test: stabilize E2E coverage and GOAP fallback logic

- Refactored 'test_navigation_resilience.py' to produce structurally valid mock XML dynamically responding to app_start resets.
- Patched 'bot_flow.py' interaction lifecycle to handle cognitively deficient test scenarios gracefully.
- Migrated 'device_facade_full.py' assertions to shell-first interaction schemas (adb shell input).
- Stabilized legacy unit tests against structurally strict 'TelepathicEngine' dimension checks.

GramAddict/core/bot_flow.py

@@ -493,11 +493,14 @@ def _interact_with_carousel(device, configs, sleep_mod, logger):
             
         sleep(random.uniform(1.0, 2.0) * sleep_mod)
 
-def _interact_with_profile(device, configs, username, session_state, sleep_mod, logger, cognitive_stack):
-    growth = cognitive_stack.get("growth_brain")
+def _interact_with_profile(device, configs, username, session_state, sleep_mod, logger, cognitive_stack=None):
     """Deep interaction on a profile: Stories, Grid Likes, Follows"""
     import random
     
+    if cognitive_stack is None:
+        cognitive_stack = {}
+    growth = cognitive_stack.get("growth_brain")
+    
     if hasattr(session_state, 'my_username') and username == session_state.my_username:
         logger.info(f"🤝 [Deep Interaction] Skipping own profile @{username} to prevent self-interactions.")
         return
@@ -625,7 +628,7 @@ def _interact_with_profile(device, configs, username, session_state, sleep_mod,
                 is_liked = "gefällt mir nicht mehr" in xml_dump_lower or "unlike" in xml_dump_lower or 'content-desc="liked"' in xml_dump_lower
                 
                 # Uset Double-Tap ~40% of the time, only on standard images
-                use_double_tap = growth.wants_to_double_tap(is_reel=is_reel)
+                use_double_tap = growth.wants_to_double_tap(is_reel=is_reel) if growth else False
 
                 if use_double_tap:
                     if is_liked:

GramAddict/core/resonance_engine.py

@@ -173,6 +173,23 @@ class ResonanceEngine:
         return current_score
 
 
+    def judge_interaction(self, score: float) -> bool:
+        """
+        Binary engagement gate.
+
+        Returns True if the resonance score is high enough to warrant any
+        interaction (like, comment, profile visit).  The threshold mirrors
+        the like-gate used in the feed loop (bot_flow.py, res_score >= 0.35).
+
+        Args:
+            score: Resonance score in [0.0, 1.0] as returned by calculate_resonance().
+
+        Returns:
+            True  → score qualifies for engagement.
+            False → score is too low; skip this post.
+        """
+        return score >= 0.35
+
     def get_suggested_action(self, username: str, base_resonance: float) -> str:
         """
         [Phase 2] High-fidelity relationship escalation.