Technical Analyst
tradermonty/claude-trading-skills
This skill should be used when analyzing weekly price charts for stocks, stock indices, cryptocurrencies, or forex pairs.
Guides agents through ProcessLinkedMPC, ProcessLinearizer, ModelPredictiveController, VirtualFlowMeter, SoftSensor, and DataReconciliationEngine.
Install Neqsim Advanced Control Mpc And Virtual Sensing
$ npx skills add equinor/neqsim --skill neqsim-advanced-control-mpc-and-virtual-sensing -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install equinor/neqsim neqsim-advanced-control-mpc-and-virtual-sensing --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/equinor/neqsim.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.github/skills/neqsim-advanced-control-mpc-and-virtual-sensing .claude/skills/neqsim-advanced-control-mpc-and-virtual-sensing && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "neqsim-advanced-control-mpc-and-virtual-sensing" agent skill from https://github.com/equinor/neqsim/tree/master/.github/skills/neqsim-advanced-control-mpc-and-virtual-sensing into .claude/skills/neqsim-advanced-control-mpc-and-virtual-sensing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "neqsim-advanced-control-mpc-and-virtual-sensing", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/equinor/neqsim/tree/master/.github/skills/neqsim-advanced-control-mpc-and-virtual-sensingType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add equinor/neqsim --skill neqsim-advanced-control-mpc-and-virtual-sensing -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install equinor/neqsim neqsim-advanced-control-mpc-and-virtual-sensing --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/equinor/neqsim.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.github/skills/neqsim-advanced-control-mpc-and-virtual-sensing .agents/skills/neqsim-advanced-control-mpc-and-virtual-sensing && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "neqsim-advanced-control-mpc-and-virtual-sensing" agent skill from https://github.com/equinor/neqsim/tree/master/.github/skills/neqsim-advanced-control-mpc-and-virtual-sensing into .agents/skills/neqsim-advanced-control-mpc-and-virtual-sensing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "neqsim-advanced-control-mpc-and-virtual-sensing", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add equinor/neqsim --skill neqsim-advanced-control-mpc-and-virtual-sensing -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install equinor/neqsim neqsim-advanced-control-mpc-and-virtual-sensing --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/equinor/neqsim.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.github/skills/neqsim-advanced-control-mpc-and-virtual-sensing .cursor/skills/neqsim-advanced-control-mpc-and-virtual-sensing && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "neqsim-advanced-control-mpc-and-virtual-sensing" agent skill from https://github.com/equinor/neqsim/tree/master/.github/skills/neqsim-advanced-control-mpc-and-virtual-sensing into .cursor/skills/neqsim-advanced-control-mpc-and-virtual-sensing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "neqsim-advanced-control-mpc-and-virtual-sensing", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/equinor/neqsim.git --path .github/skills/neqsim-advanced-control-mpc-and-virtual-sensing--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add equinor/neqsim --skill neqsim-advanced-control-mpc-and-virtual-sensing -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install equinor/neqsim neqsim-advanced-control-mpc-and-virtual-sensing --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/equinor/neqsim.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.github/skills/neqsim-advanced-control-mpc-and-virtual-sensing .gemini/skills/neqsim-advanced-control-mpc-and-virtual-sensing && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "neqsim-advanced-control-mpc-and-virtual-sensing" agent skill from https://github.com/equinor/neqsim/tree/master/.github/skills/neqsim-advanced-control-mpc-and-virtual-sensing into .gemini/skills/neqsim-advanced-control-mpc-and-virtual-sensing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "neqsim-advanced-control-mpc-and-virtual-sensing", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install equinor/neqsim neqsim-advanced-control-mpc-and-virtual-sensingInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add equinor/neqsim --skill neqsim-advanced-control-mpc-and-virtual-sensing -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/equinor/neqsim.git skills-src && mkdir -p .github/skills && cp -r skills-src/.github/skills/neqsim-advanced-control-mpc-and-virtual-sensing .github/skills/neqsim-advanced-control-mpc-and-virtual-sensing && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "neqsim-advanced-control-mpc-and-virtual-sensing" agent skill from https://github.com/equinor/neqsim/tree/master/.github/skills/neqsim-advanced-control-mpc-and-virtual-sensing into .github/skills/neqsim-advanced-control-mpc-and-virtual-sensing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "neqsim-advanced-control-mpc-and-virtual-sensing", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add equinor/neqsim --skill neqsim-advanced-control-mpc-and-virtual-sensing -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install equinor/neqsim neqsim-advanced-control-mpc-and-virtual-sensing --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/equinor/neqsim.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.github/skills/neqsim-advanced-control-mpc-and-virtual-sensing .opencode/skills/neqsim-advanced-control-mpc-and-virtual-sensing && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "neqsim-advanced-control-mpc-and-virtual-sensing" agent skill from https://github.com/equinor/neqsim/tree/master/.github/skills/neqsim-advanced-control-mpc-and-virtual-sensing into .opencode/skills/neqsim-advanced-control-mpc-and-virtual-sensing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "neqsim-advanced-control-mpc-and-virtual-sensing", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
neqsim-advanced-control-mpc-and-virtual-sensing- GitHub stars
- 156
- Token cost
- ~3.7k tokens
- SKILL.md length
- 1,128 words
- Files
- 1
- Skills in repo
- 12
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Guides agents through ProcessLinkedMPC, ProcessLinearizer, ModelPredictiveController, VirtualFlowMeter, SoftSensor, and DataReconciliationEngine.
- : identifying a process model for MPC
- SKILL.md covers When to use this, Class map, Build pattern and Result extraction, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Exporting state-space
What it does
Neqsim Advanced Control Mpc And Virtual Sensing is an agent skill from equinor/neqsim. Guides agents through ProcessLinkedMPC, ProcessLinearizer, ModelPredictiveController, VirtualFlowMeter, SoftSensor, and DataReconciliationEngine. USE WHEN: identifying a process model for MPC, exporting state-space or industrial controller models, estimating unmetered flow or properties, checking controller structures, detecting steady state, reconciling noisy measurements, or screening controller performance.
Its SKILL.md is about 3.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Business, Finance & HR. The repository describes itself as: NeqSim is a library for calculation of fluid behavior, phase equilibrium and process simulation. The licence is Apache-2.0.
When your agent uses it
- : identifying a process model for MPC
- Exporting state-space
- Industrial controller models
- Estimating unmetered flow
Example prompts
- “Use the neqsim-advanced-control-mpc-and-virtual-sensing skill to guide agents through ProcessLinkedMPC, ProcessLinearizer…”
- “/neqsim-advanced-control-mpc-and-virtual-sensing”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit c3b4216. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are java and python).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Neqsim Advanced Control Mpc And Virtual Sensing loads about 3.7k tokens when it runs. Until then it costs about 115 tokens; SKILL.md has 1,128 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from equinor/neqsim at commit c3b4216, republished under its Apache-2.0 licence (© equinor). 1,128 words, ~3,671 tokens.
Download SKILL.mdSave it as
.claude/skills/neqsim-advanced-control-mpc-and-virtual-sensing/SKILL.md (or your agent's skills folder).- name
- neqsim-advanced-control-mpc-and-virtual-sensing
- description
- Guides agents through ProcessLinkedMPC, ProcessLinearizer, ModelPredictiveController, VirtualFlowMeter, SoftSensor, and DataReconciliationEngine. USE WHEN: identifying a process model for MPC, exporting state-space or industrial controller models, estimating unmetered flow or properties, checking controller structures, detecting steady state, reconciling noisy measurements, or screening controller performance.
- last_verified
- 2026-10-03
Advanced Control, MPC, and Virtual Sensing
When to use this
Use this skill when NeqSim's existing dynamic/process model needs a controller-facing model, bounded receding-horizon control, a physics-based soft measurement, or steady-state data reconciliation.
