feat(llm): extract adapter layer for standalone llm-connect package (S1+S2)

Stage 1 — Decouple:
- Move RunConfig + LLMResponse to markitect/llm/models.py (canonical)
- Move LLMAdapter + Mock/ErrorLLMAdapter to markitect/llm/adapter.py
- markitect/prompts/execution/models.py and llm_adapter.py become re-export shims
- All 4 adapters + factory.py updated to import from markitect.llm.*
- Parameterize app_name in toml_config.py (resolve_llm, get_default_layers,
  get_preference_layers): paths and env var now derived from app_name arg
- Add tests/test_llm_isolation.py: 7 isolation + backward-compat tests

Stage 2 — Extract:
- Standalone llm-connect package created at ~/llm-connect/
- All 18 llm files copied; markitect.* imports replaced with llm_connect.*
- LLMError base inlined in llm_connect/exceptions.py (no markitect dep)
- llm-connect installed into markitect-venv; declared in pyproject.toml

Smoke test: markitect llm-check succeeds (live Gemini API call).
Backward compat: markitect.prompts.execution.{models,llm_adapter} still work.

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

markitect/llm/adapter.py

@@ -0,0 +1,169 @@
+"""
+LLM adapter interface for pluggable model providers.
+
+Implements abstraction layer for LLM integration, supporting
+multiple providers (OpenAI, Anthropic, local models, etc.).
+"""
+
+from abc import ABC, abstractmethod
+from typing import Dict, Any
+
+from markitect.llm.models import RunConfig, LLMResponse
+
+
+class LLMAdapter(ABC):
+    """
+    Abstract base class for LLM providers.
+
+    Enables pluggable LLM backends without prescribing implementation.
+    Implementations can wrap OpenAI, Anthropic, or other APIs.
+    """
+
+    @abstractmethod
+    def execute_prompt(
+        self,
+        prompt: str,
+        config: RunConfig,
+    ) -> LLMResponse:
+        """
+        Execute a prompt with the LLM.
+
+        Args:
+            prompt: Compiled prompt text
+            config: Execution configuration
+
+        Returns:
+            LLMResponse with generated content
+
+        Raises:
+            Exception: On LLM API errors
+        """
+        pass
+
+    @abstractmethod
+    def validate_config(self, config: RunConfig) -> bool:
+        """
+        Validate that configuration is supported.
+
+        Args:
+            config: Configuration to validate
+
+        Returns:
+            True if valid, False otherwise
+        """
+        pass
+
+
+class MockLLMAdapter(LLMAdapter):
+    """
+    Mock LLM adapter for testing.
+
+    Returns deterministic responses without calling external APIs.
+    """
+
+    def __init__(self, mock_response: str = "Mock LLM response"):
+        """
+        Initialize mock adapter.
+
+        Args:
+            mock_response: Response to return
+        """
+        self.mock_response = mock_response
+        self.call_count = 0
+        self.last_prompt = None
+        self.last_config = None
+
+    def execute_prompt(
+        self,
+        prompt: str,
+        config: RunConfig,
+    ) -> LLMResponse:
+        """
+        Return mock response.
+
+        Args:
+            prompt: Prompt (stored for inspection)
+            config: Config (stored for inspection)
+
+        Returns:
+            Mock LLMResponse
+        """
+        self.call_count += 1
+        self.last_prompt = prompt
+        self.last_config = config
+
+        return 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()),
+            },
+            finish_reason="stop",
+            metadata={"mock": True},
+        )
+
+    def validate_config(self, config: RunConfig) -> bool:
+        """
+        Mock validation always succeeds.
+
+        Args:
+            config: Configuration
+
+        Returns:
+            Always True
+        """
+        return True
+
+    def reset(self) -> None:
+        """Reset mock state."""
+        self.call_count = 0
+        self.last_prompt = None
+        self.last_config = None
+
+
+class ErrorLLMAdapter(LLMAdapter):
+    """
+    Mock adapter that always raises an error.
+
+    Useful for testing error handling.
+    """
+
+    def __init__(self, error_message: str = "Mock LLM error"):
+        """
+        Initialize error adapter.
+
+        Args:
+            error_message: Error message to raise
+        """
+        self.error_message = error_message
+
+    def execute_prompt(
+        self,
+        prompt: str,
+        config: RunConfig,
+    ) -> LLMResponse:
+        """
+        Raise error.
+
+        Args:
+            prompt: Prompt
+            config: Config
+
+        Raises:
+            RuntimeError: Always
+        """
+        raise RuntimeError(self.error_message)
+
+    def validate_config(self, config: RunConfig) -> bool:
+        """
+        Validation succeeds.
+
+        Args:
+            config: Configuration
+
+        Returns:
+            True
+        """
+        return True