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>

CLAUDE.md

@@ -0,0 +1,68 @@
+# Poker — Texas Hold'em with teachable AI opponents
+
+Kotlin Multiplatform. Ships iOS + Android; Android first (only Android hardware
+for physical testing).
+
+## Build
+
+No `java`/`gradle` on PATH — use Android Studio's bundled JDK:
+
+```bash
+export JAVA_HOME="/Applications/Android Studio.app/Contents/jbr/Contents/Home"
+./gradlew :engine:jvmTest          # evaluator + engine tests
+./gradlew :sim:run --args="50000"  # simulate 50k hands, print bot stats
+```
+
+## Layout
+
+| Path | What |
+|---|---|
+| `engine/src/commonMain/.../core/` | Cards, evaluator, equity, pre-flop chart |
+| `engine/src/commonMain/.../bot/` | Skill/style profiles, `MathBot` |
+| `engine/src/commonMain/.../game/` | `Table` — betting rounds, side pots, showdown |
+| `sim/` | JVM-only headless simulator used to **tune** bot profiles |
+| `assets/cards/` | 52 CC0 card faces + generated backs |
+
+`engine` is pure Kotlin with no platform APIs, so `androidTarget()` /
+`iosArm64()` slot in without touching `commonMain`.
+
+## Design rules
+
+1. **Poker maths never goes near the LLM.** Difficulty and style are engine-side
+   EV/frequency calculations — instant, deterministic, testable, offline. The LLM
+   only narrates numbers the engine already computed (`DecisionTrace`), and adds
+   persona/table talk.
+2. **Skill and style are orthogonal.** `SkillLevel` = how correct decisions are;
+   `PlayStyle` = bluffing, sandbagging, aggression, tightness. Build the strongest
+   bot, then inject *controlled error* for lower tiers.
+3. **Pre-flop is range-based, not equity-based.** All-in equity overvalues trash
+   (7-2o has ~35% vs one random hand but is unplayable). `PreflopChart` ranks the
+   169 starting hands so `looseness` means "plays the top N%".
+4. **The simulator is how bots get tuned.** Run it after any bot change; it prints
+   a controlled skill-ladder test that must stay monotonic.
+
+## Testing notes
+
+- `Table` takes a `CardSource`, so `StackedDeck.of(holes, board)` gives fully
+  deterministic hands. Use it for any rule test.
+- The simulator gives the **deck its own RNG**, separate from each bot's. Never
+  share one: bots consume RNG proportional to their `equityIterations`, so a
+  shared stream means changing a profile silently changes the cards dealt.
+- `./gradlew :sim:run --args="chart"` dumps the starting-hand ranking.
+- Small samples lie. 1,000 hands is not enough to rank profiles — use 50,000+
+  before believing a gradient.
+
+## Status
+
+- Evaluator: verified exhaustively against published frequencies for all
+  2,598,960 five-card hands. ~24M evals/sec.
+- Engine: chip-conserving; side pots, odd-chip splits, uncalled-bet refunds, and
+  incomplete (short all-in) raises all covered by tests.
+- Bots: skill gradient **passes** monotonically (73.9 / 53.9 / 27.6 / −155.4
+  bb/100 at 50k hands).
+- Known-imperfect: win-rate magnitudes are still ~10x realistic, and several
+  profiles are looser than their labels (the Rock plays ~38% VPIP, should be
+  ~12%). Tuning is the open work.
+- Gradle emits an `archives` deprecation from the Kotlin Multiplatform plugin's
+  own `jvm()` target registration — upstream in Kotlin 2.2.10, not our build.
+- Not built yet: LLM persona layer, opt-in coach, Compose UI.