How do we learn when to persist, when to let go, and when to shift gears? Gearshift Fellowship (GF) is the prototype of a new Supertask paradigm designed to model how humans and artificial agents adapt to shifting environmental demands. We introduce Supertasks as novel paradigms that combine serious gaming with computational neurocognitive modeling to study adaptive behavior in dynamic, naturalistic environments. By grounding them in cognitive neuroscience, computational psychiatry, economics, and artificial intelligence, they provide methodological scaffolding for creating structured environments that assess the underlying mechanisms of adaptive behavior using cognitive models. The resulting computational parameters not only explain behavior but also shape the game environment, enabling real-time probing of these mechanisms. Unlike traditional tasks, GF supports individualized modeling across perceptual, learning, and meta-cognitive levels. This offers a flexible testbed for understanding how cognitive control, learning strategies, affect, and motivation shift across contexts and over time. GF serves as an experimental platform for scientists, a phenotype-to-mechanism bridge for clinicians, and a training tool for individuals seeking to strengthen self-regulated learning, mood, and stress resilience. Early results from an ongoing online study (n = 60) replicate canonical effects from established laboratory tasks (demonstrating construct validity) and uncover novel patterns in learning dynamics and clinical features. These findings lay the groundwork for in-game interventions that build self-efficacy and agency in the face of real-world uncertainty. GF offers a new adaptive ecosystem that accelerates scientific discovery, transforms personalized care, and fosters individual growth. It serves as both a mirror and a training ground where humans and machines co-evolve, cultivating deeper flexibility and awareness.