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-production-system-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-production-system-modeling)<a href="https://agentmods.dev/skills/equinor/neqsim/neqsim-production-system-modeling"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/neqsim-production-system-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-production-system-modeling"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/neqsim-production-system-modeling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00082 | $0.01537 |
| Opus 5.5 | $0.00033 | $0.00615 |
| Sonnet 5.5 | $0.00016 | $0.00307 |
| Haiku 4.5 | $0.00008 | $0.00154 |
Grade A, and why
neqsim-production-system-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 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Natural-language production-system modeling
Translate the requested system into one explicit topology, physics basis and executable model in the user's chosen language. Reuse NeqSim Java models through the Python bridge or directly from Java; do not create a second Python physics implementation. Load references/chain-contract.md for stage-specific inputs, APIs, specialist routes and evidence.
1. Translate the request into a model contract
Record the objective, requested Python/Java deliverable, steady/transient/time horizon, production and injection paths, topology and fidelity at each stage. Extract fluids, well count, routes, geometry, elevations, thermal environment, facility/utility capacities, export/product requirements and controls. Distinguish user data, measured/calibrated values, synthetic assumptions and unresolved gaps. Ask only for missing decision-critical inputs; use labeled synthetic assumptions when the request permits a demonstration. Do not invent plant or reservoir facts.
Represent every requested stage and interface in the plan. Keep the reservoir, wells, SURF, flowlines, risers, process, utilities and downstream sections visible, including stages deliberately outside the requested boundary. A stream chain with fixed rates does not establish a reservoir-constrained production solution.
2. Discover before building
Use capability_search.py in a source checkout and runtime MCP search/inspection
for the deployed artifact. Use agent_search.py and skill_search.py to discover
installed specialists across core, community, enterprise and plugins. Verify
exact constructors, public methods, setup order, units and test assertions.
Follow the core discovery guide docs/development/AGENT_CAPABILITY_DISCOVERY.md.
Choose model fidelity per stage. SimpleReservoir, tank/material-balance drives
and decline curves are explicit simplifications. Use the installed reservoir
specialist and an external reservoir engine such as OPM Flow when spatial
reservoir simulation is needed; keep its deck, PVT/VFP interface and provenance.
Do not substitute a tank for a requested 3D model without making that gap explicit.
Use Beggs & Brill for an applicable steady correlation model, and assess the
current TwoFluidPipe qualification for transient/slugging requirements. Neither
an agent name nor source presence qualifies all requested regimes.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 Changed · +1 lines 6bdb7f07ef1b
- 4d ago First seen · 123 lines · 82 tokens per session scan A 36daa54e0444
neqsim-production-system-modeling is a skill published in the GitHub repository equinor/neqsim (160 stars, last pushed today), licensed Apache-2.0. It adds 82 tokens to every session and 1,537 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 2026-10-25.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…