Add safety dashboard and decision transparency

CHANGELOG.md

@@ -1,5 +1,63 @@
 # Changelog
 
+## 1.1.0 - 2026-06-17
+- Dashboard als Einrichtungs- und Visualisierungszentrale erweitert:
+  Job-Queue, Sicherheitsprofil, Entscheidungsakte, Wissen/Annahmen/
+  Unsicherheiten und Beitragsfaktoren pro Aktor.
+- Lokales Safety-Profil pro Aktor eingefuehrt: manuelle Sperre,
+  Freigabestufe, Mindest-Confidence und optionaler Cooldown werden vor
+  autonomem Schalten ausgewertet.
+- Sofort-Schaltpfad bleibt schnell: Safety prueft nur lokale Daten; der
+  Home-Assistant-Serviceaufruf wird nicht durch Discovery, Training oder
+  Statistik blockiert.
+- Sichtbare Job-Queue fuer Discovery, Reconciliation, Training, Evaluation
+  und Automation-Refresh mit Status, Dauer, Fehler und Zusammenfassung.
+- Entscheidungsstatistik erweitert: Sensor-/Kontextfaktoren, aktive
+  Gewichtungen, Sample-/Confidence-Trends und Feedbackzaehler werden
+  persistiert.
+
+## 1.0.5 - 2026-06-17
+- Lange Friendly Names, Entity-IDs, Chips, Tabellenwerte und Metriken brechen
+  im Dashboard responsiv um und laufen nicht mehr aus Karten oder Boxen.
+- Automatisierter Performance-Budget-Test fuer Root-HTML und
+  `/v1/actuators/dashboard` gegen das 5-Sekunden-Limit ergaenzt.
+- HA-/Ingress-Verifikation mit Supervisor-Status, Backup, Watchdog,
+  Hard-Reload und Rollback im Operating Guide dokumentiert.
+
+## 1.0.4 - 2026-06-17
+- Sensor-Relevanz ist in der Aktor-Detailansicht sichtbar: automatische
+  Relevanz, aktive Gewichtung und Score werden pro verwendetem Sensor/Zustand
+  angezeigt.
+- Gewichtungen koennen im Dashboard korrigiert und per API unter
+  `/v1/actuators/{actuator_entity_id}/weights` gespeichert werden.
+- Gruppen-Gewichtungen buendeln mehrere Sensoren/Zustaende fuer einen Aktor,
+  damit verbundene Kontextsignale gemeinsam bewertet werden koennen.
+
+## 1.0.3 - 2026-06-17
+- Header-Menue als Pulldown umgesetzt; die separate Navigationsleiste entfaellt.
+- Geraetegruppen und manuelle Kontextbereiche sind standardmaessig geschlossen.
+- Dashboard startet in Phasen: leere Bedienoberflaeche, dann Status, danach
+  Geraetedaten.
+- Detailansicht oeffnet streamartiger: zuerst Basis-Shell, dann Aktorwerte,
+  danach Kontextvorschlaege.
+
+## 1.0.2 - 2026-06-17
+- v1.0-Abnahme als `docs/V1_0_ACCEPTANCE.md` dokumentiert: erledigte,
+  teilweise erledigte und offene v1.0.x-Punkte sind getrennt sichtbar.
+- Dashboard-Startstatistik erweitert: Freigabebereitschaft, Aktiv/Shadow,
+  Gelernt/Wartet und gelernte Handlungen werden direkt im Startbereich
+  zusammengefasst.
+
+## 1.0.1 - 2026-06-17
+- Dashboard-UI nach v1-Korrektur neu strukturiert: feste Steuerungsleiste,
+  separate Geräteübersicht, klare Freigabe-/Detailfläche und Statusbereich.
+- Orange bleibt Primärfarbe; Cyan ist die sichtbare Komplementärfarbe. Rote
+  Aktions- und Fehlerflächen wurden aus der Oberfläche entfernt.
+- Startpfad weiter beschleunigt: Dashboard lädt nur noch lokale Startdaten.
+  HA-Discovery, Vorschläge und Automation-Refresh laufen erst nach Nutzeraktion.
+- Detailansicht öffnet ohne automatische Automation-Discovery. Passende
+  Automationen können gezielt per Button neu gesucht werden.
+
 ## 1.0.0 - 2026-06-17
 - Neuer blockweiser Dashboard-Start über `/v1/actuators/dashboard`: lokale
   Store-/Cache-Daten laden sofort, HA-Discovery und Vorschläge laufen

