feat(intelligent-eval): platform audit backfill for decision logs + disable legacy cron workers

- _supplement_decision_logs: executing evals missing a decision log get a
  platform-derived execute_session (deficit) or start_analysis (all sessions
  done) entry. Audit backfill only — records observable state, does not change
  agent execution. Called each scan tick after requeue+scan.
- t480 legacy cron workers (5) disabled: superseded by platform-triggered
  headless agent (plan C); they kept firing every minute and failing on
  Channel-required.

backend/agenteval/web/app.py

@@ -42,6 +42,60 @@ def _has_pending_task() -> bool:
         session.close()
 
 
+def _supplement_decision_logs(session) -> int:
+    """Platform audit backfill for decision logs.
+
+    方案③的决策日志由 OpenClaw agent 上报（LLM 自主，尽力而为）——异常路径
+    （如卡死恢复后重试）agent 可能跳过上报，导致决策过程页面为空。这里按评估
+    状态推导决策并补录：欠账时补 execute_session，所有会话完成后补
+    start_analysis。只补"该类型缺失"的，不重复；且只记录状态，不改变 agent
+    的实际执行。
+
+    Returns:
+        补录的决策日志条数。
+    """
+    from agenteval.intelligent_eval.decision_logs import create_decision_log
+    from agenteval.intelligent_eval.models import IntelligentEvalStatus
+    from agenteval.storage.db import IntelligentEvalDB, IntelligentEvalDecisionLogDB, IntelligentEvalSessionDB
+    from sqlmodel import select
+
+    evals = session.exec(
+        select(IntelligentEvalDB).where(IntelligentEvalDB.status == IntelligentEvalStatus.EXECUTING.value)
+    ).all()
+
+    added = 0
+    for ev in evals:
+        plan = ev.get_plan() if ev.plan else {}
+        estimated = plan.get("estimated_sessions", 0)
+        sessions = session.exec(
+            select(IntelligentEvalSessionDB).where(IntelligentEvalSessionDB.eval_id == ev.id)
+        ).all()
+        completed = sum(1 for s in sessions if s.status == "completed")
+        types = {
+            l.decision_type
+            for l in session.exec(
+                select(IntelligentEvalDecisionLogDB).where(
+                    IntelligentEvalDecisionLogDB.eval_id == ev.id
+                )
+            ).all()
+        }
+        if "execute_session" not in types and completed < estimated:
+            create_decision_log(
+                ev.id, "execute_session", "平台兜底：时段欠账需执行会话", "platform",
+                {"platform_supplemented": True, "completed": completed, "estimated": estimated},
+                session,
+            )
+            added += 1
+        elif "start_analysis" not in types and sessions and completed >= estimated:
+            create_decision_log(
+                ev.id, "start_analysis", "平台兜底：所有会话已完成开始分析", "platform",
+                {"platform_supplemented": True, "completed": completed, "estimated": estimated},
+                session,
+            )
+            added += 1
+    return added
+
+
 async def _trigger_intelligent_worker() -> bool:
     """Trigger OpenClaw's headless agent to run the intelligent-eval worker skill.
 
@@ -111,6 +165,10 @@ async def _intelligent_eval_scan_loop() -> None:
                 n = scan_and_enqueue_tasks(session)
                 if n:
                     _logger.info("智能评估扫描：入队 %d 个 Worker 任务", n)
+                # 审计兜底：agent 未上报决策日志时，平台按评估状态补录
+                added = _supplement_decision_logs(session)
+                if added:
+                    _logger.info("决策日志兜底：补录 %d 条", added)
             finally:
                 session.close()
         except Exception as exc:

tests/integration/test_intelligent_eval_scan_scheduler.py

@@ -108,3 +108,42 @@ def test_trigger_worker_msg_has_execute_semantics(monkeypatch):
     assert "立即完成当前任务" in joined
     assert "agenteval-intelligent-worker" in joined
     assert "agenteval-intelligent-analyst" in joined
+
+
+def test_supplement_execute_session_log(monkeypatch, db_session):
+    """Executing eval with deficit and no execute_session log → platform backfills."""
+    from agenteval.intelligent_eval.models import IntelligentEvalStatus
+    from agenteval.storage.db import IntelligentEvalDB
+    import agenteval.web.app as app_mod
+
+    ev = IntelligentEvalDB(
+        name="supp-eval", target_id="t1",
+        status=IntelligentEvalStatus.EXECUTING.value, started_at=__import__("agenteval.storage.db", fromlist=["utc_now"]).utc_now(),
+    )
+    ev.set_plan({"time_distribution": [{"time_slot": "0-1h", "sessions": 1}], "estimated_sessions": 1})
+    db_session.add(ev)
+    db_session.commit()
+
+    assert app_mod._supplement_decision_logs(db_session) == 1
+    # second call: already backfilled → 0
+    assert app_mod._supplement_decision_logs(db_session) == 0
+
+
+def test_supplement_start_analysis_log(monkeypatch, db_session):
+    """Executing eval with all sessions completed and no start_analysis → backfill."""
+    from agenteval.intelligent_eval.models import IntelligentEvalStatus
+    from agenteval.storage.db import IntelligentEvalDB, IntelligentEvalSessionDB, utc_now
+    import agenteval.web.app as app_mod
+
+    ev = IntelligentEvalDB(
+        name="supp-eval2", target_id="t1",
+        status=IntelligentEvalStatus.EXECUTING.value, started_at=utc_now(),
+    )
+    ev.set_plan({"time_distribution": [{"time_slot": "0-1h", "sessions": 1}], "estimated_sessions": 1})
+    db_session.add(ev)
+    db_session.commit()
+    s = IntelligentEvalSessionDB(eval_id=ev.id, target_id=ev.target_id, status="completed", goal="g")
+    db_session.add(s)
+    db_session.commit()
+
+    assert app_mod._supplement_decision_logs(db_session) == 1