neqsim-economy-basis-screening

neqsim-economy-basis-screening is a skill for Claude Code, Codex from equinor/neqsim-community-skills. It costs 70 tokens per session (1,146 once invoked), scanned A, original, Apache-2.0.

A screening worksheet for assembling and checking the economic assumptions used in an early asset-value estimate. It covers oil and gas prices, discount rate, currency, inflation, real or nominal terms, and tax regime.

In plain words
What is it for?
Use it to prepare a documented assumptions basis for preliminary NPV screening or as input to detailed NeqSim field-economics modelling.
Why use it?
It catches missing or unusual assumptions before they are used in a net present value estimate, which compares future cash flows in today’s money.

Skill for Claude CodeCodex

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

Good fit Use it to prepare a documented assumptions basis for preliminary NPV screening or as input to detailed NeqSim field-economics modelling.

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Install with agentmods
npx agentmods add skills/equinor/neqsim-community-skills/economy-basis-screening
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 economy-basis-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-economy-basis-screening

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/equinor/neqsim-community-skills/economy-basis-screening"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/economy-basis-screening.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 70 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,146 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 $0.00070 $0.01146
Opus 5 $0.00035 $0.00573
Sonnet 5 $0.00014 $0.00229
Haiku 4.5 $0.00007 $0.00115

Measured 12d ago against content hash ab36c9e8974b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

neqsim-economy-basis-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 12d ago.

The scan reads SKILL.md. This mod also ships 4 executable files (examples/basic_economy_basis.py, src/economy_basis_screening/__init__.py, src/economy_basis_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/economy-basis-screening/SKILL.md · 123 lines

How it starts

The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Economy Basis Screening

Use this skill to assemble and sanity-check the economic assumptions that feed a downstream asset-value (NPV) screening: gas and oil prices, discount rate, currency, inflation, and tax regime. It echoes a normalized basis, range-checks the discount rate against an indicative public band, checks the tax regime against a small public name set, and raises consistency flags. It is intentionally simple and should guide users toward the validated NeqSim field-economics workflow and the NeqSim MCP runFieldEconomics tool.

When to Use

  • When a user needs a clean, documented economic-assumptions basis before NPV work.
  • When an early concept needs a discount-rate and tax-regime sanity check.
  • When an agent needs an upstream "economy basis" feed for an asset-value screening chain.

Inputs

  • gas_price_per_sm3: gas price in the chosen currency per Sm3.
  • oil_price_per_bbl: oil price in the chosen currency per barrel.
  • discount_rate: real or nominal discount rate (fraction, 0-1).
  • currency: currency code (default USD).
  • inflation_rate: inflation rate (fraction, default 0).
  • real_terms: whether the basis is in real terms (default True).
  • tax_regime: tax-regime label (generic, norwegian-ncs, uk, us, none).

Outputs

  • currency, gas_price_per_sm3, oil_price_per_bbl, discount_rate, real_terms, inflation_rate, tax_regime.
  • discount_rate_flag: ok, low, or high vs the indicative band.
  • tax_regime_recognized: whether the regime is in the public name set.
  • basis_warning: ok or watch.
  • flags: human-readable consistency warnings.
  • neqsim_available: whether the optional NeqSim package is importable.
  • assumptions: public assumptions and required follow-up.

Engineering Method

The skill normalizes the supplied assumptions and applies transparent checks: the discount rate is compared against an indicative public band (default 6%-12%); the tax regime is checked against a small public name set; and consistency flags are raised for zero-revenue, or nominal terms with zero inflation. The verdict is watch when any flag is raised, otherwise ok.

Read the full file on GitHub · 123 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. 12d ago First seen · 123 lines · 70 tokens per session scan A ab36c9e8974b

Subscribe to this mod's changes

neqsim-economy-basis-screening is a skill published in the GitHub repository equinor/neqsim-community-skills (2 stars, last pushed yesterday), licensed Apache-2.0. It adds 70 tokens to every session and 1,146 once invoked, about $0.0003 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 2026-08-31.

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