refactor: simplify post-WP-0002 cleanup

- Remove redundant async_execute_prompt overrides from OpenAI/Gemini/OpenRouter
  adapters (identical to base class default — asyncio import also removed)
- Cache prompt.split() result in MockLLMAdapter to avoid double evaluation
- Promote deferred LLMBudgetExceededError imports to module level in
  models.py and adapter.py (no circular dependency)
- Auto-populate context dict in LLMBudgetExceededError.__init__ so callers
  need not pass redundant context= kwarg

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

llm_connect/adapter.py

@@ -10,6 +10,7 @@ from abc import ABC, abstractmethod
 from typing import Dict, Any
 
 from llm_connect.models import RunConfig, LLMResponse, BudgetTracker
+from llm_connect.exceptions import LLMBudgetExceededError
 
 
 class LLMAdapter(ABC):
@@ -79,14 +80,12 @@ class LLMAdapter(ABC):
     def _preflight_budget(self, config: RunConfig) -> None:
         """Raise ``LLMBudgetExceededError`` if the budget is already exhausted."""
         if config.budget_tracker is not None and config.budget_tracker.remaining() == 0:
-            from llm_connect.exceptions import LLMBudgetExceededError
             tracker = config.budget_tracker
             raise LLMBudgetExceededError(
                 "Token budget exhausted before making request",
                 total=tracker.total,
                 spent=tracker.spent,
                 requested=0,
-                context={"total": tracker.total, "spent": tracker.spent},
             )
 
     def _consume_budget(self, config: RunConfig, response: LLMResponse) -> None:
@@ -135,13 +134,15 @@ class MockLLMAdapter(LLMAdapter):
         self.last_prompt = prompt
         self.last_config = config
 
+        prompt_tokens = len(prompt.split())
+        completion_tokens = len(self.mock_response.split())
         response = LLMResponse(
             content=self.mock_response,
             model=config.model_name,
             usage={
-                "prompt_tokens": len(prompt.split()),
-                "completion_tokens": len(self.mock_response.split()),
-                "total_tokens": len(prompt.split()) + len(self.mock_response.split()),
+                "prompt_tokens": prompt_tokens,
+                "completion_tokens": completion_tokens,
+                "total_tokens": prompt_tokens + completion_tokens,
             },
             finish_reason="stop",
             metadata={"mock": True},