neqsim-dry-gas-seal-screening

neqsim-dry-gas-seal-screening is a skill for Claude Code, Codex from equinor/neqsim-community-skills. It costs 75 tokens per session (1,411 once invoked), scanned A, original, Apache-2.0.

A preliminary check of dry-gas seal supply and condensation risk on a centrifugal compressor. A dry-gas seal uses clean gas to prevent process gas from escaping around the rotating shaft.

In plain words
What is it for?
Estimating seal-gas and separation-gas demand and comparing seal-cavity temperature with the hydrocarbon dew point to flag condensation risk.
Why use it?
It indicates whether seal-gas supply has enough margin and whether liquid hydrocarbons might form in the seal area before a detailed seal-system study.

Skill for Claude CodeCodex

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

Good fit Estimating seal-gas and separation-gas demand and comparing seal-cavity temperature with the hydrocarbon dew point to flag condensation risk.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/equinor/neqsim-community-skills/dry-gas-seal-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 dry-gas-seal-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-dry-gas-seal-screening

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/equinor/neqsim-community-skills/dry-gas-seal-screening"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/dry-gas-seal-screening.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 75 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,411 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 $0.00075 $0.01411
Opus 5 $0.00037 $0.00705
Sonnet 5 $0.00015 $0.00282
Haiku 4.5 $0.00007 $0.00141

Measured 12d ago against content hash badb439624ed, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

neqsim-dry-gas-seal-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_dry_gas_seal_screening.py, src/dry_gas_seal_screening/__init__.py, src/dry_gas_seal_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/process/dry-gas-seal-screening/SKILL.md · 112 lines

How it starts

The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Dry Gas Seal Screening

Use this skill for public, educational dry gas seal screening on centrifugal compressors. It estimates the seal-gas and separation-gas supply demand from the primary seal leakage rate and flags retrograde-condensation risk by comparing the seal cavity temperature against a hydrocarbon dew point at the seal/vent reference condition.

When to Use

  • When a user asks whether a compressor dry gas seal has adequate seal-gas supply margin.
  • When an agent needs a quick condensation-risk triage for the primary vent / standpipe dead-leg before a detailed seal study.
  • When examples must run without confidential seal vendor data, machine line lists, or project seal-gas conditioning specifications.

Inputs

  • seal_leakage_rate_nl_per_min: primary seal leakage rate at standard conditions in NL/min per seal.
  • seal_cavity_temperature_c: seal cavity (process-side) temperature in degrees C.
  • hydrocarbon_dew_point_c: hydrocarbon dew point at the seal/vent reference condition in degrees C.
  • seal_count: number of seals supplied (default 2: drive end and non-drive end).
  • supply_margin: seal-gas supply margin over leakage, dimensionless, default 1.25.
  • separation_gas_rate_nl_per_min: separation (secondary) gas rate per seal in NL/min, default 0.0.

Outputs

  • total_seal_gas_supply_nl_per_min: screening seal-gas supply demand.
  • separation_gas_supply_nl_per_min: screening separation-gas supply demand.
  • seal_gas_supply_margin_ratio: the applied supply margin over leakage.
  • condensation_margin_c: seal cavity temperature minus the hydrocarbon dew point.
  • condensation_warning: ok, watch, or high (a small or negative margin means higher condensation risk).
  • assumptions: public assumptions used by the placeholder model.

Engineering Method

The Python class DryGasSealModel uses open, public concepts only:

  • seal-gas supply demand is the primary seal leakage multiplied by the seal count and a supply margin, reflecting the API 692 intent that supply must exceed leakage to keep the seal faces clean.
  • separation-gas supply demand scales with the seal count.
  • the condensation margin compares the seal cavity temperature against a hydrocarbon dew point at the seal/vent reference condition. A small or negative margin flags retrograde condensation risk in the primary vent piping and standpipe dead-legs, which is the failure mode that seal-gas conditioning units exist to prevent.

Read the full file on GitHub · 112 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 · 112 lines · 75 tokens per session scan A badb439624ed

Subscribe to this mod's changes

neqsim-dry-gas-seal-screening is a skill published in the GitHub repository equinor/neqsim-community-skills (2 stars, last pushed yesterday), licensed Apache-2.0. It adds 75 tokens to every session and 1,411 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.

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