Executive Summary / TL;DR Fable 5 tracing pipelines often break silently in Colab due to GPU memory fragmentation and non-deterministic tool‑call payloads. We built a 5‑step bulldozer: deterministic parsing, data auditing, zero‑loss serialization, baseline‑friendly formatting, and conda‑isolated training. Every step is backed by battle‑tested YAML configs and CLI checks; nothing left to chance. By the end, you’ll own a repeatable Fable 5 Traces workflow that survives Colab’s 12‑hour runtime cap and yields clean baselines for model comparison. We’ve all been burned by flaky ML pipelines that vomit trace buffers right when you need a reproducible baseline. Last sprint, our team was debugging an agent swarm that used Fable 5’s tracing mesh to log every tool invocation across 8 parallel threads. The raw traces were terabytes of JSONL chaos. We needed a Colab‑based heater that would parse those traces, strip the noise, and spit out a training‑ready dataset – without melting the ...