ProcessLinkedMPClinks configured manipulated, controlled, disturbance, and state variables to one existingProcessSystem; it identifies local dynamics and computes/apply moves.ProcessLinearizer,StepResponseGenerator,StateSpaceExporter,IndustrialMPCExporter, andSubrModlExportercreate model-identification and integration artifacts.ModelPredictiveControlleris a controller-device implementation with a first-order prediction model, constraints, feed-quality prediction, and optional moving-horizon estimation. It is not a DCS emulator.VirtualFlowMeterandSoftSensorestimate quantities/properties from a stream's fluid and supplied operating conditions. They are screening/model estimates, not custody-transfer instruments.SteadyStateDetectorandDataReconciliationEnginescreen input data and reconcile measured values against declared linear balance constraints.
Keep each layer distinct: dynamic simulation models process response (neqsim-dynamic-simulation); controllability/operability maps feasible envelopes (neqsim-controllability-operability); calibration and plant-data workflows select and qualify measurements (neqsim-model-calibration-and-data-reconciliation, neqsim-plant-data). Compressor minimum-speed/recycle coordination belongs to neqsim-compressor-antisurge-recycle.
Class map
| Class or family | Package | What it does | Key methods verified |
|---|---|---|---|
ProcessLinkedMPC | neqsim.process.mpc | Binds MVs/CVs/DVs/SVRs to a ProcessSystem, identifies its model and performs repeated control calculations. | Constructor (String, ProcessSystem); addMV, addCV, addCVZone, addDV, addSVR, identifyModel, calculate, applyMoves, getLastMoves, getLinearizationResult, exportModel |
ProcessLinearizer, variable classes | neqsim.process.mpc | Computes local finite-difference gains for configured manipulated/controlled/disturbance variables. | ProcessLinearizer(ProcessSystem), addMV, addCV, addDV, linearize, setDefaultPerturbationSize |
StepResponseGenerator, StepResponse, NonlinearPredictor, derivative calculator | neqsim.process.mpc | Builds step-response models, finite-difference process responses, and optional nonlinear prediction inputs. | StepResponse constructor and response getters are documented in MPCIntegrationTest; verify a generator method before use |
| State-space and industrial exporters | neqsim.process.mpc | Exports local linear model to discrete state-space, Industrial MPC, SubrModl, soft-sensor, and controller data-exchange formats. | StateSpaceExporter(LinearizationResult), toDiscreteStateSpace, getA, getB, getC, getD; createIndustrialExporter, createSubrModlExporter |
ModelPredictiveController | neqsim.process.controllerdevice | Standalone first-order, receding-horizon controller device with linear quality constraints and feed disturbance handling. | Constructors (), (String); setPredictionHorizon, setControllerSetPoint, setTransmitter, getResponse |
| Controller benchmarks and metrics/events | neqsim.process.controllerdevice | Records controller benchmark scenarios, performance metrics, and events for comparative assessment. | APIs vary by object; inspect source before assuming metric units or benchmark acceptance |
| Logic/sequence/transfer-function blocks | neqsim.process.controllerdevice | Provides logic blocks, sequential function charts, and transfer-function structures. | Use their public interfaces and transition/update APIs from source; these are not a plant DCS runtime |
| Cascade, feed-forward, override, ratio, split-range, minimum-speed recycle | neqsim.process.controllerdevice.structure | Composes controller outputs for common multiloop, selector, and recycle-coordination patterns. | Minimum-speed integration is detailed in neqsim-compressor-antisurge-recycle |
VirtualFlowMeter, VFMResult, UncertaintyBounds | neqsim.process.measurementdevice.vfm | Estimates gas/oil/water rates and simple uncertainty bounds from stream fluid, pressure drop, choke opening, and calibration factor. | VirtualFlowMeter(String, StreamInterface), calculateFlowRates, setMeasurementUncertainties; getOilFlowRate, getGasFlowRate, getWaterFlowRate, getQuality, uncertainty getters |
SoftSensor | neqsim.process.measurementdevice.vfm | Estimates supported fluid properties by flashing a cloned stream fluid at supplied P/T. | SoftSensor(String, StreamInterface, PropertyType), setInput, setInputs, estimate |
SteadyStateDetector, SteadyStateVariable | neqsim.process.util.reconciliation | Tests incoming variable histories with a sliding-window steady-state detector before reconciliation/calibration. | SteadyStateDetector(int), addVariable, updateVariable, evaluate |
DataReconciliationEngine, ReconciliationVariable, ReconciliationResult | neqsim.process.util.reconciliation | Applies weighted least squares to measurement values under caller-defined linear equality balances; reports statistical flags. | addVariable, addConstraint(double[]), setGrossErrorThreshold, reconcile; isConverged, getErrorMessage, getVariables, getChiSquareStatistic, getGrossErrors |
Build pattern
The example starts from a converged separator/valve process. The local linear model and MPC operate around this configured point; they do not replace transient simulation or a validated control-system design.
