feat(simulation): implement UserSimAgent for human-like interaction

simulation/user_agent.py

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+import time
+import random
+import ai_client
+
+class UserSimAgent:
+    def __init__(self, hook_client, model="gemini-2.0-flash"):
+        self.hook_client = hook_client
+        self.model = model
+        self.system_prompt = (
+            "You are a software engineer testing an AI coding assistant called 'Manual Slop'. "
+            "You want to build a small Python project and verify the assistant's capabilities. "
+            "Keep your responses concise and human-like. "
+            "Do not use markdown blocks for your main message unless you are providing code."
+        )
+
+    def generate_response(self, conversation_history):
+        """
+        Generates a human-like response based on the conversation history.
+        conversation_history: list of dicts with 'role' and 'content'
+        """
+        # Format history for ai_client
+        # ai_client expects md_content and user_message.
+        # It handles its own internal history.
+        # We want the 'User AI' to have context of what the 'Assistant AI' said.
+        
+        # For now, let's just use the last message from Assistant as the prompt.
+        last_ai_msg = ""
+        for entry in reversed(conversation_history):
+            if entry.get('role') == 'AI':
+                last_ai_msg = entry.get('content', '')
+                break
+        
+        # We need to set a custom system prompt for the User Simulator
+        ai_client.set_custom_system_prompt(self.system_prompt)
+        
+        # We'll use a blank md_content for now as the 'User' doesn't need to read its own files 
+        # via the same mechanism, but we could provide it if needed.
+        response = ai_client.send(md_content="", user_message=last_ai_msg)
+        return response
+
+    def perform_action_with_delay(self, action_func, *args, **kwargs):
+        """
+        Executes an action with a human-like delay.
+        """
+        delay = random.uniform(0.5, 2.0)
+        time.sleep(delay)
+        return action_func(*args, **kwargs)