README.md

@@ -13,6 +13,10 @@ nach einer ausdrücklichen Freigabe ausführen.
   [`docs/OPERATIONS.md`](docs/OPERATIONS.md)
 - Version 1.0.0 bedienen und prüfen:
   [`docs/V1_0_0_OPERATING_GUIDE.md`](docs/V1_0_0_OPERATING_GUIDE.md)
+- Version 1.0.x Abnahme und offene Punkte:
+  [`docs/V1_0_ACCEPTANCE.md`](docs/V1_0_ACCEPTANCE.md)
+- Version 1.1.0 Safety, Transparenz und Job-Queue:
+  [`docs/V1_1_0_OPERATING_GUIDE.md`](docs/V1_1_0_OPERATING_GUIDE.md)
 - Arbeitsregeln für Coding-Agenten: [`AGENTS.md`](AGENTS.md)
 
 ## Reifegrad

addon/config.yaml

@@ -1,5 +1,5 @@
 name: SillyHome Next
-version: "1.0.0"
+version: "1.1.0"
 slug: sillyhome_next
 description: Lernt automatisch aus deinem Verhalten und steuert freigegebene Aktoren
 url: http://192.168.6.31:3000/pino/sillyhome-next

app/actuators/lifecycle.py

@@ -16,6 +16,7 @@ from app.actuators.models import (
     ManualOverride,
     ModelLifecycleState,
     ReconciliationState,
+    SensorWeightGroup,
     model_id_for_actuator,
 )
 from app.actuators.store import ActuatorStore
@@ -239,6 +240,10 @@ class ActuatorReconciliationService:
         override = ManualOverride(
             numeric_entity_id=numeric_entity_id,
             context_entity_ids=selected_context_ids,
+            sensor_weights=record.manual_override.sensor_weights if record.manual_override else {},
+            sensor_weight_groups=(
+                record.manual_override.sensor_weight_groups if record.manual_override else []
+            ),
             updated_at=now,
             note=note,
         )
@@ -253,17 +258,23 @@ class ActuatorReconciliationService:
             update={
                 "assignment": assignment,
                 "manual_override": override,
-                "numeric_candidates": _merge_manual_candidates(
-                    record.numeric_candidates,
-                    entities,
-                    [numeric_entity_id] if numeric_entity_id else [],
-                    role=EntityRole.MEASUREMENT,
+                "numeric_candidates": _apply_weight_overrides(
+                    _merge_manual_candidates(
+                        record.numeric_candidates,
+                        entities,
+                        [numeric_entity_id] if numeric_entity_id else [],
+                        role=EntityRole.MEASUREMENT,
+                    ),
+                    override,
                 ),
-                "context_candidates": _merge_manual_candidates(
-                    record.context_candidates,
-                    entities,
-                    selected_context_ids,
-                    role=EntityRole.CONTEXT,
+                "context_candidates": _apply_weight_overrides(
+                    _merge_manual_candidates(
+                        record.context_candidates,
+                        entities,
+                        selected_context_ids,
+                        role=EntityRole.CONTEXT,
+                    ),
+                    override,
                 ),
                 "lifecycle": lifecycle,
                 "updated_at": now,
@@ -271,6 +282,65 @@ class ActuatorReconciliationService:
         )
         return self._store.upsert(updated)
 