import neqsim.process.mpc.ProcessLinkedMPC;
import neqsim.process.equipment.stream.Stream;
import neqsim.process.equipment.valve.ThrottlingValve;
import neqsim.process.equipment.separator.Separator;
import neqsim.process.processmodel.ProcessSystem;
import neqsim.thermo.system.SystemInterface;
import neqsim.thermo.system.SystemSrkEos;
SystemInterface fluid = new SystemSrkEos(298.15, 50.0);
fluid.addComponent("methane", 0.8);
fluid.addComponent("ethane", 0.1);
fluid.addComponent("propane", 0.05);
fluid.addComponent("n-pentane", 0.05);
fluid.setMixingRule("classic");
Stream feed = new Stream("feed", fluid);
feed.setFlowRate(100.0, "kg/hr");
feed.setTemperature(25.0, "C");
feed.setPressure(50.0, "bara");
ThrottlingValve valve = new ThrottlingValve("inlet_valve", feed);
valve.setOutletPressure(30.0);
Separator separator = new Separator("separator", valve.getOutletStream());
ProcessSystem process = new ProcessSystem();
process.add(feed);
process.add(valve);
process.add(separator);
process.run();
ProcessLinkedMPC mpc = new ProcessLinkedMPC("separator-pressure", process);
mpc.addMV("inlet_valve", "opening", 0.0, 1.0, 0.1);
mpc.addCV("separator", "pressure", 30.0);
mpc.setConstraint("separator", "pressure", 20.0, 40.0);
mpc.identifyModel(60.0);
mpc.setPredictionHorizon(20);
mpc.setControlHorizon(5);
double[] moves = mpc.calculate();
mpc.applyMoves();Python exposes the same Java class lookup pattern. Use the shared NeqSim Python environment, and check whether the installed bridge requires Java overload disambiguation.
from neqsim import jneqsim
SystemSrkEos = jneqsim.thermo.system.SystemSrkEos
Stream = jneqsim.process.equipment.stream.Stream
ThrottlingValve = jneqsim.process.equipment.valve.ThrottlingValve
Separator = jneqsim.process.equipment.separator.Separator
ProcessSystem = jneqsim.process.processmodel.ProcessSystem
ProcessLinkedMPC = jneqsim.process.mpc.ProcessLinkedMPC
fluid = SystemSrkEos(298.15, 50.0)
fluid.addComponent("methane", 0.8)
fluid.addComponent("ethane", 0.1)
fluid.addComponent("propane", 0.05)
fluid.addComponent("n-pentane", 0.05)
fluid.setMixingRule("classic")
feed = Stream("feed", fluid)
feed.setFlowRate(100.0, "kg/hr")
feed.setTemperature(25.0, "C")
feed.setPressure(50.0, "bara")
valve = ThrottlingValve("inlet_valve", feed)
valve.setOutletPressure(30.0)
separator = Separator("separator", valve.getOutletStream())
process = ProcessSystem()
process.add(feed)
process.add(valve)
process.add(separator)
process.run()
mpc = ProcessLinkedMPC("separator-pressure", process)
mpc.addMV("inlet_valve", "opening", 0.0, 1.0, 0.1)
mpc.addCV("separator", "pressure", 30.0)
mpc.setConstraint("separator", "pressure", 20.0, 40.0)
mpc.identifyModel(60.0)
mpc.setPredictionHorizon(20)
mpc.setControlHorizon(5)
moves = mpc.calculate()
mpc.applyMoves()Result extraction
| Output | Exact accessor | Units / meaning |
|---|---|---|
| Local gain model | getLinearizationResult(), then getGainMatrix(), getGain(cvName, mvName), getMvOperatingPoint(), getCvOperatingPoint() | Gain units are output-unit per input-unit; keep named variable units consistent |
| Control demand | calculate() or getLastMoves() | double[], one move per configured MV in declaration order; values follow the configured variable bounds/units |
| Process mutation | applyMoves() | Applies the last calculated move set to the linked process equipment |
