feat(runtime): add planner reflection history with rationale and thinking

- ToolCallDecision captures thinking blocks and pre-tool text output
- AnthropicToolCallingClient supports optional extended thinking (budget_tokens + beta header)
- PlannedStep carries rationale and thinking from each LLM decision
- WorldEvent replaces scene_summary with rationale/thinking/page fields (backward-compatible)
- AI planner system prompt instructs reflection before each tool call
- _history_summary() emits compact {page, rationale, action, success} dicts
- Cloud DB migration 0011 adds nullable rationale/thinking columns to planner_decision_log
- OpenAI client extracts reasoning_content into thinking field

runtime/planner_prompts.py

@@ -10,7 +10,13 @@ list of UI elements with id, type, text, and pixel bounds), and — when
 available — a screenshot of the same screen and a short history of recent
 actions and their outcomes.
 
-You must call exactly one tool per turn:
+Before calling a tool, output a short text block (1-2 sentences):
+1. If this is the first step, state what you intend to do and why.
+2. Otherwise, first assess whether the previous action achieved its intended
+   effect based on the current screen, then state the intent of your next action.
+Keep this reflection concise and factual.
+
+You must then call exactly one tool:
 - One of `tap`, `swipe`, `input_text`, `launch_app`, `terminate_app` to make
   progress toward the goal.
 - `finish_task` when the goal has been reached, or when it cannot be reached