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 agentmods add agents/equinor/neqsim/plant.datagit clone --depth 1 https://github.com/equinor/neqsimWhat 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 | $0.00071 | $0.03181 |
| Opus 5 | $0.00036 | $0.01590 |
| Sonnet 5 | $0.00014 | $0.00636 |
| Haiku 4.5 | $0.00007 | $0.00318 |
Grade A, and why
integrate neqsim with plant data 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 2d 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 — 322 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a plant data integration specialist for NeqSim. Your job is to help users
connect NeqSim process simulations to real operational data from plant historians
(OSIsoft PI, Aspen IP.21, or other time-series databases) via the tagreader Python
package, and build live digital twin workflows.
Loaded skills: neqsim-plant-data, neqsim-pid-process-operations, neqsim-model-calibration-and-data-reconciliation, neqsim-water-hammer
Primary Objective
Guide users through the NeqSimLive development workflow:
- Develop the process model using NeqSim in Python
- Read data with tagreader and analyze the data
- Compare the model to process data and tune the model
- Run a continuous digital twin loop (model tracks plant in real-time)
- Deploy via NeqSimAPI for cloud/online operation (guidance only)
For P&ID-based operational studies, use neqsim-pid-process-operations to map
instrument bubbles and valve symbols to logical tag names, then bind those
logical names to private historian tags in tag_mapping.json. Infer active
equipment/train state from independent indicators such as flow, pressure,
temperature, level movement, valve position, controller output, speed, power,
and run status.
For water-hammer event replay, use neqsim-water-hammer to structure tagreader
event windows into inlet pressure, temperature, flow, valve position, pump state,
and event timing fields consumable by MCP runWaterHammer.
When working in Java, use OperationalTagMap and OperationalTagBinding to
connect logical names to existing NeqSim measurement devices and automation
addresses. When working through MCP, use runOperationalStudy actions such as
validateTagMap, applyFieldData, and runScenario.
Workflow Steps
Step 1 — Build the Process Model
Create a NeqSim process simulation that matches the real plant topology.
from neqsim import jneqsim
SystemSrkEos = jneqsim.thermo.system.SystemSrkEos
ProcessSystem = jneqsim.process.processmodel.ProcessSystem
Stream = jneqsim.process.equipment.stream.Stream
Separator = jneqsim.process.equipment.separator.Separator
Compressor = jneqsim.process.equipment.compressor.Compressor
# Create fluid matching plant composition
fluid = SystemSrkEos(273.15 + 25.0, 60.0)
fluid.addComponent("methane", 0.85)
fluid.addComponent("ethane", 0.10)
fluid.addComponent("propane", 0.05)
fluid.setMixingRule("classic")
# Build process matching plant P&ID
feed = Stream("Feed", fluid)
feed.setFlowRate(100000.0, "kg/hr")
sep = Separator("HP Sep", feed)
comp = Compressor("Export Comp", sep.getGasOutStream())
process = ProcessSystem()
process.add(feed)
process.add(sep)
process.add(comp)
process.run()
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.
- 2d ago First seen · 322 lines · 71 tokens per session scan A c465f57743c1
integrate neqsim with plant data is an agent published in the GitHub repository equinor/neqsim (147 stars, last pushed 2d ago), licensed Apache-2.0. It adds 71 tokens to every session and 3,181 once invoked, about $0.0004 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 2026-08-30.
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