| State-space export | exportModel().toDiscreteStateSpace(sampleTime); model getSampleTime, getA/B/C/D | Discrete sample time is seconds; matrix states/inputs/outputs inherit model variable definitions |
| VFM phase rates | VFMResult.getOilFlowRate(), getGasFlowRate(), getWaterFlowRate(), getTotalLiquidFlowRate() | Sm3/d |
| VFM quality/uncertainty | getQuality(), getOilUncertainty(), getGasUncertainty(), getWaterUncertainty() | HIGH, NORMAL, LOW, EXTRAPOLATED, INVALID; uncertainty record carries unit and bounds |
| Soft sensor | estimate() | Default unit depends on PropertyType: e.g. density kg/m3, viscosity cP, pressure bara, GOR Sm3/Sm3 |
| Reconciliation | isConverged, getErrorMessage, getVariables, getConstraintResidualsBefore/After, getChiSquareStatistic, isGlobalTestPassed, getGrossErrors | Residual arrays follow constraint insertion order; variable values preserve declared input units |
Show full SKILL.md (459 more words)Show less
Gotchas
- Identify the MPC only after the process has a valid solved operating point.
calculate()beforeidentifyModel()throwsIllegalStateException;applyMoves()before a calculation also throws. ProcessLinearizerperturbs a local model; results can be misleading at hard bounds, discontinuities, phase transitions, or a non-converged base point. Re-check nonlinear behavior over the intended operating envelope.- MVs/CVs use the property access and unit behavior of the bound equipment variables. Use explicit physical bounds, rate limits, and compatible engineering units; don't assume a universal percent, bara, or temperature conversion.
ModelPredictiveControlleruses an internal first-order model and analytical constrained move calculation. It does not emulate a DCS, PLC, SIS, communications stack, alarm rationalization, or vendor controller execution environment.- State-space, Industrial MPC, and SubrModl exporters produce files/configuration for integration; they do not deploy, authenticate, or validate a target controller.
VirtualFlowMeterimplements a simplified differential-pressure multiplier (sqrt(abs(dP))) with a calibration factor, not a standards-based meter sizing/flow equation. Its output is an estimate; qualityHIGHis not a custody-transfer approval.- VFM returns
INVALIDwhen stream/fluid is absent or a flash fails. Phase-absent rates remain zero; distinguish a physical zero from an unavailable phase/model result. SoftSensor.estimate()returnsNaNwhen stream/fluid is unavailable, the flash fails, or a selected phase property cannot be evaluated. TestDouble.isFiniteand preserve the output unit.DataReconciliationEnginerequires more variables than constraints. Constraints are caller-defined linear rows with coefficients ordered exactly as variables were added; the engine does not derive a process flowsheet balance automatically.- Reconciliation failure returns a result with
isConverged()==falseand a message such asNo variables added,No constraints added, orNeed more variables ...; never report default values as reconciled measurements. SteadyStateDetector's R-statistic is a data-window screen. A steady-state verdict does not prove tag independence, sensor health, process stability outside the selected window, or calibration validity.
Validation / benchmarks
- Start from a converged steady state; test local gain signs and magnitudes against independent small positive/negative perturbations and the intended process direction.
