbach form
dP/L = f (1/D) (rho v^2 / 2). - the guideline ratio compares the pressure gradient to a configurable recommended gradient aligned with NORSOK P-002 and GPSA style line pressure-gradient guidelines.
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.
- 11d ago First seen · 112 lines · 62 tokens per session scan A 55315af2edd9
neqsim-pressure-drop-screening is a skill published in the GitHub repository equinor/neqsim-community-skills (2 stars, last pushed yesterday), licensed Apache-2.0. It adds 62 tokens to every session and 1,268 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 2026-08-31.
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