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 --skill neqsim-autonomous-investigationgit clone --depth 1 https://github.com/equinor/neqsimWrote 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/neqsim-autonomous-investigation)<a href="https://agentmods.dev/skills/equinor/neqsim/neqsim-autonomous-investigation"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/neqsim-autonomous-investigation/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/neqsim-autonomous-investigation"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/neqsim-autonomous-investigation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00130 | $0.02545 |
| Opus 5 | $0.00065 | $0.01273 |
| Sonnet 5 | $0.00026 | $0.00509 |
| Haiku 4.5 | $0.00013 | $0.00254 |
Grade A, and why
neqsim-autonomous-investigation 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.
How it starts
The opening of the file, as written. The whole thing — 217 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NeqSim Autonomous Investigation Skill
Make agents investigate instead of follow a checklist. Use this skill when a task (PEPR action, root-cause, operational study, digital-twin deviation) does not tell you the symptom, the driver, or which relationships matter. The goal is that the agent discovers the important relationships from the data and the flowsheet on its own, forms competing hypotheses, and tests them — reaching findings that were not spelled out in the task.
When to Use
- A PEPR action or work order describes a problem but not a cause.
- Historian data is available but no one has said "look at tag X vs tag Y".
- A model-vs-plant deviation appears and the responsible variable is unknown.
- Any "why is this happening?" question where spoon-feeding relations is wrong.
Do not use this to replace a known, well-scoped calculation — if the symptom
and mechanism are already given, go straight to neqsim-root-cause-analysis or
the relevant discipline skill.
The Investigation Loop (mandatory ordering)
Run this loop before fixing a scope. Never assume the task's stated classification is correct — treat it as a hypothesis to challenge.
- Observe (no assumptions). Pull all available tags, not just the ones the
task names. Scan each tag against its own baseline and (when available) STID
design envelope for what is abnormal. Discover cross-tag relationships with
RelationshipGraph— including lead-lag direction, which distinguishes a driver from a follower. - Hypothesize (compete). Generate at least three competing causal hypotheses, always including a "not a real problem / instrument or data artifact" hypothesis. Seed them from the discovered leaders (candidate causes), not from intuition alone.
- Predict (differentiate). For each hypothesis, state what it implies for other tags/streams. Two hypotheses that predict the same thing cannot be distinguished — find a prediction where they disagree (the discriminating test).
- Test. Use NeqSim (
runProcess/runFlowAssurance/simulation verification viaRootCauseAnalyzer) plus historian evidence (EvidenceCollector) to check each prediction. - Discriminate & iterate. Keep the hypothesis that best explains the pattern across relationships, not a single number. Loop until one dominates or the data is exhausted; report residual ambiguity honestly.
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.
- 11d ago First seen · 217 lines · 130 tokens per session scan A 158aa22b3c7e
neqsim-autonomous-investigation is a skill published in the GitHub repository equinor/neqsim (151 stars, last pushed today), licensed Apache-2.0. It adds 130 tokens to every session and 2,545 once invoked, about $0.0006 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 2026-08-30.
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