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 --skill neqsim-api-patternsgit clone --depth 1 https://github.com/equinor/neqsimWrote 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/neqsim-api-patterns)<a href="https://agentmods.dev/skills/equinor/neqsim/neqsim-api-patterns"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/neqsim-api-patterns/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/neqsim-api-patterns"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/neqsim-api-patterns.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00078 | $0.16957 |
| Opus 5.5 | $0.00031 | $0.06783 |
| Sonnet 5.5 | $0.00016 | $0.03391 |
| Haiku 4.5 | $0.00008 | $0.01696 |
Grade A, and why
neqsim-api-patterns 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 today.
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 — 1,496 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NeqSim API Patterns
Copy-paste reference for common NeqSim operations. All Java code must be Java 8 compatible.
Use progressive disclosure: search headings and read only the task-relevant section.
For property-model selection, diffusion or gas-oil-water interfacial tension, read
references/physical-properties.md and its tested guides.
For black-oil tables, conversion and reservoir export, use docs/blackoil/README.md and
the pvt-simulation agent. For APIs absent from this recipe list, follow
source/runtime discovery.
MCP server vs. Python/Java API — which to use
Default policy: MCP first. For any single calculation, always check whether
a NeqSim MCP tool (mcp_neqsim_*) already covers it before writing Python or
Java. Only drop to code when MCP genuinely cannot do the job. Concretely:
- Curated tool exists (
runFlash,runProcess,runPVT,getPhaseEnvelope,sizeEquipment,calculateStandard,runFlowAssurance,runBatch, ...) → use it directly. Confirm field names withgetSchema/validateInputfirst. - No curated tool, but it might still be reachable → use
runCapability(search→inspectApi→ invoke) before writing any code. runCapabilityreportsinspect-only, or the task needs loops/plotting/ state/notebooks/reports → fall back to the Python API (import neqsim) or Java in a checkout. This is the only reason to write code for a calculation MCP already exposes.- NeqSim itself lacks the capability (not a packaging gap, an engine gap)
→ implement it in Java with tests (
spotless:apply) rather than working around it with ad-hoc Python; seeneqsim-troubleshootingand the continuous-improvement rule inAGENTS.md.
Both the MCP tools and the Python/Java API call the same NeqSim engine — the difference is packaging, not physics — but MCP additionally gives a stable JSON contract, schema validation, and provenance/quality-gate info (EOS, convergence, benchmark trust, standards) for free.
What ships with it
1 file 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.
- today Changed · +11 lines · +18 tokens per session 60e5fbdd462f
- 12d ago Changed · +29 lines c034ba7a5bd9
- 1mo ago Changed · +39 lines d01e72356eed
- 1mo ago Changed · +21 lines 045ad05872c6
- 1mo ago Changed · +12 lines 1300cda73a77
- 1mo ago Changed · +9 lines b15ede74865f
- 1mo ago Changed · +43 lines f6279b6157e5
- 1mo ago First seen · 1,332 lines · 60 tokens per session scan A ccb110e57ed2
neqsim-api-patterns is a skill published in the GitHub repository equinor/neqsim (158 stars, last pushed today), licensed Apache-2.0. It adds 78 tokens to every session and 16,957 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 2026-08-30.
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