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        "author": "TJ Nicholls",
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        "description": "# PyFunnels\n\nThe goal of PyFunnels is to create a collaborative code library which makes integrating data into automated workflows easier. The library acts as a centralized location where everyone can contribute and use code. \n\nPyFunnels consists of multiple classes structured modularly so that additional tools and data points can be easily added and work independently of one another. The classes within the library can be thought of as a catalog of tools and methods to retrieve data. Not all data point methods are required for each tool, meaning a new tool can be added with only a single method. Ideally, all data points would be supported for each tool but this structure allows the functionality to grow organically and makes it easy to contribute code to the project. \n\n![](Pyfunnels_Concept.gif)\n\nThe library reduces the time it takes information security professionals to utilize output from tools. For example, consider the following workflow:\n1.\tCollect data with tool one.\n2.\tCollect data with tool two.\n3.\tWrite code to isolate the data for tool one.\n4.\tWrite code to isolate and data for tool two. \n5.\tMerge data into a standard format.\n6.\tRemove duplicated data.\n7.\tExpose normalized data.\n\nTo summarize, this workflow can be reduced to the following using PyFunnels:\n1.\tSpecify output files\n2.\tInitiate an object.\n3.\tUse method on the object.\n4.\tExpose normalized data.\n\nPyFunnels has been purposely structured for ease of use and extensibility to new tools and data points. Users of the library are encouraged to contribute code for new tools and data points they find useful. Whenever a user creates Python3 code to isolate data from the output of a tool, he or she is encouraged to commit that code to PyFunnels so others in the community can use it as well.\n\n\n",
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