neqsim-technology-fmirr

neqsim-technology-fmirr is a skill for Claude Code, Codex from equinor/neqsim-community-skills. It costs 88 tokens per session (1,541 once invoked), scanned A, original, Apache-2.0.

A failure-mode register for a technology being qualified. It records what can fail, why it can fail, how serious and likely it is, and which qualification test addresses it; FMIRR means Failure Mode Identification and Risk Ranking.

In plain words
What is it for?
It helps prepare or update a risk register, plan qualification activities, and track how test results or mitigations change risk ratings.
Why use it?
It provides a structured way to prioritise technology risks and connect important risks to evidence from testing. It does not replace review by experts from the relevant disciplines.

Skill for Claude CodeCodex

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

Good fit It helps prepare or update a risk register, plan qualification activities, and track how test results or mitigations change risk ratings.

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Install with agentmods
npx agentmods add skills/equinor/neqsim-community-skills/neqsim-technology-fmirr
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-technology-fmirr
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-technology-fmirr

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/equinor/neqsim-community-skills/neqsim-technology-fmirr"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/neqsim-technology-fmirr.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 88 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,541 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 $0.00088 $0.01541
Opus 5.5 $0.00035 $0.00616
Sonnet 5.5 $0.00018 $0.00308
Haiku 4.5 $0.00009 $0.00154

Measured 10d ago against content hash c5ed61500594, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

neqsim-technology-fmirr 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.

The scan reads SKILL.md. This mod also ships 4 executable files (examples/basic_fmirr.py, src/technology_fmirr/__init__.py, src/technology_fmirr/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/neqsim-technology-fmirr/SKILL.md · 146 lines

How it starts

The opening of the file, as written. The whole thing — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Technology FMIRR (Failure Mode Identification and Risk Ranking)

Use this skill to keep and rank the failure-mode register of a technology that is being qualified. The register is the basis for defining and prioritising the qualification activities: every critical failure mode must trace to a qualification activity (the TEST column) and every re-rating must trace to evidence.

When to Use

  • After neqsim-technology-assessment has identified novel elements (category 2b-4) and before the qualification plan is written.
  • To prepare or review the output of an FMIRR workshop with an expert panel.
  • To repeat the FMIRR as test results and mitigations change the risk.
  • To feed the FMIRR chapter of neqsim-technology-assessment-report.

It does not replace the workshop. Failure modes, causes and ratings must come from a panel covering the needed disciplines.

Inputs

  • technology name.
  • entries: one per failure mode with id, item (failed item or component), function, failure_mode, failure_mechanism, failure_mechanism_class, failure_cause, failure_cause_class, detection, consequence_text, consequence_class (1-5), probability_class (1-5), comments, qualification_test (link to the qualification plan), lifecycle_phase, novelty_category.
  • Optional: a custom 5x5 matrix and the high-consequence floor setting.

Outputs

  • rows with the computed risk (Low, Medium, High) and a critical flag.
  • summary: counts per risk level, critical ids ranked, critical modes with and without a qualification test.
  • risk_matrix: counts per probability and consequence cell.
  • findings: warnings and gaps (mixed terminology, missing mechanism, critical modes without a test or detection, life-cycle phases not covered).
  • history: previous ratings with their basis; iteration number.

Engineering Method

  1. Scope. Take the novel elements from the technology assessment and their life-cycle phases (design, fabrication, transport, installation, commissioning, operation and maintenance, decommissioning).
  2. Identify failure modes per element and phase. Keep four separate fields:
    • Failed item: the component that fails.
    • Failure mode: how it fails (for example external leakage, fails to close).
    • Failure mechanism: the physical process (mechanical, material, instrumentation, electrical, external influence, miscellaneous).
    • Failure cause: why (design, fabrication/installation, operation/maintenance, management, miscellaneous; include human error). Use the equipment-specific ISO 14224 failure-mode tables where they exist.
  3. Rate consequence for the system as specified, without safeguards that are not yet adopted. Use the highest class across people, environment, delay/downtime and reputation. Define the scale for the case.
  4. Rate probability with qualitative classes early; replace by evidence (tests, analysis, documented experience) as it arrives. If evidence is lacking, be conservative, more so for higher novelty.
  5. Rank risk with the matrix. Medium and High are critical and must be covered by the qualification plan. Low modes stay in the register. The high-consequence floor keeps class-5 consequences at Medium or above so that low probability does not dismiss them.
  6. Link tests. Give each critical mode a qualification activity and a detection method.
  7. Re-rate and repeat. Record the basis for every change, start a new iteration at each qualification milestone, and never delete a mode.

Read the full file on GitHub · 146 lines

Files

What ships with it

7 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. 10d ago First seen · 146 lines · 88 tokens per session scan A c5ed61500594

Subscribe to this mod's changes

neqsim-technology-fmirr is a skill published in the GitHub repository equinor/neqsim-community-skills (4 stars, last pushed today), licensed Apache-2.0. It adds 88 tokens to every session and 1,541 once invoked, about $0.0004 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 2026-10-28.

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