Here are two examples of Gild's algorithms for evaluating skills and knowledge based on what the company finds out about a subject on the Web. The company has created more than 50,000 of these "features," or rules, for its Gild Source service.
Using Bayesian analysis, Gild claims it can predict how skilled a subject might be even little data is available, such as when a person has no open-source code available for evaluation.
Raw data: In an online profile you describe yourself as proficient in C/C++.
Conclusion: You're not very good at either.
Logic: C and C++ are completely different languages. So why would you lump them together? Listing them together indicates that you may have just put them in as checklist items.
Raw data: On Twitter you recently said that Celery sucks.
Conclusion: You have knowledge of Python, Django and Celery.
Logic: The fact that you dislike the asynchronous processing toolkit, written in Python and used extensively in Python Web development, means you're not only familiar with Celery but almost certainly are knowledgeable about Python and Django, with which Celery is commonly used.