agentmods.dev

neqsim-gas-dispersion-distance-screening

neqsim-gas-dispersion-distance-screening is a skill for Claude Code, Codex from equinor/neqsim-community-skills. It costs 84 tokens per session (1,257 once invoked), scanned A, original, Apache-2.0.

An educational calculator for estimating how far a continuous gas release travels downwind before reaching a chosen concentration. It uses a simple Gaussian plume model, which represents a spreading cloud carried by wind.

In plain words
What is it for?
Use it to estimate distance to the lower flammability limit or to a toxic concentration in parts per million, given release and weather inputs.
Why use it?
It provides an early estimate of the extent of a flammable or toxic gas cloud without requiring a full dispersion study.

Skill for Claude CodeCodex

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

not rated 4repo changed 1mo ago A scan 84 tokens original Apache-2.0

Good fit Use it to estimate distance to the lower flammability limit or to a toxic concentration in parts per million, given release and weather inputs.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/equinor/neqsim-community-skills/neqsim-gas-dispersion-distance-screening
View source ↗ equinor/neqsim-community-skills equinor.github.io
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 neqsim-gas-dispersion-distance-screening
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-gas-dispersion-distance-screening

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

agentmods 80×15 button for neqsim-gas-dispersion-distance-screening

Your own site · 80×15
<a href="https://agentmods.dev/skills/equinor/neqsim-community-skills/neqsim-gas-dispersion-distance-screening"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/neqsim-gas-dispersion-distance-screening.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 84 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,257 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 $0.00084 $0.01257
Opus 5.5 $0.00034 $0.00503
Sonnet 5.5 $0.00017 $0.00251
Haiku 4.5 $0.00008 $0.00126

Measured 1mo ago against content hash fe8f382ea60b, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

neqsim-gas-dispersion-distance-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 1mo ago.

The scan reads SKILL.md. This mod also ships 4 executable files (examples/basic_gas_dispersion_distance_screening.py, src/gas_dispersion_distance_screening/__init__.py, src/gas_dispersion_distance_screening/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/safety/neqsim-gas-dispersion-distance-screening/SKILL.md · 117 lines

How it starts

The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Gas Dispersion Distance Screening

Use this skill for public, educational atmospheric-dispersion screening. It estimates the downwind centerline distance at which a continuous gas release falls to a target concentration using the open Gaussian plume model with Briggs rural dispersion coefficients, so an agent can scope a flammable or toxic hazard zone before detailed dispersion analysis.

When to Use

  • When a user asks roughly how far a gas cloud reaches the LFL or a toxic ppm limit.
  • When an agent needs a quick distance-to-target hazard extent for a continuous release.
  • When examples must run without confidential plant layout or consequence-tool data.

Inputs

  • release_rate: continuous mass release rate Q in kg/s.
  • wind_speed: wind speed u in m/s.
  • stability_class: Pasquill-Gifford stability class A-F.
  • target_concentration: target concentration in kg/m3 (use the helper below for vol% or ppm).
  • release_height: effective release height He in m, default 0 (ground level).

Helper: concentration_from_volume_fraction(volume_fraction, molar_mass, temperature_k, pressure_bara) converts a gas volume (mole) fraction to kg/m3 using the ideal-gas law.

Outputs

  • stability_class: the resolved stability class.
  • target_concentration_kg_m3: the target concentration used.
  • hazard_distance_m: farthest downwind distance at or above the target, or null if never reached.
  • peak_concentration_kg_m3: peak centerline concentration over the scan.
  • dispersion_warning: hazard-zone, no-hazard-distance, or beyond-assessment-distance.
  • assumptions: public assumptions used by the placeholder model.

Engineering Method

The Python class GaussianDispersionModel uses the open Gaussian plume model:

  • the centerline concentration uses C = Q / (pi * u * sigma_y * sigma_z) * exp(-He^2 / (2 sigma_z^2)).
  • the dispersion coefficients sigma_y and sigma_z use the Briggs (1973) rural (open-country) formulas for class A-F.
  • the hazard distance is the farthest downwind point at or above the target, found by a logarithmic distance scan.

Read the full file on GitHub · 117 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. 1mo ago Changed · +6 lines fe8f382ea60b
  2. 1mo ago First seen · 111 lines · 84 tokens per session scan A af56b915bf11

Subscribe to this mod's changes

neqsim-gas-dispersion-distance-screening is a skill published in the GitHub repository equinor/neqsim-community-skills (4 stars, last pushed today), licensed Apache-2.0. It adds 84 tokens to every session and 1,257 once invoked, about $0.0003 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 2026-09-19.

Scientific
Related

neqsim-gas-dispersion-distance-screening compared with skills from other repositories

neqsim-gas-dispersion-distance-screening next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
neqsim-gas-dispersion-distance-screeningequinor/neqsim-community-skillsSkill4todayApache-2.0A84Claude Code, Codex
instrument-data-to-allotropeanthropics/knowledge-work-pluginsSkill35k3d agoApache-2.0A123Claude Code
exploratory-data-analysisK-Dense-AI/scientific-agent-skillsSkill56k4d agoMITA83Claude Code
matlabK-Dense-AI/scientific-agent-skillsSkill56k4d agoMITA44Claude Code
phylogeneticsK-Dense-AI/scientific-agent-skillsSkill56k4d agoMITA71Claude Code, Codex
research-engineerdavila7/claude-code-templatesSkill33kyesterdayMITA43Claude Code, Codex
mapping-to-snomedmaziyarpanahi/openmedSkill5.8kyesterdayApache-2.0A205Claude Code