+    def set_weight_overrides(
+        self,
+        actuator_entity_id: str,
+        *,
+        sensor_weights: dict[str, float],
+        sensor_weight_groups: list[SensorWeightGroup],
+        note: str | None = None,
+    ) -> ActuatorRecord:
+        now = datetime.now(timezone.utc)
+        record = self._store.get(actuator_entity_id)
+        selected_ids = {
+            entity_id
+            for entity_id in [
+                record.assignment.selected_numeric_entity_id,
+                *record.assignment.selected_context_entity_ids,
+            ]
+            if entity_id
+        }
+        selected_ids.update(sensor_weights)
+        for group in sensor_weight_groups:
+            selected_ids.update(group.entity_ids)
+        entities = {entity.entity_id: entity for entity in self._ha_reader.read_entities()}
+        missing = [entity_id for entity_id in selected_ids if entity_id not in entities]
+        if missing:
+            raise ValueError(f"Unbekannte Home-Assistant-Entity: {', '.join(sorted(missing))}")
+
+        previous = record.manual_override
+        override = ManualOverride(
+            numeric_entity_id=(
+                previous.numeric_entity_id
+                if previous is not None
+                else record.assignment.selected_numeric_entity_id
+            ),
+            context_entity_ids=(
+                previous.context_entity_ids
+                if previous is not None
+                else record.assignment.selected_context_entity_ids
+            ),
+            sensor_weights={entity_id: round(weight, 4) for entity_id, weight in sensor_weights.items()},
+            sensor_weight_groups=sensor_weight_groups,
+            updated_at=now,
+            note=note,
+        )
+        updated = record.model_copy(
+            update={
+                "manual_override": override,
+                "numeric_candidates": _apply_weight_overrides(
+                    record.numeric_candidates,
+                    override,
+                ),
+                "context_candidates": _apply_weight_overrides(
+                    record.context_candidates,
+                    override,
+                ),
+                "updated_at": now,
+            }
+        )
+        return self._store.upsert(updated)
+
     def reconcile_all(self, trigger: str = "manual") -> ReconciliationState:
         state = self._store.load_reconciliation_state().model_copy(
             update={
@@ -376,6 +446,9 @@ class ActuatorReconciliationService:
             ),
             context=True,
         )
+        if record.manual_override is not None:
+            numeric_candidates = _apply_weight_overrides(numeric_candidates, record.manual_override)
+            context_candidates = _apply_weight_overrides(context_candidates, record.manual_override)
         assignment = (
             self._manual_assignment(record.manual_override)
             if record.manual_override is not None
@@ -918,6 +991,41 @@ def _merge_manual_candidates(
     return sorted(by_id.values(), key=lambda item: (-item.confidence, item.entity_id))
 
 
+def _apply_weight_overrides(
+    candidates: list[AssignmentCandidate],
+    override: ManualOverride,
+) -> list[AssignmentCandidate]:
+    if not override.sensor_weights and not override.sensor_weight_groups:
+        return candidates
+    group_weights: dict[str, float] = {}
+    for group in override.sensor_weight_groups:
+        for entity_id in group.entity_ids:
+            group_weights[entity_id] = max(group_weights.get(entity_id, 0.0), group.weight)
+    weighted: list[AssignmentCandidate] = []
+    for candidate in candidates:
+        explicit = override.sensor_weights.get(candidate.entity_id)
+        group_weight = group_weights.get(candidate.entity_id)
+        manual_weight = explicit if explicit is not None else group_weight
+        effective_weight = manual_weight if manual_weight is not None else 1.0
+        evidence = [
+            item
+            for item in candidate.evidence
+            if not item.startswith("Manuelle Gewichtung:")
+        ]
+        if manual_weight is not None:
+            evidence.append(f"Manuelle Gewichtung: {round(manual_weight * 100)} %.")
+        weighted.append(
+            candidate.model_copy(
+                update={
+                    "manual_weight": manual_weight,
+                    "effective_weight": round(effective_weight, 4),
+                    "evidence": evidence,
+                }
+            )
+        )
+    return sorted(weighted, key=lambda item: (-item.confidence * item.effective_weight, item.entity_id))
+
+
 def _preferred_device_classes(domain: str, *, context: bool) -> frozenset[str]:
     if context:
         mapping = {

app/actuators/models.py

@@ -37,6 +37,21 @@ class BehaviorStatus(StrEnum):
     BLOCKED = "blocked"
 
 
+class SafetyStage(StrEnum):
+    OBSERVE = "observe"
+    SUGGEST = "suggest"
+    SHADOW = "shadow"
+    PARTIAL = "partial"
+    ACTIVE = "active"
+
+
+class JobStatus(StrEnum):
+    PENDING = "pending"
+    RUNNING = "running"
+    COMPLETED = "completed"
+    FAILED = "failed"
+
+
 class AssignmentCandidate(BaseModel):
     entity_id: str
     domain: str
@@ -49,6 +64,8 @@ class AssignmentCandidate(BaseModel):
     device_name: str | None = None
     score: float = Field(ge=0.0)
     confidence: float = Field(ge=0.0, le=1.0)
+    manual_weight: float | None = Field(default=None, ge=0.0, le=1.0)
+    effective_weight: float = Field(default=1.0, ge=0.0, le=1.0)
     auto_accepted: bool = False
     evidence: list[str] = Field(default_factory=list)
 
@@ -62,9 +79,18 @@ class AssignmentSelection(BaseModel):
     reason: str = "Noch keine Zuordnung vorhanden."
 
