We propose Meta-World Conditional Neural Process (MW-CNP), a conditional world model generator that leverages sample efficiency and scalability of Conditional Neural Processes architecture to allow an agent to sample from the generated world model. We intend to reduce agent's interaction with the target environment as much as possible. Thus, we designed a model-based meta-RL framework where the RL agent can be conditioned on significantly fewer samples collected from the target environment to imagine the unseen environment. We emphasize that the agent does not have access to the task parameters throughout training and testing.