neqsim: Skill for Claude Code

.github/skills/neqsim-capacity-increase-screening/SKILL.md

neqsim-capacity-increase-screening is a skill for Claude Code, Codex from equinor/neqsim. It costs 108 tokens per session (3,897 once invoked), scanned A, original, Apache-2.0.

A screening workflow for ideas that could increase production from an existing oil and gas facility. It uses a calibrated model to map equipment limits, test possible changes, and rank ideas by which constraints they relieve.

In plain words
What is it for?
Use it to assess changes to wells, subsea systems, separators, compressors, pumps, and other production equipment, then create an idea register for further study.
Why use it?
It shows whether a proposed change actually increases output or only moves the bottleneck elsewhere. Comparing the model with plant data helps avoid decisions based on an inaccurate starting point.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

This is equinor/neqsim's own configuration. It tells Claude Code and Codex how to work on neqsim itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything neqsim configures →

Reuse

Borrowing it

Nothing to install: this file belongs to equinor/neqsim. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/equinor/neqsim/master/.github/skills/neqsim-capacity-increase-screening/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/equinor/neqsim

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for neqsim-capacity-increase-screening

README.md
[![agentmods](https://agentmods.dev/badge/skills/equinor/neqsim/neqsim-capacity-increase-screening/github.svg)](https://agentmods.dev/skills/equinor/neqsim/neqsim-capacity-increase-screening)
Your own site
<a href="https://agentmods.dev/skills/equinor/neqsim/neqsim-capacity-increase-screening"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/neqsim-capacity-increase-screening/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.

agentmods 80×15 button for neqsim-capacity-increase-screening

Your own site · 80×15
<a href="https://agentmods.dev/skills/equinor/neqsim/neqsim-capacity-increase-screening"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/neqsim-capacity-increase-screening.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 108 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,897 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00108 $0.03897
Opus 5.5 $0.00043 $0.01559
Sonnet 5.5 $0.00022 $0.00779
Haiku 4.5 $0.00011 $0.00390

Measured 7d ago against content hash 227b33ff2850, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

neqsim-capacity-increase-screening 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 7d 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.

.github/skills/neqsim-capacity-increase-screening/SKILL.md · 128 lines

How it starts

The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Capacity Increase Screening (wells, subsea, topside)

Use this skill when the question is "which ideas lift or accelerate production on this host, and which should be matured?" It turns a calibrated host model into a ranked idea register. It does not replace the equipment-specific skills it calls.

Workflow

  1. Baseline. Build or reuse the host model on a plan profile (oil, gas, water per year). Close mass balance and compare with plant data before any lever is tested (neqsim-process-modeling, neqsim-model-calibration-and-data-reconciliation).
  2. Constraint map. Register the limit of every unit on the oil, gas, water and power paths and label its basis (vendor design case, code, operator monitoring limit, demonstrated peak). Run the year grid and record utilisation per constraint, not only the maximum (neqsim-capacity-and-utilization-analysis; ProcessModelDebottleneckStudy, BottleneckTracker).
  3. Binding sequence. List which constraint binds in which years and what the next one is. A lever that relieves constraint A only buys time until constraint B binds.
  4. Idea long list. Fill the lever catalogue below with the asset's own ideas first, then add missing families. Ask the asset for design margins, planned shutdowns and earlier studies before ranking.
  5. Lever runs. Express each idea as a change to the model or its feed, rerun the year grid, and record the new binding sequence and the volume or time gained.
  6. Register and score. One row per idea with scope, cost class, emissions, HSE, value and maturity (schema below). Test combinations, since levers rarely add.
  7. Maturation pipeline and host ullage. Assign each idea a next stage and report the per-year ullage left on every constraint for future tie-ins (TieInCapacityPlanner, HostFacility).

Lever catalogue

Domain Idea Constraint it relieves How to model it Main risk
Wells AICD, AICV, ICD, DAR-type autonomous or adjustable completions Water and gas handling, liquid rate InflowControlCompletion.compareAtSameOil gives the water and gas change per barrel of oil and the extra drawdown; carry the result into WellDeliverabilityCurve / IntegratedProductionModel Benefit is reservoir-dependent and unproven until a well test; productivity penalty
Wells Gas lift rate, lift-gas allocation, choke strategy Liquid rate, gas compression GasLiftNetworkOptimizer, NetworkOptimizer Lift gas loads the same compressors
Wells New or sidetracked wells, infill Plateau length Plan profile with new wells; not a debottleneck Cost class and rig availability
Subsea Boosting (multiphase pump, wet-gas compression) Wellhead back-pressure, plateau rate SubseaBooster plus flowline hydraulics (PipeBeggsAndBrills, TwoFluidPipe) Power supply, reliability, intervention cost
Subsea Bulk water or gas separation Topside water, liquid or gas path SubseaSeparationStation: split gas, liquid and water before the riser, boost liquid and reinject water, read the power Qualification, power, controls, hydrate and slug control
Subsea Flowline or riser pressure-drop reduction, slug control Back-pressure, separator surge Hydraulic rerun with changed diameter, routing or choking (neqsim-flow-assurance) Hydrate, wax and slugging margin
Topside Higher separation pressure Gas compression suction, scrubber load Pressure setpoint on the inlet separator; recompression power falls Oil spec, flashing, vessel rating
Topside Scrubber internals, cyclones, vane packs Gas load factor (K) and momentum limits New K limit on the unit; check carry-over (SeparatorMechanicalDesign) Vessel pressure rating, liquid carry-over
Topside Compressor re-wheel, speed, driver, anti-surge strategy Compressor flow, power, surge New chart or driver limit (neqsim-rotating-equipment-design, neqsim-compressor-antisurge-recycle) Casing limits, power from the driver or grid
Topside Pump upgrade (water injection, export), heater or cooler capacity Water, liquid or temperature path Capacity constraint on the unit Utility demand
Topside Produced-water treatment, flare, power capacity Water discharge, flare load, electrical load Separate screen against demonstrated peak and design case Permit limits
System Debottleneck sequencing across several of the above The next binding constraint DebottleneckingAdvisor, ProcessModelDebottleneckRanking Interactions

Read the full file on GitHub · 128 lines

Changes

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.

  1. 7d ago Changed · +11 lines 227b33ff2850
  2. 9d ago First seen · 117 lines · 108 tokens per session scan A d5d49b2f9e35

Subscribe to this mod's changes

neqsim-capacity-increase-screening is a skill published in the GitHub repository equinor/neqsim (156 stars, last pushed today), licensed Apache-2.0. It adds 108 tokens to every session and 3,897 once invoked, about $0.0004 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 2026-10-09.

Related

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…

microsoft/ai-agents-for-beginners · 200 tokens

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

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…

vercel/next.js · 95 tokens

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.

microsoft/vscode · 53 tokens

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…

microsoft/vscode · 71 tokens

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…

vercel/next.js · 83 tokens