 
+class SensorWeightGroup(BaseModel):
+    group_id: str = Field(pattern=r"^[a-z0-9_-]{1,64}$")
+    name: str = Field(min_length=1, max_length=120)
+    entity_ids: list[str] = Field(default_factory=list)
+    weight: float = Field(default=1.0, ge=0.0, le=1.0)
+
+
 class ManualOverride(BaseModel):
     numeric_entity_id: str | None = None
     context_entity_ids: list[str] = Field(default_factory=list)
+    sensor_weights: dict[str, float] = Field(default_factory=dict)
+    sensor_weight_groups: list[SensorWeightGroup] = Field(default_factory=list)
     updated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
     note: str | None = None
 
@@ -110,6 +136,61 @@ class BehaviorPrediction(BaseModel):
     execution_reason: str = "Vorhersage wurde noch nicht ausgeführt."
 
 
+class DecisionFactor(BaseModel):
+    entity_id: str | None = None
+    label: str
+    factor_type: str = Field(max_length=40)
+    state: str | None = None
+    weight: float = Field(default=1.0, ge=0.0, le=1.0)
+    contribution: float = Field(default=0.0, ge=0.0, le=1.0)
+    evidence: list[str] = Field(default_factory=list)
+
+
+class SafetyRule(BaseModel):
+    rule_id: str = Field(pattern=r"^[a-z0-9_-]{1,64}$")
+    label: str = Field(min_length=1, max_length=160)
+    enabled: bool = True
+    blocking: bool = True
+    reason: str = Field(default="", max_length=300)
+
+
+def default_safety_rules() -> list[SafetyRule]:
+    return [
+        SafetyRule(
+            rule_id="activation_ready",
+            label="Nur nach Lernfreigabe aktiv schalten",
+            reason="Der Aktor muss genug eindeutiges Verhalten gelernt haben.",
+        ),
+        SafetyRule(
+            rule_id="confidence_threshold",
+            label="Mindest-Sicherheit einhalten",
+            reason="Vorhersagen unter der Schaltschwelle bleiben im Shadow-Modus.",
+        ),
+        SafetyRule(
+            rule_id="cooldown",
+            label="Sicherheits-Cooldown gegen Hin-und-her-Schalten",
+            reason="Gleiche Zielzustände werden nicht zu schnell wiederholt.",
+        ),
+        SafetyRule(
+            rule_id="manual_block",
+            label="Manuelle Sperre respektieren",
+            reason="Nutzer können jeden Aktor sofort blockieren.",
+        ),
+    ]
+
+
+class SafetyProfile(BaseModel):
+    stage: SafetyStage = SafetyStage.SHADOW
+    manual_block: bool = False
+    min_confidence: float = Field(default=0.82, ge=0.0, le=1.0)
+    min_confidence_on: float | None = Field(default=None, ge=0.0, le=1.0)
+    min_confidence_off: float | None = Field(default=None, ge=0.0, le=1.0)
+    cooldown_seconds: int | None = Field(default=None, ge=0)
+    rules: list[SafetyRule] = Field(default_factory=default_safety_rules)
+    updated_at: datetime | None = None
+    note: str | None = Field(default=None, max_length=500)
+
+
 class ExecutionEvent(BaseModel):
     target_state: str
     executed_at: datetime
@@ -139,6 +220,16 @@ class BehaviorState(BaseModel):
     related_automations: list[RelatedAutomation] = Field(default_factory=list)
     paused_automation_entity_ids: list[str] = Field(default_factory=list)
     reason: str = "Historische Aktorhandlungen werden analysiert."
+    safety: SafetyProfile = Field(default_factory=SafetyProfile)
+    decision_factors: list[DecisionFactor] = Field(default_factory=list)
+    knowledge: list[str] = Field(default_factory=list)
+    assumptions: list[str] = Field(default_factory=list)
+    uncertainties: list[str] = Field(default_factory=list)
+    safety_blockers: list[str] = Field(default_factory=list)
+    sample_trend: list[int] = Field(default_factory=list)
+    confidence_trend: list[float] = Field(default_factory=list)
+    correct_feedback_count: int = Field(default=0, ge=0)
+    incorrect_feedback_count: int = Field(default=0, ge=0)
 