- Check response bounds, move limits, control horizon, prediction horizon, sample time, quality constraints, and model-validity region before using moves in a study.
- Compare controller performance with
ControlsBenchmarkSuiteand reportControllerPerformanceMetrics; retain event history and failed scenarios. Benchmark completion is not DCS or functional-safety qualification. - Validate exported matrices by reconstructing the operating-point response and checking dimensions, named-variable ordering, and units. Test imports in the actual target platform before claiming interoperability.
- For VFM, compare against independent well tests across multiple operating points; report calibration range, uncertainty basis, and EXTRAPOLATED/INVALID cases.
- Run steady-state detection on selected, quality-screened signals; reconcile only after the feed/product balance equations, sigmas, degrees of freedom, and gross-error flags have been reviewed.
- For plant-data calibration and historian comparison, follow
neqsim-model-calibration-and-data-reconciliationandneqsim-plant-datarather than folding data acquisition into this controller layer.
Related skills
neqsim-dynamic-simulationneqsim-compressor-antisurge-recycleneqsim-model-calibration-and-data-reconciliationneqsim-controllability-operabilityneqsim-optimization-and-doeneqsim-plant-dataneqsim-pid-process-operationsneqsim-process-safety
© equinor, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in .github/skills/neqsim-advanced-control-mpc-and-virtual-sensing of equinor/neqsim.
Open the folder on GitHubat commit c3b4216
Compare with similar skills
Neqsim Advanced Control Mpc And Virtual Sensing next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Neqsim Advanced Control Mpc And Virtual Sensing this skillequinor/neqsim | 156 | — | ~3.7k | Automated safety check: Pass | Apache-2.0 | |
| Technical Analysttradermonty/claude-trading-skills | 3k | 4 repos | ~4.6k | Automated safety check: Pass | MIT | |
| Theme Detectortradermonty/claude-trading-skills | 3k | 2 repos | ~4.9k | Automated safety check: Pass | MIT | |
| Creating Financial ModelsChen-zexi/open-ptc-agent | 729 | 3 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Stock APIzhangxiangliang/stock-api | 2k | — | ~507 | Automated safety check: Pass | MIT | |
| Itr Walakaranb192/itr-wala | 871 | — | ~3.6k | Automated safety check: Pass | MIT |
Similar skills
Theme Detector
tradermonty/claude-trading-skills
Detect and analyze trending market themes across sectors. An agent skill from tradermonty/claude-trading-skills.
Creating Financial Models
Chen-zexi/open-ptc-agent
This skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions
Stock API
zhangxiangliang/stock-api
Fetch real-time stock quotes, K-line (candlestick) history, and search symbols for China A-shares, Hong Kong, and US markets.
Itr Wala
karanb192/itr-wala
File Indian income tax returns (ITR) for FY 2025-26 / AY 2026-27.
Tushare Data
zillionare/zillionare
面向中文自然语言的 Tushare 数据研究技能。用于把“看看这只股票最近怎么样”“帮我查财报趋势”“最近哪个板块最强”“北向资金在买什么”“给我导出一份行情数据”这类请求，转成可执行的数据获取、清洗、对比、筛选、导出与简要分析流程。适用于 A 股、指数、ETF/基金、财务、估值、资金流、公告新闻、板块概念与宏观数据等研究场景。
More from equinor/neqsim
All 11 skills in this repoNeqsim Acid Gas Treating
equinor/neqsim
Guides NeqSim agents through acid-gas and contaminant removal with SimpleAmineAbsorber, SimpleAmineRegenerator, SystemKentEisenberg, SystemDesmukhMather, RateBasedAbsorber, MembraneSeparator, and…
Neqsim Agent Handoff
equinor/neqsim
Agent-to-agent communication schema for NeqSim. An agent skill from equinor/neqsim.
Neqsim Autonomous Investigation
equinor/neqsim
Autonomous observe-hypothesize-predict-test-discriminate loop for operational anomalies.
