From 53fb1c4bdab40c1835c076e8f01c308c123330d2 Mon Sep 17 00:00:00 2001
From: "wassname (Michael J Clark)" <1103714+wassname@users.noreply.github.com>
Date: Wed, 3 Jun 2026 08:19:07 +0800
Subject: [PATCH] Update SKILL.md

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 SKILL.md | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

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@@ -5,11 +5,7 @@ description: "Wassname's practical folklore for debugging ML systems: convergenc
 
 # wassname's ML Debugging Folklore
 
-In an attempt to upskill the ML debugging on AI coding assistants (and humans), I've collected high quality sources on ML debugging and the mindset and the "taste". When I started ML I went searching for discussions on best practices, and started a few discussions of my own and they helped me a lot, I hope they can help others. This intro is human written, and the below is AI written with human guidance.
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-Practitioner knowledge for debugging ML systems that's hard to find in papers: convergence stalls, NaNs, gradient pathologies, stuck metrics, sweeps you can trust. It's *folklore*, so the quotes are the content; the people who learned these lessons the hard way say it better than a paraphrase can. Full sources and links are collected at the end.
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-The core problem: errors aren't local. Especially in RL, information flows in a loop (actor -> learner -> actor), so as Jones puts it, "a numerical error in one spot gets smeared throughout the system in seconds, poisoning everything"[^jones]. Most errors then show up as *all* your metrics going weird at once (loss exploding, KL collapsing, rewards oscillating), so you can tell something's wrong but not *what* or *where*. That's why the discipline here leads with calibration and clue-collection before any fix.
+Foreword: In an attempt to upskill the ML debugging on AI coding assistants (and humans), I've collected high quality sources on ML debugging and the mindset and the "taste". When I started ML I went searching for discussions on best practices, and started a few discussions of my own and they helped me a lot, I hope they can help others. This intro is human written, and the below is AI written with human guidance.
 
 ## Before you debug: calibrate