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neqsim-resource-classification-screening

neqsim-resource-classification-screening is a skill for Claude Code, Codex from equinor/neqsim-community-skills. It costs 83 tokens per session (1,569 once invoked), scanned A, original, Apache-2.0.

An educational tool for classifying petroleum resources using Norwegian Offshore Directorate maturity classes and separate SPE-PRMS resource categories. It keeps project maturity distinct from uncertainty labels such as 1P/2P/3P and 1C/2C/3C.

In plain words
What is it for?
It helps label projects from production and approved development through prospects, leads, and plays, and explain SODIR classes such as RC0–RC9, including F and A markers.
Why use it?
It prevents different classification systems from being mixed together or treated as measures of the same thing. It supports screening with public information rather than confidential volumes or reservoir data.

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 label projects from production and approved development through prospects, leads, and plays, and explain SODIR classes such as RC0–RC9, including F and A markers.

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

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/equinor/neqsim-community-skills/neqsim-resource-classification-screening"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/neqsim-resource-classification-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 1,569 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 $0.01569
Opus 5.5 $0.00033 $0.00628
Sonnet 5.5 $0.00017 $0.00314
Haiku 4.5 $0.00008 $0.00157

Measured 1mo ago against content hash 040cf35af6ea, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

neqsim-resource-classification-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_resource_classification_screening.py, src/resource_classification_screening/__init__.py, src/resource_classification_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-resource-classification-screening/SKILL.md · 116 lines

How it starts

The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Resource Classification Screening

Use this skill for public, educational petroleum resource classification screening. It maps project maturity to the public Norwegian Offshore Directorate (SODIR) resource classes and separately reports the corresponding SPE-PRMS category. It does not infer quantity uncertainty from project maturity.

When to Use

  • When a user asks which resource category a project sits in.
  • When an agent needs to distinguish SODIR RC0-RC9 from a PRMS category.
  • When a user asks what F and A mean in a SODIR resource class.
  • When a report must keep maturity separate from 1P/2P/3P, 1C/2C/3C, or low/best/high estimates.
  • When examples must run without confidential volumes, reservoir data, or company estimates.

Inputs

  • maturity_stage: an explicit SODIR class (RC0, RC4A, and so on) or a supported public maturity descriptor such as on production, approved for development, development pending, recovery unlikely, prospect, lead, or play.
  • commercial: optional flag indicating whether the project has been judged commercial.

Outputs

  • resource_class: a normalized maturity-stage label.
  • resource_category: compatibility category used by existing callers.
  • sodir_resource_class: RC0-RC9, including F/A where applicable, or unclassified when evidence is insufficient.
  • sodir_resource_category: historical-production, reserves, contingent-resources, or undiscovered-resources.
  • prms_category: reserves, contingent resources, prospective resources, or not applicable for historical production.
  • prms_class_range: compatibility alias for prms_category; it is not a numbered PRMS uncertainty range.
  • uncertainty_basis: reminder that uncertainty must be reported independently.
  • maturity_warning: ok, watch, sodir-class-needs-evidence, or unclassified.
  • assumptions: public assumptions used by the placeholder model.

Engineering Method

The Python class ResourceClassificationModel keeps three concepts separate:

Read the full file on GitHub · 116 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 · 116 lines · 83 tokens per session scan A 040cf35af6ea

Subscribe to this mod's changes

neqsim-resource-classification-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 1,569 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-resource-classification-screening compared with skills from other repositories

neqsim-resource-classification-screening next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
neqsim-resource-classification-screeningequinor/neqsim-community-skillsSkill44d agoApache-2.0A83Claude Code, Codex
instrument-data-to-allotropeanthropics/knowledge-work-pluginsSkill35k3d agoApache-2.0A123Claude Code
exploratory-data-analysisK-Dense-AI/scientific-agent-skillsSkill56k4d agoMITA83Claude Code
matlabK-Dense-AI/scientific-agent-skillsSkill56k4d agoMITA44Claude Code
phylogeneticsK-Dense-AI/scientific-agent-skillsSkill56k4d agoMITA71Claude Code, Codex
research-engineerdavila7/claude-code-templatesSkill33kyesterdayMITA43Claude Code, Codex
mapping-to-snomedmaziyarpanahi/openmedSkill5.8kyesterdayApache-2.0A205Claude Code