We had the following considerations in mind when developing Collective Knowledge (CK) and CK-based workflows:

• Why: we are passionate about collaborative, reproducible, systematic and reusable R&D. We have open-sourced Collective Knowledge to help the community collaboratively tackle grand challenges in computer engineering and other sciences.
• Open-source/agile: our vision for Collective Knowledge is to follow the Linux, Wikipedia and GitHub way for collaborative R&D. To fully embrace the open-source movement, we use a permissive license, no strings attached!
• Community-driven: by partnering with leading companies, universities and conferences, we are growing a passionate community of users and developers to create a public repository of customizable and reusable artifacts!
• Simple: Collective Knowledge is a cross-platform and relatively simple knowledge management system taking advantage of the best modern techniques including JSON, Git, Elasticsearch, SciPy, web services and agile R&D methodology to deal with ever changing software and hardware stack.
• Portable and extensible: minimal software dependencies, plugin-based architecture and simple JSON API makes CK easily extensible and customizable. CK components can be assembled together just like LEGO bricks to focus on problem-solving. Full API validation can be added at any time after research idea is prototyped and fully validated.
• Agile documentation (incremental improvements): We have spent considerable effort to document Collective Knowledge via community-maintained wiki covering everything from the basics to advanced use cases including artifact sharing, universal autotuning and predictive analytics. CK users also write blog articles describing their own ways to understand, use and extend CK (see Michel Steuwer's blog).

Feel free to reuse or extend CK repositories, workflows, software detection plugins, portable packages and modules already shared by the community.

Please, check this wiki for further details about CK development.