feat(official-bots): standalone self-play + one-shot dataset builder for NNUE training

Add an easy local data pipeline feeding GPU training on Colab.

- SelfPlayMain: standalone NNUEBot self-play (no microservices) writing FENs
  for labeling; randomised openings for game diversity, sequential due to the
  shared EvaluationNNUE accumulator. Exposed via the `selfPlay` Gradle task and
  selfplay.sh.
- NNUEBot: optional fixedMoveTimeMs so self-play runs fast (default unchanged).
- NbaiLoader: honor `-Dnnue.weights=<path>` to load weights from a file before
  falling back to the bundled resource.
- build_dataset.py / dataset.sh: one command builds the entire dataset
  (Lichess eval-DB backbone + self-play + tactical + random filler), dedups,
  balances the eval histogram, writes append-only zstd shards + manifest, and
  rclone-pushes to Drive.
- train.py: NNUEDataset reads a directory of .jsonl.zst shards (streaming) in
  addition to a single file.
- NNUETraining.ipynb: clone to ephemeral /content, sync shards from Drive
  (cache-aware), train on the shards dir; removed Colab generation/upload steps.
- Concept + implementation plan docs.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

modules/official-bots/build.gradle.kts

@@ -47,6 +47,14 @@ tasks.withType<JavaCompile> {
     options.compilerArgs.add("-parameters")
 }
 
+tasks.register<JavaExec>("selfPlay") {
+    group = "nnue"
+    description = "Run standalone NNUEBot self-play and write FENs for labeling."
+    mainClass.set("de.nowchess.bot.selfplay.SelfPlayMain")
+    classpath = sourceSets["main"].runtimeClasspath
+    args((project.findProperty("spArgs")?.toString() ?: "").split(" ").filter { it.isNotBlank() })
+}
+
 dependencies {
 
     compileOnly("org.scala-lang:scala3-compiler_3") {