neqsim-wax-margin-check

neqsim-wax-margin-check is a skill for Claude Code, Codex from equinor/neqsim-community-skills. It costs 55 tokens per session (1,042 once invoked), scanned A, original, Apache-2.0.

An educational calculator for checking whether a process or pipeline temperature stays above the wax appearance temperature, the point where wax may begin to form. It compares the operating temperature with a required safety margin.

In plain words
What is it for?
Calculating the temperature margin, detecting when an operating point is at or below the wax appearance temperature, and flagging the result as acceptable, watch, or high risk.
Why use it?
It offers a quick public-data screening check before detailed wax prediction and deposition design with validated NeqSim methods.

Skill for Claude CodeCodex

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

Good fit Calculating the temperature margin, detecting when an operating point is at or below the wax appearance temperature, and flagging the result as acceptable, watch, or high risk.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/equinor/neqsim-community-skills/wax-margin-check
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 wax-margin-check
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-wax-margin-check

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/equinor/neqsim-community-skills/wax-margin-check"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/wax-margin-check.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 55 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,042 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 $0.00055 $0.01042
Opus 5 $0.00028 $0.00521
Sonnet 5 $0.00011 $0.00208
Haiku 4.5 $0.00006 $0.00104

Measured 12d ago against content hash dce4b5171eff, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

neqsim-wax-margin-check 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_wax_margin_check.py, src/wax_margin_check/__init__.py, src/wax_margin_check/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/flow-assurance/wax-margin-check/SKILL.md · 100 lines

How it starts

The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Wax Margin Check

Use this skill for a quick, public check of whether a process or pipeline operating point keeps a temperature margin above a known wax appearance temperature (WAT). It is intentionally simple and should guide users toward validated NeqSim wax calculations for real work.

When to Use

  • When a user asks whether an operating temperature stays clear of the wax deposition region.
  • When a validated NeqSim wax appearance temperature or a public lab WAT is already available.
  • When an agent should explain that validated NeqSim methods are required for real wax prediction and deposition design.

Inputs

  • operating_temperature: operating temperature in C.
  • wax_appearance_temperature: wax appearance temperature in C from a validated NeqSim calculation or public lab measurement.
  • min_margin: configurable minimum acceptable margin in C (constructor, default 5.0).

Outputs

  • wax_margin_c: operating temperature minus the wax appearance temperature in C.
  • below_wax_appearance: whether the operating point is at or below the wax appearance temperature.
  • margin_warning: ok, watch, or high.
  • neqsim_available: whether the optional NeqSim package is importable.
  • assumptions: public assumptions and required follow-up.

Engineering Method

The placeholder method computes the wax margin as operating_temperature - wax_appearance_temperature. A non-positive margin means the operating point is at or below the wax appearance temperature and is flagged high. A small positive margin below min_margin is flagged watch. A larger margin is flagged ok.

This is not a wax thermodynamic or deposition model. The wax appearance temperature must come from a validated NeqSim wax calculation or a public lab measurement with a defined fluid composition and operating envelope.

Python Usage Pattern

from wax_margin_check import WaxMarginModel

model = WaxMarginModel(min_margin=5.0)
result = model.evaluate(
    operating_temperature=33.0,
    wax_appearance_temperature=30.0,
)

print(result.wax_margin_c)
print(result.margin_warning)
print(result.assumptions)

Read the full file on GitHub · 100 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 · 100 lines · 55 tokens per session scan A dce4b5171eff

Subscribe to this mod's changes

neqsim-wax-margin-check is a skill published in the GitHub repository equinor/neqsim-community-skills (2 stars, last pushed yesterday), licensed Apache-2.0. It adds 55 tokens to every session and 1,042 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.

Related

Other skills, from other repositories

instrument-data-to-allotrope

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…

anthropics/knowledge-work-plugins · 123 tokens

matlab

Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.

K-Dense-AI/scientific-agent-skills · 42 tokens

exploratory-data-analysis

Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…

K-Dense-AI/scientific-agent-skills · 83 tokens

phylogenetics

Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.

K-Dense-AI/scientific-agent-skills · 68 tokens

research-engineer

An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.

davila7/claude-code-templates · 43 tokens

mapping-to-snomed

Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…

maziyarpanahi/openmed · 205 tokens