diff --git a/benchmark_fces_vs_adam.py b/benchmark_fces_vs_adam.py
index 6760593..17facfa 100644
--- a/benchmark_fces_vs_adam.py
+++ b/benchmark_fces_vs_adam.py
@@ -16,6 +16,11 @@ from send_telemetry import push_to_mariadb, push_to_surrealdb  # noqa: E402
 from transformers import AutoModelForCausalLM, AutoTokenizer  # noqa: E402
 from parasitic_qlora import ParasiticQLoRAExtractor, QLoRAConfig  # noqa: E402
 from expert_manifold_alignment import ExpertManifoldAligner  # noqa: E402
+from representation_engineering import (  # noqa: E402
+    PlaybookParser,
+    RepresentationVectorExtractor,
+    SkillVectorLibrary,
+)
 
 # ==============================================================================
 # 1. DSPY SIGNATURE & SYSTEM DESIGN
@@ -278,6 +283,27 @@ def train_run(
     library_path = f"parasitic_adapters_{optimizer_name.lower()}_step{steps}.pt"
     extractor.save_library(library_path)
 
+    # Extract and save Skill representation vectors
+    print(f"[{optimizer_name}] Extracting Skill representation vectors...")
+    try:
+        skills_dir = "C:/Users/Sven/Documents/svenco-knowledge/skills"
+        playbooks = PlaybookParser.parse_directory(skills_dir)
+        print(f"[{optimizer_name}] Found {len(playbooks)} playbooks in {skills_dir}")
+
+        rep_extractor = RepresentationVectorExtractor(model, tokenizer, device)
+        skill_lib = SkillVectorLibrary()
+
+        for pb in playbooks:
+            print(f"[{optimizer_name}] Extracting steering vector for skill: {pb.name}")
+            vec = rep_extractor.extract_steering_vector(pb)
+            skill_lib.add_vector(vec)
+
+        skill_lib_path = f"skill_library_{optimizer_name.lower()}.pt"
+        skill_lib.save(skill_lib_path)
+        print(f"[{optimizer_name}] Saved SkillVectorLibrary to {skill_lib_path}")
+    except Exception as e:
+        print(f"[{optimizer_name}] Error extracting skill representation vectors: {e}")
+
     # 4. Post-Training Evaluation
     print(f"[{optimizer_name}] Running Post-Training Evaluation...")
     post_eval = evaluate_model(model, tokenizer, device)