 
 class ActuatorRecord(BaseModel):
@@ -165,5 +256,22 @@ class ReconciliationState(BaseModel):
     last_summary: str = "Noch keine Reconciliation ausgeführt."
 
 
+class JobQueueItem(BaseModel):
+    job_id: str = Field(min_length=1, max_length=120)
+    kind: str = Field(min_length=1, max_length=40)
+    target: str | None = Field(default=None, max_length=160)
+    trigger: str = Field(default="manual", max_length=40)
+    status: JobStatus = JobStatus.PENDING
+    started_at: datetime | None = None
+    completed_at: datetime | None = None
+    duration_ms: int | None = Field(default=None, ge=0)
+    error: str | None = Field(default=None, max_length=500)
+    summary: str = Field(default="", max_length=500)
+
+
+class JobQueueState(BaseModel):
+    jobs: list[JobQueueItem] = Field(default_factory=list)
+
+
 def model_id_for_actuator(actuator_entity_id: str) -> str:
     return f"actuator.{actuator_entity_id}"

app/actuators/store.py

@@ -8,6 +8,9 @@ from threading import RLock
 
 from app.actuators.models import (
     ActuatorRecord,
+    JobQueueItem,
+    JobQueueState,
+    JobStatus,
     LifecycleStatus,
     ModelLifecycleState,
     ReconciliationState,
@@ -22,6 +25,7 @@ class ActuatorStore:
         self._actuators_root.mkdir(parents=True, exist_ok=True)
         self._lock = RLock()
         self._reconciliation_state_path = self._root / "reconciliation_state.json"
+        self._job_queue_path = self._root / "job_queue.json"
 
     def list(self) -> list[ActuatorRecord]:
         with self._lock:
@@ -85,6 +89,75 @@ class ActuatorStore:
             self._persist_reconciliation_state(state)
             return state
 
+    def load_job_queue(self) -> JobQueueState:
+        with self._lock:
+            if not self._job_queue_path.exists():
+                return JobQueueState()
+            try:
+                return JobQueueState.model_validate_json(
+                    self._job_queue_path.read_text(encoding="utf-8")
+                )
+            except ValueError as exc:
+                raise ValueError("Ungültiger Job-Queue-Status.") from exc
+
+    def start_job(
+        self,
+        *,
+        kind: str,
+        trigger: str,
+        target: str | None = None,
+        summary: str = "",
+    ) -> JobQueueItem:
+        now = datetime.now(timezone.utc)
+        job = JobQueueItem(
+            job_id=f"{now.strftime('%Y%m%d%H%M%S%f')}-{kind}-{target or 'all'}",
+            kind=kind,
+            target=target,
+            trigger=trigger,
+            status=JobStatus.RUNNING,
+            started_at=now,
+            summary=summary,
+        )
+        with self._lock:
+            queue = self.load_job_queue()
+            queue.jobs = [*queue.jobs, job][-50:]
+            self._persist_job_queue(queue)
+        return job
+
+    def finish_job(
+        self,
+        job_id: str,
+        *,
+        status: JobStatus,
+        summary: str = "",
+        error: str | None = None,
+    ) -> JobQueueItem | None:
+        now = datetime.now(timezone.utc)
+        with self._lock:
+            queue = self.load_job_queue()
+            updated_job: JobQueueItem | None = None
+            jobs: list[JobQueueItem] = []
+            for job in queue.jobs:
+                if job.job_id != job_id:
+                    jobs.append(job)
+                    continue
+                duration_ms = None
+                if job.started_at is not None:
+                    duration_ms = max(0, int((now - job.started_at).total_seconds() * 1000))
+                updated_job = job.model_copy(
+                    update={
+                        "status": status,
+                        "completed_at": now,
+                        "duration_ms": duration_ms,
+                        "summary": summary or job.summary,
+                        "error": error,
+                    }
+                )
+                jobs.append(updated_job)
+            queue.jobs = jobs[-50:]
+            self._persist_job_queue(queue)
+            return updated_job
+
     def _target(self, actuator_entity_id: str) -> Path:
         if "." not in actuator_entity_id:
             raise ValueError("Ungültige actuator_entity_id.")
@@ -108,6 +181,14 @@ class ActuatorStore:
         )
         os.replace(temporary, self._reconciliation_state_path)
 
