feat: initial CompanionGuard-RL framework

Two-module pipeline for AI companion safety:
- Module B: context-aware risk detector with CrossAttention fusion
- Module C: PPO-based adaptive intervention policy

Includes CompanionRisk Taxonomy (10 primary + 14 fine-grained labels),
dataset generation/annotation pipeline, training scripts, and eval suite.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

scripts/generate_data.py

@@ -0,0 +1,40 @@
+"""
+Step 1: Generate companion conversation dataset using LLM.
+
+Usage:
+  python scripts/generate_data.py --config configs/data_generation.yaml
+"""
+
+import argparse
+import yaml
+from pathlib import Path
+from src.data.data_generator import ConversationGenerator
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", default="configs/data_generation.yaml")
+    args = parser.parse_args()
+
+    with open(args.config) as f:
+        cfg = yaml.safe_load(f)
+
+    Path(cfg["output"]["raw_dir"]).mkdir(parents=True, exist_ok=True)
+
+    generator = ConversationGenerator(
+        api_type=cfg["api"]["type"],
+        model=cfg["api"]["model"],
+    )
+
+    count = generator.generate_dataset(
+        output_path=cfg["output"]["output_file"],
+        total_samples=cfg["generation"]["total_samples"],
+        samples_per_category=cfg["generation"]["samples_per_category"],
+        delay=cfg["generation"]["delay"],
+    )
+
+    print(f"Generated {count} samples → {cfg['output']['output_file']}")
+
+
+if __name__ == "__main__":
+    main()