Initial commit: Hold'em engine, bots, and simulation harness

Kotlin Multiplatform engine (JVM target only for now; androidTarget and
iosArm64 slot in without touching commonMain).

Core:
- HandEvaluator: single-pass 5-7 card evaluation, ~24M evals/sec. Verified
  exhaustively against published frequencies for all 2,598,960 five-card hands.
- Equity: Monte Carlo with ties split. PreflopChart ranks the 169 starting
  hands using all-in equity plus an explicit playability adjustment, so
  looseness means "plays the top N%".
- Table: no-limit betting rounds, side pots, odd-chip splits, uncalled-bet
  refunds, and incomplete (short all-in) raises that correctly do not reopen
  betting.

Bots:
- SkillLevel and PlayStyle are orthogonal axes. Skill drives decision quality
  (rollout accuracy, pot-odds discipline, position awareness, error rate);
  style drives bluffing, sandbagging, aggression, tightness.
- BotMood gives tilt that persists between hands and decays.
- OpponentModel lets Advanced/Expert exploit habitual bettors.
- MathBot emits a DecisionTrace of the numbers behind each decision, which the
  coach will later hand to an LLM to narrate. The LLM never does poker maths.

Simulator:
- 2,200-3,400 hands/sec. Deck RNG is separate from bot RNGs so rollout counts
  cannot shift the deal.
- Controlled skill-ladder test asserts the difficulty gradient is monotonic:
  73.9 / 53.9 / 27.6 / -155.4 bb/100 over 50k hands.

Assets: 52 CC0 English-pattern card faces plus generated backs.

Tests: 30 passing (evaluator, table rules, pre-flop chart).

Known open: win-rate magnitudes ~10x realistic and several profiles looser
than their labels. Tuning, not correctness.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

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