+    def _persist_job_queue(self, state: JobQueueState) -> None:
+        temporary = self._job_queue_path.with_suffix(".json.tmp")
+        temporary.write_text(
+            json.dumps(state.model_dump(mode="json"), ensure_ascii=True, sort_keys=True) + "\n",
+            encoding="utf-8",
+        )
+        os.replace(temporary, self._job_queue_path)
+
     @staticmethod
     def _load(path: Path) -> ActuatorRecord:
         try:

app/api/v1/actuators.py

@@ -9,7 +9,8 @@ from fastapi import APIRouter, Depends, HTTPException, Query, Request, status
 from pydantic import BaseModel, Field
 
 from app.actuators.lifecycle import ActuatorReconciliationService
-from app.actuators.models import ActuatorRecord, ReconciliationState
+from app.actuators.models import ActuatorRecord, ReconciliationState, SensorWeightGroup
+from app.actuators.models import JobQueueItem, JobQueueState, JobStatus, SafetyProfile
 from app.actuators.store import ActuatorStore
 from app.behavior.engine import BehaviorEngine
 from app.config import Settings
@@ -44,11 +45,21 @@ class ManualAssignmentRequest(BaseModel):
     note: str | None = Field(default=None, max_length=500)
 
 
+class WeightOverrideRequest(BaseModel):
+    sensor_weights: dict[str, float] = Field(default_factory=dict)
+    sensor_weight_groups: list[SensorWeightGroup] = Field(default_factory=list)
+    note: str | None = Field(default=None, max_length=500)
+
+
 class FeedbackRequest(BaseModel):
     correct: bool
     expected_state: str | None = Field(default=None, max_length=100)
 
 
+class SafetyProfileRequest(BaseModel):
+    safety: SafetyProfile
+
+
 class ActuatorSuggestion(BaseModel):
     entity_id: str
     domain: str
@@ -106,6 +117,7 @@ class DashboardOverview(BaseModel):
     cache: EntityCacheStatus
     actuators: list[ActuatorSummary]
     discovery_groups: list[DashboardDiscoveryGroup]
+    jobs: JobQueueState = Field(default_factory=JobQueueState)
 
 
 @router.get("/discovery", response_model=list[HaEntitySummary])
@@ -118,8 +130,19 @@ def discover_actuators(
     if cached_entities:
         entities = {entity.entity_id: entity for entity in cached_entities}
     else:
-        fresh_entities = list(ha_reader.read_entities())
-        _save_cached_entities(request, fresh_entities)
+        job = _start_job(
+            request,
+            kind="discovery",
+            trigger="manual" if refresh else "cache-miss",
+            summary="Home-Assistant-Entities werden gelesen und klassifiziert.",
+        )
+        try:
+            fresh_entities = list(ha_reader.read_entities())
+            _save_cached_entities(request, fresh_entities)
+        except Exception as exc:
+            _finish_job(job, request, status=JobStatus.FAILED, summary="Discovery fehlgeschlagen.", error=str(exc))
+            raise
+        _finish_job(job, request, status=JobStatus.COMPLETED, summary=f"{len(fresh_entities)} Entities klassifiziert.")
         entities = {entity.entity_id: entity for entity in fresh_entities}
     discovered = discover_entities(list(entities.values()))
     actuator_ids = _deduplicate_actuator_ids(
@@ -272,6 +295,12 @@ def dashboard_overview(request: Request) -> DashboardOverview:
     reconciliation = _reconciliation_state_or_default(request)
     ws_status = getattr(request.app.state, "ws_status", None)
     actuators = list_configured_summary(request)
+    store = getattr(request.app.state, "actuator_store", None)
+    jobs = (
+        store.load_job_queue()
+        if isinstance(store, ActuatorStore)
+        else JobQueueState()
+    )
     return DashboardOverview(
         system=DashboardSystemStatus(
             websocket_status=getattr(ws_status, "status", "unavailable"),
@@ -292,6 +321,7 @@ def dashboard_overview(request: Request) -> DashboardOverview:
         ),
         actuators=actuators,
         discovery_groups=cached_groups,
+        jobs=jobs,
     )
 
