Maritime AI Prompt Pack

Here are my fleet’s last month of technical and commercial summaries. Rank the vessels by combined operational and commercial risk, and give one sentence of reasoning per vessel.

List the maritime regulations most likely to affect a fleet of [number] [vessel type]s trading [region] in the next 24 months, ordered by financial impact. Cite the instrument for each.

A [age]-year-old [vessel type] needs [survey/docking scope]. Lay out the invest-vs-sell decision as a table: costs to keep trading 5 more years vs realistic sale scenario. State every assumption you make.

Estimate the combined EU ETS and FuelEU exposure for a fleet of [number] [vessel type]s on [trade pattern], and show the arithmetic step by step so my team can check it.

Turn these weekly technical reports into a one-page management summary: top 5 issues, money at stake, decisions needed from me, deadlines.

Based on these reports, which vessel has the highest near-term risk of detention, breakdown or off-hire — and what single action reduces it most?

Compare these four vessels’ weekly reports. Show critical deviations only — anything that moved for the worse since last week — grouped by vessel.

Compared with last month, what deteriorated across the fleet? Answer as a ranked list with the evidence line quoted from the source report.

From these reports, find issues that recur across more than one vessel of the same superintendent. Present as a table: issue, vessels, first seen, still open?

Prepare the five topics I must raise at this month’s board meeting from these fleet reports, each with one number that makes the point.

Read these vessel reports and flag weak signals: items mentioned in passing that have not become failures yet but follow a worsening trend.

Our technical budget overran by [X]%. Using these cost records, attribute the overrun: maintenance, fuel, unplanned repairs or purchasing — with figures.

Analyse these main engine performance parameters. Identify values outside typical ranges for a [engine type] and rank them by urgency of investigation. Do not invent limits — say when a maker limit is needed.

Exhaust gas temperatures across units differ by [X]°C at [load]. List the plausible causes in order of likelihood, and the first onboard check for each.

Lube oil consumption has risen over three months from [X] to [Y] l/day. Build a diagnostic checklist from most likely to least likely cause.

Sort this PMS overdue list by criticality: safety-critical, class-related, redundancy-loss, routine. Justify each top-5 placement.

Draft a dry dock work scope from this defect list and last docking report: group by trade, flag long-lead items, mark anything that can safely wait one more cycle.

Review this maker service report. Separate: work that is clearly necessary, work that needs evidence, and items that may fall under warranty — with the sentence that justifies each call.

Compare these two spare part quotations technically and commercially: genuine vs OEM-equivalent, delivery, exclusions, hidden costs. Recommend one and state the risk of the cheaper option.

For this SIRE 2.0 observation, draft root cause, immediate cause, corrective action and preventive action — then critique your own draft: would a vetting reviewer call it superficial?

We have a SIRE 2.0 inspection in 14 days on a [vessel type]. Build a day-by-day preparation plan covering hardware walkthroughs, documentation, and crew interview readiness.

Generate the follow-up questions a SIRE 2.0 inspector could ask the chief engineer about [system/procedure], with strong example answers.

From our last three PSC reports, list recurring deficiencies and the systemic weakness each points to. One line each.

Our next port call is [port]. Based on published Paris/Tokyo MoU categories, which deficiency areas deserve a focused pre-arrival check for a [vessel type]?

Review this observation response before submission: strengthen the weakest sentence, remove anything that admits liability beyond the finding, keep it factual.

Calculate this voyage’s true profitability after bunkers, port costs and EU ETS. Show the arithmetic and state every rate assumption separately so I can swap in actuals.

Under this charterparty wording, who bears the EU ETS cost — owner or charterer? Quote the clause language your answer depends on, and say what is ambiguous.

Assess this technical failure from an off-hire claim perspective: what does the charterer need to prove, what evidence should we preserve today?

Compare the [route A] and [route B] routings for cost and risk: fuel, days, war-risk premium, ETS scope difference. Present as a decision table.

Review this demurrage claim and list its weak points: laytime calculation, notice validity, exceptions clauses — with the supporting clause for each.

Summarise this charterer’s risk profile from the information I paste: payment history signals, operational disputes, sanctions exposure. Mark what is verified vs inferred.

We are evaluating a maritime software vendor. Build a due-diligence question list covering: data ownership, hosting jurisdiction, OT/IT separation, offboarding and data return.

Which shipboard systems are typically most exposed to cyber attack, and what is the first low-cost mitigation for each? Frame per IACS E26/E27 categories.

List the practical signs of AIS spoofing or GPS jamming a bridge team can detect, and the immediate verification steps.

Draft the questions to ask before uploading any vessel data to an external AI service: retention, training use, access, deletion, jurisdiction.

Review this vendor’s security claims. Which are verifiable certificates and which are marketing language? List what evidence to request for each claim.