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neqsim-artificial-lift-screening

neqsim-artificial-lift-screening is a skill for Claude Code, Codex from equinor/neqsim-community-skills. It costs 83 tokens per session (1,473 once invoked), scanned A, original, Apache-2.0.

A screening tool for deciding whether a well may produce naturally or may need artificial lift. Artificial lift uses equipment such as gas injection or an electric submersible pump to help bring fluids to the surface.

In plain words
What is it for?
It helps estimate natural deliverability, check the required bottomhole-pressure reduction, and screen gas-lift and ESP feasibility.
Why use it?
It provides an early comparison with a target production rate before detailed well-flow analysis, vendor pump curves, or proprietary models are available. It can also show when the reservoir may not deliver the target even with substantial pressure reduction.

Skill for Claude CodeCodex

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

not rated 4repo 4d ago A scan 83 tokens original Apache-2.0

Good fit It helps estimate natural deliverability, check the required bottomhole-pressure reduction, and screen gas-lift and ESP feasibility.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/equinor/neqsim-community-skills/neqsim-artificial-lift-screening
View source ↗ equinor/neqsim-community-skills equinor.github.io
Install

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.

Any agent
npx skills add equinor/neqsim-community-skills --skill neqsim-artificial-lift-screening
Clone the repo
git clone --depth 1 https://github.com/equinor/neqsim-community-skills

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-artificial-lift-screening

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/equinor/neqsim-community-skills/neqsim-artificial-lift-screening"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/neqsim-artificial-lift-screening.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 83 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,473 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 $0.00083 $0.01473
Opus 5.5 $0.00033 $0.00589
Sonnet 5.5 $0.00017 $0.00295
Haiku 4.5 $0.00008 $0.00147

Measured 1mo ago against content hash a4ab6b927b02, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

neqsim-artificial-lift-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 1mo ago.

The scan reads SKILL.md. This mod also ships 4 executable files (examples/basic_artificial_lift_screening.py, src/artificial_lift_screening/__init__.py, src/artificial_lift_screening/model.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/field-development/neqsim-artificial-lift-screening/SKILL.md · 120 lines

How it starts

The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Artificial Lift Screening

Use this skill for public, educational artificial-lift triage. It estimates natural deliverability from a straight-line inflow performance relationship (IPR), checks it against a target rate, and screens gas lift and electric submersible pump (ESP) feasibility from the bottomhole-pressure reduction required to meet the target.

When to Use

  • When a user asks whether a well can flow naturally or needs artificial lift to hit a target rate.
  • When an agent needs a quick gas-lift vs ESP recommendation before nodal analysis.
  • When the reservoir may be unable to deliver the target even at zero bottomhole pressure.
  • When examples must run without proprietary well models, vendor pump curves, or field data.

Inputs

  • reservoir_pressure_bar: average reservoir pressure in bar.
  • bottomhole_flowing_pressure_bar: current flowing bottomhole pressure in bar.
  • productivity_index_sm3_d_bar: straight-line productivity index in Sm3/d per bar drawdown.
  • target_rate_sm3_d: required production rate in Sm3/d.
  • water_cut: produced water fraction in [0, 1), default 0.0.
  • gas_lift_available: whether gas lift injection is available, default True.
  • max_injection_gas_sm3_d: optional available lift-gas injection rate in Sm3/d.
  • esp_max_head_m: optional maximum ESP head in metres.
  • fluid_gradient_bar_per_m: hydrocarbon fluid gradient in bar/m, default 0.09.
  • well_depth_m: producing depth in metres, default 2000.

Outputs

  • natural_rate_sm3_d: deliverability from the straight-line IPR at the current bottomhole pressure.
  • required_pwf_bar: bottomhole pressure required to deliver the target rate.
  • required_pressure_reduction_bar: bottomhole-pressure reduction needed beyond the current flowing pressure.
  • esp_required_head_m: ESP head needed for the pressure reduction, or null.
  • gas_lift_feasible: True/False gas-lift feasibility flag.
  • esp_feasible: True/False ESP feasibility flag.
  • recommended_method: natural-flow, gas-lift, esp, or infeasible.
  • warning: same value as recommended_method for quick triage.
  • assumptions: public assumptions used by the placeholder model.

Read the full file on GitHub · 120 lines

Files

What ships with it

6 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.

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. 1mo ago First seen · 120 lines · 83 tokens per session scan A a4ab6b927b02

Subscribe to this mod's changes

neqsim-artificial-lift-screening is a skill published in the GitHub repository equinor/neqsim-community-skills (4 stars, last pushed 4d ago), licensed Apache-2.0. It adds 83 tokens to every session and 1,473 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 2026-09-19.

Scientific
Related

neqsim-artificial-lift-screening compared with skills from other repositories

neqsim-artificial-lift-screening next to 6 skills from other repositories
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