restructure: folklore-first, quote-verified, with wassname intro

Reorder around what's durable, per wassname's curation:
- human-written intro up top; rename to "wassname's ML Debugging Folklore"
- mindset first: calibrate -> mental models -> Part 1 general tricks (kept,
  they're well-based) -> read a working implementation when stuck
- a Folklore section built from verbatim, source-checked quotes (Jones,
  Rahtz, Karpathy, Schulman, Henderson, Irpan, CS231n, Slavv, Goodfellow),
  each footnoted to the canonical URL + the cached copy with line numbers
- LLM-agent babysitting (debugging loop, triage menu, anti-patterns) moved to
  the bottom where it belongs; triage reframed as a menu, not a flowchart
- deeper one-off tricks split to refs/ (loss_surface, metric_stuck, sweeps),
  scrubbed of private tooling (wandb/just/SI/personal scripts)

Quote integrity: every quote independently verified by fresh-eyes subagents
against the cached sources; fixed a reformatted Schulman slide, a truncated
Jones sentence, a reversed-order Rahtz stitch, a falsely-quoted Slavv phrase,
and the 3e-4 line (now the real tweet, framed as the joke Karpathy confirmed
it was, not gospel). lr_scheduler anti-pattern nuanced (warmup/cyclic matter).

Remove superseded SKILL2.md draft.

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>

README.md

@@ -1,4 +1,6 @@
-# ML Debugging Folklore
+# 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.
 
 Practitioner knowledge for debugging ML systems, curated and synthesized by [wassname](https://github.com/wassname). Opinionated by source selection -- I picked sources I trust (Schulman, Goodfellow, CS231n, ...) and had an LLM extract the most relevant information for debugging ML systems.
 
@@ -12,6 +14,6 @@ Or paste `SKILL.md` into your system prompt / context when debugging.
 
 ## What's here
 
-- **[SKILL.md](SKILL.md)** -- the main artifact. Load into an LLM agent's context as a debugging skill. Parts 1-5 are reference knowledge; Part 6 is a runnable triage protocol (grep patterns, diagnostic snippets, decision tree); Part 7 is debugging mental models and practitioner priors.
+- **[SKILL.md](SKILL.md)** -- the main artifact. Load into an LLM agent's context as a debugging skill. Leads with the mindset (calibrate, mental models, general debugging tricks, and reading a working implementation when stuck), then a folklore section of sourced quotes, then an LLM-agent playbook (debugging loop, triage menu, anti-patterns). Deeper one-off tricks (loss-surface analysis, stuck-metric diagnosis, sweep reliability) live in [refs/](refs/).
 
 - **[docs/evidence/](docs/evidence/)** -- frozen local copies of source material (blog posts, talks, papers, reddit threads). Claims in SKILL.md link back to exact quotes here.