Based solely on the provided article body, this tutorial works with the Fable 5 Traces dataset from Hugging Face and builds a complete workflow around real coding-agent trace data. It starts by setting up a lightweight environment that avoids fragile dependencies such as datasets, scikit-learn, and scipy, then manually downloads and parses the merged JSONL file to keep the notebook stable in Colab. From there, it inspects repository files, previews raw trace examples, normalizes tool calls and text outputs, audits the dataset structure, detects potential secret-like patterns, and visualizes key distributions, including output types, tools, source roots, and text lengths. It also creates safe no-CoT chat/SFT exports, builds a simple keyword-search helper, and trains pure-Python Naive Bayes baselines to assess whether trace context can predict the assistant’s output type and tool usage.