Learn how the 30-minute Ocean Cleanup game works across three sites, then prepare with a detailed phase walkthrough, practical strategy, and realistic McKinsey Sea Wolf practice.
Last updated: September 2026
McKinsey Solve walkthrough
McKinsey Sea Wolf is a constraint-satisfaction game within the McKinsey Solve assessment. You act as a microbiologist developing treatments for three contaminated ocean sites, selecting microbe combinations that satisfy each site’s requirements within a 30-minute window.
You may also see it called Ocean Cleanup, Ocean Treatment, the microbe game, or simply the Sea Wolf game. These names refer to the same Solve module.
Each microbe has one trait and three numerical attributes with values from 1 to 10. Each site provides target ranges for all three attributes, one desired trait, and one undesired trait. Your final output is a treatment of exactly three microbes whose attribute averages land inside the target ranges, include the desired trait, and avoid the undesired trait.
Sea Wolf is commonly completed alongside the 35-minute Redrock module. A 65-minute invitation often refers to those two games; 85- and some 95-minute invitations may add a Sustainable Futures Lab format. These are reported patterns rather than a universal schedule, so your invitation and on-screen instructions take priority. The McKinsey Solve Game guide explains the complete sequence.
The visible work exercises analytical thinking: understanding constraints, filtering information, adapting across stages, and reaching a defensible answer under time pressure. McKinsey does not publish the weight assigned to individual clicks, phases, or treatment results in its broader assessment methodology.
The same sequence repeats across the three sites. Early decisions organise the work; Prospecting and Treatment shape the final pool and submitted treatment.
Select two characteristics to prioritise. This signals how you reason but does not change which microbes appear later in the current practice model.
Pacing target: under 30 seconds.
Sort ten microbes into three buckets: keep for the current site, save for the next site, or return. You can see the current requirements and one clue about the next site.
This phase rewards fast, consistent classification rather than exhaustive calculation.
Build the pool used in Treatment. You begin with six microbes and add four more through successive choices, finishing with a pool of ten.
Rough filtering is usually faster than trying to solve every possible trio during selection.
Choose exactly three microbes from the pool and submit the treatment. Their three attribute averages and desired or undesired traits determine the displayed treatment effectiveness.
This is the final optimisation step, so protect most of your remaining time for it.
Time management is the central challenge. Move through Profiling and Categorising with consistent rules so that Prospecting and Treatment receive the attention they need.
Spend roughly 15–20 seconds noting the desired and undesired traits, the next-site clue, and the three target ranges. When a range spans two values, memorise the lower bound and mentally add two rather than repeatedly looking back and forth.
The Sea Wolf Cheat Sheet keeps these bucket rules in a compact reference.
Detailed optimisation during every choice is theoretically attractive but usually too slow. Rough filtering is more practical: avoid undesired traits, retain a desired trait option, and build numerical variety. Treatment is the place to optimise the final trio.
Instead of averaging three values and then comparing the result with a target range, multiply both range bounds by three and compare the sum. A target of 3–5 becomes a valid sum window of 9–15. Values of 7, 4, and 5 total 16, so that trio misses the range.
Each site starts at 100% treatment effectiveness. The displayed treatment model deducts 20% for each attribute average outside its range, 20% when the desired trait is absent, and 20% when any selected microbe has the undesired trait. These five treatment criteria do not reveal how McKinsey weights the broader Solve assessment.
Worked example
Site requirements: Attribute 1 = 3–5, Attribute 2 = 5–7, Attribute 3 = 2–4; desired trait = Adaptive; undesired trait = Volatile.
Selected microbes: A (3, 6, 2, Stable), B (5, 7, 5, Adaptive), and C (4, 5, 2, Stable).
The averages are 4.0, 6.0, and 3.0. All sit inside their ranges, Adaptive is present, and Volatile is absent, producing 100% treatment effectiveness.
If no perfect combination exists, minimise missed criteria. When two combinations have equal displayed effectiveness, avoiding the undesired trait is usually the cleaner tiebreaker because it prevents a known negative condition.
A short orientation followed by realistic timed practice is more useful than extensive passive research. Use this sequence to turn the interface and calculations into habits.
Learn what each phase contributes and when the final pool is formed. Use the cheat sheet until the three categorisation buckets feel automatic.
Convert average ranges into sum windows and check three values quickly. The goal is fluency, not advanced mathematics.
Compare your choices with valid combinations to see why a trio succeeds or fails. Use it between timed runs, not as a substitute for them.
Work through all three sites without pausing. Review where time disappeared—often Categorising—and adjust one pacing rule before the next attempt.
The Sea Wolf simulator covers all four phases across three timed sites, with the same kinds of attributes, traits, constraints, and treatment decisions described in this guide. It is independent practice and is not affiliated with McKinsey.
The solver calculates averages and checks constraints to identify valid combinations. It accelerates pattern learning during preparation, but it is not available in the assessment and should sit alongside full timed practice.
Explore the Sea Wolf solverA compact reference for phase rules and bucket decisions.
More background on the scenario, mechanics, and terminology.
Focused calculation and treatment-selection practice.
Understand the other established game candidates commonly complete alongside Sea Wolf.
Browse the complete Sea Wolf preparation collection.
Clear answers about the format, names, scoring explanation, preparation, and practice.
Sea Wolf is a 30-minute constraint-optimisation module in McKinsey's digital Solve assessment. Candidates select microbe combinations to meet site-specific treatment targets across three ocean sites. It is also called Ocean Cleanup, Ocean Treatment, or the microbe game.
Yes. Ocean Cleanup, Ocean Treatment, the microbe game, and Sea Wolf are names used for the same module inside McKinsey Solve.
Sea Wolf lasts 30 minutes and covers three sites. It is completed in the same sitting as the other modules listed in your invitation, and you cannot pause mid-module.
Each site starts at 100% treatment effectiveness. Deductions of 20% apply for each attribute average outside its range, missing the desired trait, or including an undesired trait. McKinsey does not publish how these treatment results are weighted in its broader assessment methodology.
The challenge is primarily time-based. Processing microbe attributes, applying constraints, and making selections must happen faster than most candidates initially expect. Practice under timed conditions reveals where improvements are needed.
Spend minimal time on Profiling, move efficiently through Categorising, and reserve most of the clock for Prospecting and Treatment, where your choices directly shape the final treatment.
Not always. Some site configurations may not offer a perfect-scoring combination. When no perfect combination exists, choose the option that minimises the number of missed criteria.
A solver accelerates learning by showing which combinations work without manual calculation. It helps build pattern recognition for the assessment, when the practice solver is not available.
Choose the combination that minimises penalties. Avoiding the undesired trait is usually the safer tiebreaker when two options produce the same treatment score.
McKinsey does not publish a full practice version. Solve Games Guide includes one free Sea Wolf session with the complete four-phase flow. No credit card is required.
Select the package that fits your preparation needs. All packages include 2-week access.
You only get one attempt at the McKinsey Solve Games. This costs less than a single interview coaching session, and prepares you for the actual filter.
The complete toolkit: simulations, solver, cases, and both unique versions of the Sustainable Futures Lab game. Everything you need to prepare with confidence.
Three-hour prep for candidates who want one Redrock case per template, two Sustainable Futures Lab missions, and the Sea Wolf solver.
Start with one free session covering all four phases across three timed sites.
