清理 EA 過期行動隊列

tests/test_action_plan_hygiene.py

@@ -0,0 +1,49 @@
+from datetime import datetime, timedelta
+
+
+def test_action_plan_hygiene_preview_closes_only_stale_advisory_sources():
+    from services.action_plan_hygiene import build_action_plan_hygiene_preview
+
+    now = datetime(2026, 5, 19, 12, 0, 0)
+    rows = [
+        {
+            "id": 1,
+            "status": "pending",
+            "priority": 1,
+            "created_at": now - timedelta(hours=100),
+            "action_type": "openclaw_recommendation",
+            "description": "old advisory",
+        },
+        {
+            "id": 2,
+            "status": "auto_pending",
+            "priority": 1,
+            "created_at": now - timedelta(hours=90),
+            "action_type": "code_review_fix",
+            "description": "old code review",
+        },
+        {
+            "id": 3,
+            "status": "pending",
+            "priority": 1,
+            "created_at": now - timedelta(hours=90),
+            "created_by": "nemotron",
+            "description": "must stay",
+        },
+        {
+            "id": 4,
+            "status": "pending",
+            "priority": 1,
+            "created_at": now - timedelta(hours=2),
+            "action_type": "openclaw_recommendation",
+            "description": "fresh advisory",
+        },
+    ]
+
+    preview = build_action_plan_hygiene_preview(rows, now=now, stale_hours=72)
+
+    assert preview["candidate_count"] == 2
+    assert preview["by_source"] == {"openclaw_recommendation": 1, "code_review_fix": 1}
+    assert {item["id"] for item in preview["candidates"]} == {1, 2}
+    target_by_id = {item["id"]: item["to_status"] for item in preview["candidates"]}
+    assert target_by_id == {1: "rejected", 2: "auto_disabled"}

tests/test_elephant_alpha_engine.py

@@ -252,6 +252,7 @@ def test_resource_optimization_bypasses_llm_orchestrator(monkeypatch):
         sent.append(args)
 
     monkeypatch.setattr(engine_module.elephant_orchestrator, "analyze_and_coordinate", _boom)
+    monkeypatch.setattr(engine, "_run_action_plan_hygiene", lambda: {"updated_count": 0})
     monkeypatch.setattr(engine, "_record_resource_pressure_insight", lambda *args, **kwargs: 77)
     monkeypatch.setattr(engine, "_send_resource_pressure_telegram", _capture_send)
     monkeypatch.setattr(engine, "_store_escalation", lambda trigger_type: stored.append(trigger_type))
@@ -280,3 +281,40 @@ def test_resource_optimization_bypasses_llm_orchestrator(monkeypatch):
     assert stored == ["resource_optimization"]
     assert len(sent) == 1
     assert engine.max_autonomous_decisions_per_hour == 8
+
+
+def test_resource_pressure_message_reports_hygiene_result():
+    from services.elephant_alpha_autonomous_engine import ElephantAlphaAutonomousEngine
+
+    metrics = ElephantAlphaAutonomousEngine._classify_resource_pressure({
+        "action_queue_size": 9,
+        "high_priority_count": 0,
+        "human_review_count": 0,
+        "stale_count": 0,
+        "system_load_pct": 20.0,
+        "queue_threshold": 10,
+        "load_threshold_pct": 80,
+        "high_priority_threshold": 5,
+        "stale_threshold": 5,
+    })
+    metrics["pre_hygiene"] = {
+        "action_queue_size": 100,
+        "high_priority_count": 47,
+        "stale_count": 99,
+    }
+    metrics["hygiene_result"] = {
+        "updated_count": 91,
+        "by_source": {"code_review_fix": 66, "openclaw_recommendation": 25},
+    }
+
+    msg = ElephantAlphaAutonomousEngine._build_resource_pressure_telegram_message(
+        metrics,
+        insight_id=124,
+        previous_limit=10,
+        new_limit=10,
+    )
+
+    assert "P4 resolved" in msg
+    assert "清理前 Action queue：100" in msg
+    assert "已自動關閉過期 action_plans 91 筆" in msg
+    assert "只改 status/metadata，不刪除資料" in msg