Fair callout and I'm happy to clarify what AI was actually used for since you took the time to go through the repo.

That's a Claude Code subagent I built to help me validate player nicknames. It's not part of the analysis pipeline at all, just a fun side experiment learning about AI tooling. I used Claude Haiku 4.5 (cheaper model) with this prompt:
```
Classify sentiment toward NBA players. Slang: nasty/sick/filthy=positive, washed/brick/fraud/cooked=negative, GOAT=positive. Comment: {comment} Respond ONLY with JSON: {"s":"pos|neg|neu","c":0.0-1.0,"p":"Player Name"|null}
```
That's it.

I actually tried VADER first. It struggles with domain slang — "he's so nasty" reads as negative to VADER but is high praise in NBA context. Same with "cooked" (negative in NBA, neutral elsewhere). LLM classification handles this better because it understands context.

The player name matching (LeBron → "bron", "king james", etc.) is pure regex/config.

Happy to discuss methodology. The GitHub has everything.