Besides the user guide, there exist other opportunities to get help on using skimage.
The examples_gallery gallery provides graphical examples of typical image processing tasks. By a quick glance at the different thumbnails, the user may find an example close to a typical use case of interest. Each graphical example page displays an introductory paragraph, a figure, and the source code that generated the figure. Downloading the Python source code enables one to modify quickly the example into a case closer to one's image processing applications.
Users are warmly encouraged to report on their use of skimage on the mailing_list, in order to propose more examples in the future. Contributing examples to the gallery can be done on github (see howto_contribute).
The quick search field located in the navigation bar of the html documentation can be used to search for specific keywords (segmentation, rescaling, denoising, etc.).
Docstrings of skimage functions are formatted using Numpy's documentation standard, starting with a Parameters section for the arguments and a Returns section for the objects returned by the function. Also, most functions include one or more examples.
The scikit-image mailing-list is scikit-image@python.org (users should join before posting). This mailing-list is shared by users and developers, and it is the right place to ask any question about skimage, or in general, image processing using Python. Posting snippets of code with minimal examples ensures to get more relevant and focused answers.
We would love to hear from how you use skimage for your work on the mailing-list!