neqsim-energy-emissions-screening

neqsim-energy-emissions-screening is a skill for Claude Code, Codex from equinor/neqsim-community-skills. It costs 87 tokens per session (1,305 once invoked), scanned A, original, Apache-2.0.

A screening tool that adds up yearly energy use for an oil or gas field and estimates its carbon dioxide-equivalent emissions over the field's lifetime. It can also calculate emissions per barrel of oil equivalent and an optional carbon-tax cost.

In plain words
What is it for?
Use it to compare field concepts, report annual and lifetime emissions, calculate carbon intensity, or provide emissions and tax-cost estimates to an asset-value model.
Why use it?
It gives an early, public-data estimate before detailed emissions studies, without needing confidential or certified data.

Skill for Claude CodeCodex

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

Good fit Use it to compare field concepts, report annual and lifetime emissions, calculate carbon intensity, or provide emissions and tax-cost estimates to an asset-value model.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/equinor/neqsim-community-skills/energy-emissions-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 energy-emissions-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-energy-emissions-screening

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/equinor/neqsim-community-skills/energy-emissions-screening"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/energy-emissions-screening.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 87 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,305 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 $0.00087 $0.01305
Opus 5 $0.00044 $0.00652
Sonnet 5 $0.00017 $0.00261
Haiku 4.5 $0.00009 $0.00130

Measured 12d ago against content hash fb99f7cd46b8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

neqsim-energy-emissions-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_energy_emissions.py, src/energy_emissions_screening/__init__.py, src/energy_emissions_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/environment/energy-emissions-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.

Energy & Emissions Screening

Use this skill for a quick, public roll-up of field-life energy use into emissions. Given a year-by-year energy use (MWh), a public emission factor, and an optional production profile, it returns annual and total CO2-equivalent emissions, a carbon intensity in kg CO2e per barrel of oil equivalent, and an optional CO2-tax cost. It complements the instantaneous combustion skill neqsim-co2-emissions-screening by aggregating over the field life and adding carbon intensity and cost.

When to Use

  • When a user wants total and per-year CO2e and a carbon intensity for a concept.
  • When an agent needs an emissions/cost feed for an asset-value chain.
  • When examples must run without certified emission factors or confidential data.

Inputs

  • annual_energy_use_mwh: sequence of annual energy use in MWh (one per year).
  • emission_factor_kg_co2e_per_mwh: public emission factor (default 450).
  • annual_production_boe: a flat value or a per-year sequence in boe (default 0).
  • co2_tax_usd_per_tonne: optional CO2 tax (default 0).

Outputs

  • annual_co2e_tonnes: per-year CO2-equivalent emissions in tonnes.
  • total_co2e_tonnes: total field-life CO2e in tonnes.
  • total_energy_use_mwh: total field-life energy use.
  • carbon_intensity_kg_per_boe: total CO2e (kg) divided by total production (boe), or None.
  • annual_emission_cost_musd / total_emission_cost_musd: CO2-tax cost in million USD.
  • intensity_warning: ok, watch, high, or no-production.
  • neqsim_available: whether the optional NeqSim package is importable.
  • assumptions: public assumptions and required follow-up.

Engineering Method

Annual CO2e is energy_use_MWh * emission_factor_kg/MWh / 1000 (kg to tonnes). Total CO2e and total energy are the year sums. Carbon intensity is total_CO2e_kg / total_production_boe (undefined when production is zero). The CO2-tax cost is annual_CO2e_tonnes * tax_USD/tonne / 1e6 (million USD). The verdict compares carbon intensity to a configurable threshold (default 17 kg CO2e/boe, a public global-average order of magnitude).

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 · 87 tokens per session scan A fb99f7cd46b8

Subscribe to this mod's changes

neqsim-energy-emissions-screening is a skill published in the GitHub repository equinor/neqsim-community-skills (2 stars, last pushed yesterday), licensed Apache-2.0. It adds 87 tokens to every session and 1,305 once invoked, about $0.0004 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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