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-platform-modelinggit 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-platform-modeling)<a href="https://agentmods.dev/skills/equinor/neqsim/neqsim-platform-modeling"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/neqsim-platform-modeling/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-platform-modeling"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/neqsim-platform-modeling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00096 | $0.12993 |
| Opus 5.5 | $0.00038 | $0.05197 |
| Sonnet 5.5 | $0.00019 | $0.02599 |
| Haiku 4.5 | $0.00010 | $0.01299 |
Grade A, and why
neqsim-platform-modeling 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 yesterday.
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 — 1,165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NeqSim Production Platform Modeling
Comprehensive patterns for building complete topside process simulations from platform design documents. Derived from production-grade models of 15+ NCS platforms (Åsgard A/B, Troll A/B, Grane, Castberg, Martin Linge, Gudrun, etc.) in the NeqSim-dev-environment.
1. Architecture Overview
1.1 Standard Model Structure
Every production platform model follows the same architecture:
ProcessInput (pydantic) → simulate() function → ProcessSystem → calc_result() → ProcessOutput (pydantic)
| Component | Purpose | Pattern |
|---|---|---|
ProcessInput |
All operating conditions as typed fields | Pydantic BaseModel with 50-100+ fields |
simulate() |
Builds and runs the ProcessSystem | Single function, 500-1500 lines |
calc_result() |
Extracts structured results from solved system | Calls simulate(), builds ProcessOutput |
ProcessOutput |
All results in typed, serializable format | Pydantic BaseModel with response objects |
neqsim_responses.py |
Helper functions to extract equipment data | get_separator_response(), get_compressor_response(), etc. |
pydantic_classes.py |
Shared response types | CompressorResponse, SeparatorResponse, HeaterResponse, etc. |
1.2 Two Execution Strategies
Strategy A — Manual Iteration (ASGA pattern):
NUMBER_OF_ITERATIONS = 25
operations = ProcessSystem()
# ... build entire flowsheet ...
for _ in range(NUMBER_OF_ITERATIONS):
operations.run_step()
Best for: models with many recycles that need controlled convergence.
Strategy B — Single Blocking Run (Martin Linge pattern):
operations = ProcessSystem()
# ... build entire flowsheet ...
thread = operations.runAsThread()
thread.join(config.SYNC_REQUEST_TIMEOUT_MS) # e.g., 120000 ms
if thread.isAlive():
thread.interrupt()
raise CalculationTimeout("Timed out")
Best for: models with few recycles where NeqSim's internal solver handles convergence.
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.
- yesterday Changed · +7 lines f12d02b506c9
- 9d ago Changed · +43 lines bafead9e0f0f
- 20d ago Changed · -13 tokens per session ed0142afbbfe
- 1mo ago Changed · +13 lines 6b3deb630f76
- 1mo ago First seen · 1,102 lines · 109 tokens per session scan A 7d92fc9e683b
neqsim-platform-modeling is a skill published in the GitHub repository equinor/neqsim (156 stars, last pushed today), licensed Apache-2.0. It adds 96 tokens to every session and 12,993 once invoked, about $0.0004 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 2026-09-03.
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