feat: surface task execution progress across Host Agent and Cloud

Host Agent now persists step-level execution detail locally (via a real
TaskMetadataStore/Timeline wired into TaskRunner) and reports a bounded
in-progress snapshot piggybacked on lease renewal. Cloud persists that
snapshot per active assignment and exposes it through the existing task
list/detail query path; Cloud Console renders it as a live badge. Host
Agent's local console gains authenticated, read-only task list and
detail/timeline pages (same-origin, server-rendered) with inlined
screenshots.

Also fixes a pre-existing gap in the shared Timeline: the actual
per-step LLM prompt is now recorded instead of the task goal, benefiting
both Runtime and Host Agent consoles. When a host uses the cloud planner
transport, each decide call's prompt and resulting tool decision are
durably logged in a new planner_decision_log table (with bounded
retention) and browsable from Cloud Console; direct-transport hosts
explicitly surface a "not reported" state.

Includes Alembic migrations 0008 (progress columns on scheduled_tasks)
and 0009 (planner_decision_log), bounded Host-Agent-local retention,
dual-backend repository parity, and Vitest + pytest coverage. Task 6.5
(manual end-to-end device verification) remains.

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

runtime/ai_planner.py

@@ -36,13 +36,14 @@ class AIPlanner(Planner):
         world: "WorldState | None" = None,
         screenshot: bytes | None = None,
     ) -> list[PlannedStep]:
+        user_prompt = planner_user_prompt(
+            goal=goal,
+            scene_json=scene.to_dict(),
+            history_summary=_history_summary(world),
+        )
         decision = self.client.decide(
             system_prompt=PLANNER_SYSTEM_PROMPT,
-            user_prompt=planner_user_prompt(
-                goal=goal,
-                scene_json=scene.to_dict(),
-                history_summary=_history_summary(world),
-            ),
+            user_prompt=user_prompt,
             screenshot=screenshot,
             tools=ALL_TOOL_SPECS,
             timeout=self.config.timeout,
@@ -58,6 +59,7 @@ class AIPlanner(Planner):
                 action=decision.tool_name,
                 description=f"AI planner: {decision.tool_name}({decision.arguments})",
                 args=dict(decision.arguments),
+                prompt=decision.user_prompt or user_prompt,
             )
         ]