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-model-calibration-and-data-reconciliationgit 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-model-calibration-and-data-reconciliation)<a href="https://agentmods.dev/skills/equinor/neqsim/neqsim-model-calibration-and-data-reconciliation"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/neqsim-model-calibration-and-data-reconciliation/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-model-calibration-and-data-reconciliation"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/neqsim-model-calibration-and-data-reconciliation.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 | $0.00080 | $0.02073 |
| Opus 5.5 | $0.00032 | $0.00829 |
| Sonnet 5.5 | $0.00016 | $0.00415 |
| Haiku 4.5 | $0.00008 | $0.00207 |
Grade A, and why
neqsim-model-calibration-and-data-reconciliation 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 — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Calibration and Data Reconciliation
A practical workflow for calibrating NeqSim models against plant historian data and producing auditable validation output.
When to Use This Skill
- When simulated values do not match measured plant data.
- When building digital twin loops for separators, compressors, or heat exchangers.
- When you need reproducible parameter tuning with physical bounds.
- When a study requires train/validation split and objective fit metrics.
Scope and Non-Goals
This skill focuses on steady-state calibration and data reconciliation.
This skill does not replace dynamic controller tuning. For transient tuning,
load neqsim-dynamic-simulation in addition.
Required Companion Skills
neqsim-plant-data: historian read patterns (PI/IP.21), tag maps, quality flags.neqsim-input-validation: parameter bounds and physically realistic inputs.neqsim-troubleshooting: convergence recovery when each case is re-simulated.
Calibration Workflow
- Define objective and acceptance criteria.
- Build a trusted tag map from plant tags to model variables.
- Read, clean, and quality-filter plant data.
- Detect steady-state windows for fitting.
- Choose tunable model parameters with hard physical bounds.
- Run bounded optimization and re-simulate the full NeqSim process each iteration.
- Validate on holdout windows and publish fit diagnostics.
Typical Tunable Parameters
| Equipment | Parameters to Tune | Keep Fixed Unless Needed |
|---|---|---|
| Compressor | Polytropic efficiency, pressure ratio correction, anti-surge margin | Driver type, map family |
| Heat exchanger | UA correction factor, fouling factor | Duty sign convention, stream topology |
| Separator | Entrainment assumptions, liquid carry-over tuning factors | Phase model and EOS |
| Valve/choke | Cv multiplier, discharge coefficient | Control architecture |
| Pipeline | Roughness, overall heat transfer coefficient | Length and diameter |
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 Changed · +2 lines fd50835810a2
- 1mo ago First seen · 219 lines · 80 tokens per session scan A 53ddff3eaaca
neqsim-model-calibration-and-data-reconciliation is a skill published in the GitHub repository equinor/neqsim (156 stars, last pushed today), licensed Apache-2.0. It adds 80 tokens to every session and 2,073 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 2026-09-03.
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