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 gas-dispersion-distance-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/gas-dispersion-distance-screening)<a href="https://agentmods.dev/skills/equinor/neqsim-community-skills/gas-dispersion-distance-screening"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/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.
<a href="https://agentmods.dev/skills/equinor/neqsim-community-skills/gas-dispersion-distance-screening"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/gas-dispersion-distance-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 | $0.00084 | $0.01199 |
| Opus 5 | $0.00042 | $0.00600 |
| Sonnet 5 | $0.00017 | $0.00240 |
| Haiku 4.5 | $0.00008 | $0.00120 |
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 12d 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 — 111 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 rateQin kg/s.wind_speed: wind speeduin m/s.stability_class: Pasquill-Gifford stability classA-F.target_concentration: target concentration in kg/m3 (use the helper below for vol% or ppm).release_height: effective release heightHein 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, ornullif never reached.peak_concentration_kg_m3: peak centerline concentration over the scan.dispersion_warning:hazard-zone,no-hazard-distance, orbeyond-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_yandsigma_zuse 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.
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.
- examples/basic_gas_dispersion_distance_screening.py 1.1 KB runs code
- pyproject.toml 574 B
- README.md 968 B
- src/gas_dispersion_distance_screening/__init__.py 136 B runs code
- src/gas_dispersion_distance_screening/model.py 6.4 KB runs code
- tests/test_gas_dispersion_distance_screening.py 2.1 KB runs code
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.
- 12d ago First seen · 111 lines · 84 tokens per session scan A af56b915bf11
neqsim-gas-dispersion-distance-screening is a skill published in the GitHub repository equinor/neqsim-community-skills (2 stars, last pushed yesterday), licensed Apache-2.0. It adds 84 tokens to every session and 1,199 once invoked, about $0.0004 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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