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>
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@@ -11,6 +11,8 @@ from storage.artifact_store import ArtifactStore
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class TimelineRecord:
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index: int
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scene: dict[str, Any]
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# The prompt actually sent to the LLM for this step. For non-LLM planners
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# (or older records persisted before D9), this falls back to the task goal.
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prompt: str
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tool_call: dict[str, Any]
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result: dict[str, Any]
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@@ -60,3 +62,6 @@ class Timeline:
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def read(self, task_id: str) -> list[dict[str, Any]]:
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return self.artifact_store.read_steps(task_id)
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def delete_task(self, task_id: str) -> None:
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"""Delete all timeline records and screenshots for a task."""
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self.artifact_store.delete_task(task_id)
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