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 reliability-data-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/reliability-data-screening)<a href="https://agentmods.dev/skills/equinor/neqsim-community-skills/reliability-data-screening"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/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.
<a href="https://agentmods.dev/skills/equinor/neqsim-community-skills/reliability-data-screening"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/reliability-data-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 | $0.00085 | $0.01272 |
| Opus 5 | $0.00043 | $0.00636 |
| Sonnet 5 | $0.00017 | $0.00254 |
| Haiku 4.5 | $0.00009 | $0.00127 |
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 10d 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 — 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, orlow-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) andMTBF_h = 8760 / lambda(hours). - single-unit availability uses
A = MTBF_h / (MTBF_h + MTTR). - redundant unavailability from failures uses
(1 - A) ** nfornparallel units. - planned downtime adds
planned_downtime / 8760to the unavailability, capped at 1. - mission reliability uses
R = exp(-lambda * t)for a unit and1 - (1 - R) ** nfor the parallel system. - expected failures use
lambda * mission_timefor a single unit.
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.
- 10d ago First seen · 115 lines · 85 tokens per session scan A a819074b02d3
neqsim-reliability-data-screening is a skill published in the GitHub repository equinor/neqsim-community-skills (2 stars, last pushed today), licensed Apache-2.0. It adds 85 tokens to every session and 1,272 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.
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