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The Simple Guide to Agent Skills
One of the easiest and most flexible new ideas in language model tools is the idea of skills. You can build these yourself easily and plug them into your favourite language model app. You can also share them and download other peoples skills using cool new standards like skills.sh. But before you do any of that, lets try and understand what they are.
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Improving Machine Learning Outcomes Focusing on Framing, Timing, and Targets
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Text data is a powerful a useful source of signal in machine learning systems. There are a set of very standard approaches to dealing with raw text to prepare it for machine learning and statistics. We routinely use: bag of words and n-gram models, TF-IDF, topic modelling or potentially word embeddings derived from one of the neural network language models like Word2Vec, GloVe or fastText. All of these approaches have proven themselves as effective text pre-processing techniques that require no domain knowledge and are widely applicable.
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Text data is a fascinating source of information for data scientists. It can betray subtle clues as to the mood, motives and behaviours of people, in both conscious and unconscious expressions. We can extract text from a wide variety of sources: internal documents, email records, web forms, social media posts, and even the text descriptions from financial transactions.
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Summarizing data is one of those small tasks that data scientists and analysts need to do routinely. However, we often need to write bespoke scripts to get exactly what we want, coping with missing values and assorted data types. We then need to go through a tedious process to format it for sharing or publication.