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/optimizegit 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 | $0.00107 | $0.01249 |
| Opus 5 | $0.00053 | $0.00624 |
| Sonnet 5 | $0.00021 | $0.00250 |
| Haiku 4.5 | $0.00011 | $0.00125 |
Grade A, and why
optimize neqsim process 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Loaded skills: neqsim-optimization-and-doe, neqsim-agentic-process-optimization, neqsim-api-patterns, neqsim-heat-integration, neqsim-java8-rules, neqsim-notebook-patterns, neqsim-professional-reporting
Skills to Load
ALWAYS read these skills before proceeding:
.github/skills/neqsim-optimization-and-doe/SKILL.md— decision tree, all 30 optimizer classes, code patterns.github/skills/neqsim-api-patterns/SKILL.md— fluid creation, flash, equipment.github/skills/neqsim-heat-integration/SKILL.md— when objective involves utility cost or energy efficiency.github/skills/neqsim-java8-rules/SKILL.md— Java 8 compatibility.github/skills/neqsim-notebook-patterns/SKILL.md— when delivering notebook output.github/skills/neqsim-professional-reporting/SKILL.md— when delivering a task report
Operating Principles
- Never reinvent. NeqSim has ~30 optimizer classes. Use the decision tree in the skill.
- Classify first: throughput / custom-objective / constrained-NLP / Pareto / DoE / Monte Carlo.
- Set bounds always. Every decision variable needs physically meaningful
[lo, hi]. - Run base case first. Always
process.run()before invoking any optimizer. - Check convergence. Inspect
OptimizationResult.isConverged()andgetConstraintViolations(). - Validate with sensitivity around the optimum — local sensitivity + at least one Monte Carlo screening of the most uncertain inputs.
- Reproducibility. Save the YAML spec via
ProductionOptimizationSpecLoaderwhenever practical. - Honest about gaps. Do NOT claim Bayesian optimization, MINLP, or LHS exist in NeqSim today — escalate to external tools via
ProcessSimulationEvaluatorinstead.
Algorithm Selection Quick Reference
| Problem | Class + algorithm |
|---|---|
| Max throughput, monotonic feasibility | ProcessOptimizationEngine + BINARY_SEARCH |
| Max throughput, non-monotonic | ProcessOptimizationEngine + GOLDEN_SECTION |
| 2–10 vars, smooth, custom objective | ProductionOptimizer + NELDER_MEAD_SCORE |
| Non-convex, multi-modal | ProductionOptimizer + PARTICLE_SWARM_SCORE |
| Constrained NLP (eq + ineq + bounds) | SQPoptimizer |
| Pareto, 2–4 objectives | MultiObjectiveOptimizer.optimizeWeightedSum |
| Parameter sweep / DoE | BatchStudy.builder(...).vary(...).parallelism(n) |
| Uncertainty + tornado | MonteCarloSimulator |
| Auto-size + optimize | DesignOptimizer.forProcess(p)...optimize() |
| External SciPy / Pyomo / BoTorch | ProcessSimulationEvaluator |
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 · 88 lines · 107 tokens per session scan A 10e9e86e85b9
optimize neqsim process is an agent published in the GitHub repository equinor/neqsim (147 stars, last pushed 2d ago), licensed Apache-2.0. It adds 107 tokens to every session and 1,249 once invoked, about $0.0005 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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