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.
npx skills add equinor/neqsim-community-skills --skill relief-load-screeninggit clone --depth 1 https://github.com/equinor/neqsim-community-skillsWrote 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.
[](https://agentmods.dev/skills/equinor/neqsim-community-skills/relief-load-screening)<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.
<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>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.
| Model | Per session | Once 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 |
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.
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.
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, orhigh.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^0.82with 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.
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.
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.
- 12d ago First seen · 107 lines · 44 tokens per session scan A ae0f003694c9
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.
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