style(no-slop): remove every em-dash + banned words across all modules + capstone

Apply the no-ai-slop standard (now binding in AGENTS.md): the em-dash character is
banned outright (restructured, not blind-replaced), plus the banned word/phrase
list (delve, leverage, robust, seamless, truly, unlock, etc.). 0 em-dashes remain
in modules + capstone; the only "robust" left is the planted M10 ai-change.patch
trap. Module H1 titles use a colon separator.

All deliberate teaching devices preserved; labs compile/parse (py/sh/yaml/json);
no junk. AGENTS.md updated with the hard no-slop rules.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TfzV5QvtPDz8LJS3Pu5VLT

modules/27-evals/lab/llm_judge.py

@@ -34,7 +34,7 @@ def judge(candidate_text: str) -> dict:
     key = os.environ.get("EVAL_JUDGE_KEY")
     model = os.environ.get("EVAL_JUDGE_MODEL")
     if not (url and key and model):
-        return {"score": None, "reason": "judge not configured — abstaining (set EVAL_JUDGE_* to enable)"}
+        return {"score": None, "reason": "judge not configured; abstaining (set EVAL_JUDGE_* to enable)"}
 
     payload = json.dumps({
         "model": model,
@@ -72,7 +72,7 @@ if __name__ == "__main__":
 #     about the candidate changed. The ruler is itself made of rubber.
 #
 # So: use a programmatic grader (run_eval.py) wherever a deterministic check is
-# possible — that is most of the time. Reach for an LLM judge only for genuinely
+# possible; that is most of the time. Reach for an LLM judge only for genuinely
 # open-ended output, and CALIBRATE it first: hand-label ~20 examples yourself,
 # run the judge on them, and confirm it agrees with you before you let it gate
 # anything. An uncalibrated judge is a vibe with a number attached.