Crowdsourcing has emerged as an important paradigm in human-problem solving techniques on the Web. One application of crowdsourcing is to outsource certain tasks to the crowd that are difficult to implement as solutions based on software services only. Another benefit of crowdsourcing is the on-demand allocation of a flexible workforce. Businesses may outsource certain tasks to the crowd based on workload variations. The paper addresses the monitoring of crowd members' characteristics and the effective use of monitored data to improve the quality of work. Here the authors propose the extensions of standards such as Web Service Level Agreement (WSLA) to settle quality guarantees between crowd consumers and the crowdsourcing platform. Based on negotiated agreements, they provide a skill-based crowd scheduling algorithm.