neqsim-relief-load-screening

neqsim-relief-load-screening is a skill for Claude Code, Codex from equinor/neqsim-community-skills. It costs 44 tokens per session (1,125 once invoked), scanned A, original, Apache-2.0.

An educational calculator for early screening of fire-related pressure-relief loads. A pressure-relief device releases fluid when pressure becomes unsafe.

In plain words
What is it for?
Use it to estimate fire heat input, vapor relief mass rate, and a relief-load indicator from wetted area, latent heat, pressure, and an environment factor.
Why use it?
It gives a rough public estimate for initial planning without confidential vessel data or detailed engineering design rules.

Skill for Claude CodeCodex

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

Good fit Use it to estimate fire heat input, vapor relief mass rate, and a relief-load indicator from wetted area, latent heat, pressure, and an environment factor.

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

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/equinor/neqsim-community-skills/relief-load-screening"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/relief-load-screening.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 44 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,125 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 $0.00044 $0.01125
Opus 5 $0.00022 $0.00562
Sonnet 5 $0.00009 $0.00225
Haiku 4.5 $0.00004 $0.00112

Measured 12d ago against content hash ae0f003694c9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

neqsim-relief-load-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_relief_load_screening.py, src/relief_load_screening/__init__.py, src/relief_load_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/safety/relief-load-screening/SKILL.md · 107 lines

How it starts

The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Relief Load Screening

Use this skill for public, educational pressure-relief screening examples. It provides a simple fire-case heat input and relief mass-rate indicator that helps agents structure early relief questions before moving to validated relief sizing and detailed process safety design.

When to Use

  • When a user asks for a quick, public fire-case relief load triage example.
  • When an agent needs a placeholder relief mass-rate estimate to scope a relief or flare study.
  • When examples must run without confidential vessel data, project relief bases, or company design rules.

Inputs

  • wetted_area: fire-exposed wetted surface area in m2.
  • latent_heat: latent heat of vaporization of the relieving fluid in kJ/kg.
  • relief_pressure: set or relieving pressure in bara (used for context and warnings).
  • environment_factor: dimensionless credit factor for insulation or drainage, default 1.0 (no credit).

Outputs

  • fire_heat_input_kW: screening fire heat input from a public API 521 style correlation.
  • relief_mass_rate_kg_per_h: screening vapor relief mass rate from heat input divided by latent heat.
  • relief_load_indicator: dimensionless ratio of relief mass rate to a configurable public basis.
  • relief_warning: ok, watch, or high.
  • assumptions: public assumptions used by the placeholder model.

Engineering Method

The Python class ReliefLoadModel uses open, published correlations only:

  • fire heat input uses the public API 521 adequate-drainage form Q = F * C * A^0.82 with a public coefficient expressed in kW.
  • vapor relief mass rate is estimated as fire heat input divided by latent heat of vaporization.
  • the relief load indicator scales the relief mass rate by a configurable public reference basis.
  • warnings are rule-based thresholds on the relief load indicator.

This is educational and screening-only logic. It is not a relief sizing standard, an orifice sizing method, a vendor method, or a replacement for a validated relief design and process safety review.

Read the full file on GitHub · 107 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 · 107 lines · 44 tokens per session scan A ae0f003694c9

Subscribe to this mod's changes

neqsim-relief-load-screening is a skill published in the GitHub repository equinor/neqsim-community-skills (2 stars, last pushed yesterday), licensed Apache-2.0. It adds 44 tokens to every session and 1,125 once invoked, about $0.0002 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