neqsim-root-cause-analysis

neqsim-root-cause-analysis is a skill for Claude Code, Codex from equinor/neqsim. It costs 101 tokens per session (7,284 once invoked), scanned A, original, Apache-2.0.

A structured method for finding why process equipment fails or behaves abnormally, using reliability information, plant historian data, and NeqSim process simulations.

In plain words
What is it for?
Use it to investigate compressor, pump, or turbine trips; high vibration; efficiency loss; separator carryover; heat-exchanger fouling; valve problems; and other operating anomalies.
Why use it?
It turns scattered evidence about trips, vibration, fouling, or other problems into ranked possible causes. This helps teams investigate equipment issues systematically instead of relying only on guesswork.

Skill for Claude CodeCodex

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

Good fit Use it to investigate compressor, pump, or turbine trips; high vibration; efficiency loss; separator carryover; heat-exchanger fouling; valve problems; and other operating anomalies.

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Install with agentmods
npx agentmods add skills/equinor/neqsim/neqsim-root-cause-analysis
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 --skill neqsim-root-cause-analysis
Clone the repo
git clone --depth 1 https://github.com/equinor/neqsim

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-root-cause-analysis

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/equinor/neqsim/neqsim-root-cause-analysis"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/neqsim-root-cause-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 101 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 7,284 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. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Excessive Agency · line 286
    Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.
    Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
How audits are shown
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 $0.00101 $0.07284
Opus 5.5 $0.00040 $0.02914
Sonnet 5.5 $0.00020 $0.01457
Haiku 4.5 $0.00010 $0.00728

Measured 11d ago against content hash 7d461afbb5e4, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

neqsim-root-cause-analysis 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 11d 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.

.github/skills/neqsim-root-cause-analysis/SKILL.md · 694 lines

How it starts

The opening of the file, as written. The whole thing — 694 lines — stays where its author put it; the contents beside it link to each section on GitHub.

NeqSim Root Cause Analysis Skill

Systematic equipment-level root cause analysis integrating process simulation, multi-source reliability data, plant historian time-series, and STID design conditions.

When to Use

  • Equipment trip investigation (compressor, pump, turbine)
  • High vibration diagnosis
  • Efficiency degradation analysis
  • Separator liquid carryover or gas blow-by
  • Heat exchanger fouling assessment
  • Valve erosion or malfunction
  • Any operational anomaly requiring structured root cause identification

Already-closed incident with a "document learning" ask

When the failure is already resolved and recorded (for example a closed Blalys whose only open subtask is to document learning), do not re-derive the cause. Cite the recorded cause, test it for consistency against the historian and alarm evidence (consistent, contradicted or not testable), then deliver a consequence and learning screening: capacity impact (for a derated driver use Compressor.getDriverLimitedEnvelope()), the barrier or early indicator that would have shortened the outage, and the cost of recurrence. Mark causal claims beyond the recorded cause as hypotheses.

Rapid Triage Mode (live disturbance)

A full RCA is a study. When the plant is still down and a response team needs a direction within minutes, run the same framework in a reduced, explicitly labelled mode rather than a different method:

Full RCA Rapid triage
Prior Multi-source reliability data Reliability prior only, no source reconciliation
Likelihood Full historian window, quality-screened Last stable period to now, unscreened
Verification NeqSim simulation of each hypothesis Skipped; verification is deferred
Output Ranked hypotheses with confidence Top 3 candidates, confidence capped at MODERATE
Cause claim Established when evidence supports it NOT_DETERMINED unless a single candidate dominates

Rules that make the reduced mode safe to act on:

Read the full file on GitHub · 694 lines

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. 11d ago Changed · +9 lines 7d461afbb5e4
  2. 12d ago Changed · +28 lines 851bb1e275b5
  3. 20d ago Changed · -31 tokens per session 3e5b77199855
  4. 29d ago Changed · +64 lines · +30 tokens per session 8d5f768f2b61
  5. 1mo ago First seen · 593 lines · 102 tokens per session scan A e53c74c3eb43

Subscribe to this mod's changes

neqsim-root-cause-analysis is a skill published in the GitHub repository equinor/neqsim (156 stars, last pushed today), licensed Apache-2.0. It adds 101 tokens to every session and 7,284 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 2026-09-03.

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