As researchers embrace micro-task markets for eliciting human input, the nature of the posted tasks moves from those requiring simple mechanical labor to requiring specific cognitive skills. On the other hand, increase is seen in the number of such tasks and the user population in micro-task market places requiring better search interfaces for productive user participation. In this paper we posit that understanding user skill sets and presenting them with suitable tasks not only maximizes the over quality of the output, but also attempts to maximize the benefit to the user in terms of more successfully completed tasks. We also implement a recommendation engine for suggesting tasks to users based on implicit modeling of skills and interests. We present results from a preliminary evaluation of our system using publicly available data gathered from a variety of human computation experiments recently conducted on Amazon's Mechanical Turk.