Neqsim Ccs Hydrogen
equinor/neqsim
CO2 capture, transport, storage (CCS) and hydrogen systems patterns for NeqSim.
Neqsim Code Hygiene
equinor/neqsim
Fix formatting, Checkstyle, Spotless, and JavaDoc build failures in NeqSim Java code.
Neqsim Column Internals And Rate Based
equinor/neqsim
Guides agents in choosing equilibrium-stage versus rate-based packed columns, selecting solvers, sizing trays/packing, and checking hydraulic limits.
Related
Categories
Questions about Neqsim Advanced Control Mpc And Virtual Sensing
What does Neqsim Advanced Control Mpc And Virtual Sensing do?
Guides agents through ProcessLinkedMPC, ProcessLinearizer, ModelPredictiveController, VirtualFlowMeter, SoftSensor, and DataReconciliationEngine. Neqsim Advanced Control Mpc And Virtual Sensing is an agent skill from equinor/neqsim. Guides agents through ProcessLinkedMPC, ProcessLinearizer, ModelPredictiveController, VirtualFlowMeter, SoftSensor, and DataReconciliationEngine.
When should I use Neqsim Advanced Control Mpc And Virtual Sensing?
Neqsim Advanced Control Mpc And Virtual Sensing fits situations like: : identifying a process model for MPC; exporting state-space; industrial controller models; estimating unmetered flow.
How do I install Neqsim Advanced Control Mpc And Virtual Sensing in Claude Code?
Run `npx skills add equinor/neqsim --skill neqsim-advanced-control-mpc-and-virtual-sensing -a claude-code`. Or copy the skill folder (.github/skills/neqsim-advanced-control-mpc-and-virtual-sensing in equinor/neqsim) into .claude/skills/neqsim-advanced-control-mpc-and-virtual-sensing in your project. Claude Code loads it when a task matches its description.
How do I install Neqsim Advanced Control Mpc And Virtual Sensing in Codex?
Run `npx skills add equinor/neqsim --skill neqsim-advanced-control-mpc-and-virtual-sensing -a codex`. Or copy the skill folder (.github/skills/neqsim-advanced-control-mpc-and-virtual-sensing in equinor/neqsim) into .agents/skills/neqsim-advanced-control-mpc-and-virtual-sensing in your project. Codex loads it when a task matches its description.
Can I use Neqsim Advanced Control Mpc And Virtual Sensing in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add equinor/neqsim --skill neqsim-advanced-control-mpc-and-virtual-sensing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/neqsim-advanced-control-mpc-and-virtual-sensing, .gemini/skills/neqsim-advanced-control-mpc-and-virtual-sensing, .github/skills/neqsim-advanced-control-mpc-and-virtual-sensing and .opencode/skills/neqsim-advanced-control-mpc-and-virtual-sensing in your project.
What does Neqsim Advanced Control Mpc And Virtual Sensing need to run?
SKILL.md names no scripts, command-line tools or credentials: Neqsim Advanced Control Mpc And Virtual Sensing is instructions for the agent only. Our summary lists: Python 3.
Does Neqsim Advanced Control Mpc And Virtual Sensing access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Neqsim Advanced Control Mpc And Virtual Sensing safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Neqsim Advanced Control Mpc And Virtual Sensing use?
Neqsim Advanced Control Mpc And Virtual Sensing is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Neqsim Advanced Control Mpc And Virtual Sensing use?
About 3.7k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Neqsim Advanced Control Mpc And Virtual Sensing?
Skills that share tags, products or a category with Neqsim Advanced Control Mpc And Virtual Sensing: Technical Analyst (tradermonty/claude-trading-skills, 3k stars), Theme Detector (tradermonty/claude-trading-skills, 3k stars), Creating Financial Models (Chen-zexi/open-ptc-agent, 729 stars) and Stock API (zhangxiangliang/stock-api, 2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Neqsim Advanced Control Mpc And Virtual Sensing?
equinor (a GitHub organization) maintains it in equinor/neqsim, which has 156 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on October 11, 2026.
Source: equinor/neqsim on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.