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 hydrate-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/hydrate-screening)<a href="https://agentmods.dev/skills/equinor/neqsim-community-skills/hydrate-screening"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/hydrate-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/hydrate-screening"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/hydrate-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 | $0.00043 | $0.00895 |
| Opus 5 | $0.00022 | $0.00447 |
| Sonnet 5 | $0.00009 | $0.00179 |
| Haiku 4.5 | $0.00004 | $0.00089 |
Grade A, and why
neqsim-hydrate-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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hydrate Screening
Use this skill for a quick, public hydrate-risk screening example. It is intentionally simple and should guide users toward validated NeqSim hydrate calculations for real work.
When to Use
- When a user asks for a first-pass hydrate risk triage example.
- When only pressure, temperature, and water presence are available.
- When an agent should explain that validated NeqSim methods are required for real hydrate prediction.
Inputs
pressure: operating pressure in bar.temperature: operating temperature in C.water_present: boolean indicating whether free or condensed water may be present.
Outputs
risk_level:low,medium, orhigh.margin_indicator: temperature margin in C above the placeholder hydrate boundary.estimated_boundary: placeholder hydrate boundary temperature in C.assumptions: public assumptions and required follow-up.
Engineering Method
The placeholder method estimates a simple pressure-dependent boundary and compares operating temperature to that boundary. If water is absent, risk is reported as low with an assumption note. If water is present, low margin gives higher risk.
This is not a thermodynamic hydrate model. Real hydrate calculations should use validated NeqSim methods with a defined fluid composition, water content, inhibitor basis, and operating envelope.
Python Usage Pattern
from hydrate_screening import HydrateScreener
screener = HydrateScreener()
result = screener.screen(pressure=80.0, temperature=4.0, water_present=True)
print(result.risk_level)
print(result.margin_indicator)
print(result.assumptions)
If the optional neqsim Python package is available, the result records that fact so an agent can recommend moving to validated NeqSim hydrate workflows. If not, the example still runs with fallback placeholder logic.
Validation Checklist
- Pressure is positive and temperature is finite.
- Water-present and water-absent cases are tested.
- The placeholder boundary is documented as educational only.
- Results are not used as inhibitor dosage, design margin, or operating limit.
- Real hydrate work is redirected to validated NeqSim methods.
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 · 94 lines · 43 tokens per session scan A 49a8b87769a1
neqsim-hydrate-screening is a skill published in the GitHub repository equinor/neqsim-community-skills (2 stars, last pushed today), licensed Apache-2.0. It adds 43 tokens to every session and 895 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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