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neqsim-reliability-data-screening

neqsim-reliability-data-screening is a skill for Claude Code, Codex from equinor/neqsim-community-skills. It costs 85 tokens per session (1,272 once invoked), scanned A, original, Apache-2.0.

A calculation for basic equipment reliability and availability. Reliability is the chance equipment keeps working for a mission, while availability is the share of time it is ready to operate; the calculation uses failure rate, repair time, redundancy, and planned downtime.

In plain words
What is it for?
Use it to estimate mean time between failures, availability, mission reliability, expected failures, and simple parallel-redundancy effects.
Why use it?
It turns limited failure and repair information into early figures for maintenance and system planning. It can also show how parallel backup units affect availability.

Skill for Claude CodeCodex

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

not rated 4repo today A scan 85 tokens original Apache-2.0

Good fit Use it to estimate mean time between failures, availability, mission reliability, expected failures, and simple parallel-redundancy effects.

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

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/equinor/neqsim-community-skills/neqsim-reliability-data-screening"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/neqsim-reliability-data-screening.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 85 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,272 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 $0.00085 $0.01272
Opus 5.5 $0.00034 $0.00509
Sonnet 5.5 $0.00017 $0.00254
Haiku 4.5 $0.00009 $0.00127

Measured 1mo ago against content hash a819074b02d3, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

neqsim-reliability-data-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_reliability_data_screening.py, src/reliability_data_screening/__init__.py, src/reliability_data_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/neqsim-reliability-data-screening/SKILL.md · 115 lines

How it starts

The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Reliability Data Screening

Use this skill for public, educational reliability screening. From an ISO 14224 / OREDA style failure rate and a mean time to repair, it estimates mean time between failures (MTBF), steady-state availability with simple parallel redundancy, mission reliability, and expected failures, so an agent can scope a reliability/availability/maintainability (RAM) study before detailed analysis.

When to Use

  • When a user asks for an MTBF, availability, or reliability figure from a failure rate and repair time.
  • When an agent needs a quick parallel-redundancy availability estimate.
  • When planned downtime must be folded into a screening availability number.
  • When examples must run without proprietary OREDA datasets or vendor reliability data.

Inputs

  • failure_rate_per_year: failure rate (lambda) in failures per year.
  • mean_time_to_repair_h: mean time to repair in hours, default 24.
  • redundancy: number of parallel units (k = 1 of n), integer, default 1.
  • mission_time_years: mission duration in years for reliability, default 1.
  • planned_downtime_h_per_year: planned downtime in hours per year, default 0.

Outputs

  • mtbf_years: mean time between failures in years.
  • mtbf_h: mean time between failures in hours.
  • unit_availability: steady-state availability of a single unit.
  • system_availability: availability of the redundant system including planned downtime.
  • system_unavailability: one minus the system availability.
  • reliability_over_mission: probability of no system failure over the mission time.
  • expected_failures: expected single-unit failures over the mission time.
  • availability_warning: ok, watch, or low-availability.
  • assumptions: public assumptions used by the placeholder model.

Engineering Method

The Python class ReliabilityDataModel uses open reliability relations only:

  • MTBF uses MTBF = 1 / lambda (years) and MTBF_h = 8760 / lambda (hours).
  • single-unit availability uses A = MTBF_h / (MTBF_h + MTTR).
  • redundant unavailability from failures uses (1 - A) ** n for n parallel units.
  • planned downtime adds planned_downtime / 8760 to the unavailability, capped at 1.
  • mission reliability uses R = exp(-lambda * t) for a unit and 1 - (1 - R) ** n for the parallel system.
  • expected failures use lambda * mission_time for a single unit.

Read the full file on GitHub · 115 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 · 115 lines · 85 tokens per session scan A a819074b02d3

Subscribe to this mod's changes

neqsim-reliability-data-screening is a skill published in the GitHub repository equinor/neqsim-community-skills (4 stars, last pushed today), licensed Apache-2.0. It adds 85 tokens to every session and 1,272 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.

Analytics and metrics
Related

neqsim-reliability-data-screening compared with skills from other repositories

neqsim-reliability-data-screening next to 6 skills from other repositories
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