 
@@ -366,6 +396,18 @@ def record_feedback(
         raise HTTPException(status_code=404, detail=str(exc)) from exc
 
 
+@router.post("/{actuator_entity_id}/safety", response_model=ActuatorRecord)
+def set_safety_profile(
+    actuator_entity_id: str,
+    payload: SafetyProfileRequest,
+    request: Request,
+) -> ActuatorRecord:
+    try:
+        return _behavior(request).set_safety_profile(actuator_entity_id, profile=payload.safety)
+    except KeyError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+
+
 @router.post("/{actuator_entity_id}/activation", response_model=ActuatorRecord)
 def set_activation(
     actuator_entity_id: str,
@@ -406,6 +448,27 @@ def set_manual_assignment(
         raise HTTPException(status_code=422, detail=str(exc)) from exc
 
 
+@router.post("/{actuator_entity_id}/weights", response_model=ActuatorRecord)
+def set_weight_overrides(
+    actuator_entity_id: str,
+    payload: WeightOverrideRequest,
+    request: Request,
+) -> ActuatorRecord:
+    try:
+        _validate_weight_payload(payload)
+        record = _service(request).set_weight_overrides(
+            actuator_entity_id,
+            sensor_weights=payload.sensor_weights,
+            sensor_weight_groups=payload.sensor_weight_groups,
+            note=payload.note,
+        )
+        return record
+    except KeyError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+    except ValueError as exc:
+        raise HTTPException(status_code=422, detail=str(exc)) from exc
+
+
 @router.post(
     "/{actuator_entity_id}/related-automations/refresh",
     response_model=ActuatorRecord,
@@ -414,11 +477,27 @@ def refresh_related_automations(
     actuator_entity_id: str,
     request: Request,
 ) -> ActuatorRecord:
+    job = _start_job(
+        request,
+        kind="automation_refresh",
+        trigger="manual",
+        target=actuator_entity_id,
+        summary="Passende HA-Automationen werden gesucht.",
+    )
     try:
-        return _behavior(request).refresh_related_automations(actuator_entity_id)
+        record = _behavior(request).refresh_related_automations(actuator_entity_id)
+        _finish_job(
+            job,
+            request,
+            status=JobStatus.COMPLETED,
+            summary=f"{len(record.behavior.related_automations)} Automationen gefunden.",
+        )
+        return record
     except KeyError as exc:
+        _finish_job(job, request, status=JobStatus.FAILED, summary="Automation-Refresh fehlgeschlagen.", error=str(exc))
         raise HTTPException(status_code=404, detail=str(exc)) from exc
     except (ValueError, HaClientError) as exc:
+        _finish_job(job, request, status=JobStatus.FAILED, summary="Automation-Refresh fehlgeschlagen.", error=str(exc))
         raise HTTPException(status_code=409, detail=str(exc)) from exc
 
 
@@ -459,12 +538,85 @@ def run_reconciliation(
     request: Request,
     trigger: str = Query(default="manual", pattern=r"^[a-z0-9_-]{1,32}$"),
 ) -> ReconciliationState:
-    state = _service(request).reconcile_all(trigger=trigger)
-    _behavior(request).train_all()
-    _behavior(request).evaluate_all()
+    reconciliation_job = _start_job(
+        request,
+        kind="reconciliation",
+        trigger=trigger,
+        summary="Kontext, Zuordnung und Modelle werden abgeglichen.",
+    )
+    training_job: JobQueueItem | None = None
+    evaluation_job: JobQueueItem | None = None
+    try:
+        state = _service(request).reconcile_all(trigger=trigger)
+        _finish_job(reconciliation_job, request, status=JobStatus.COMPLETED, summary=state.last_summary)
+        reconciliation_job = None
+        training_job = _start_job(
+            request,
+            kind="training",
+            trigger=trigger,
+            summary="Gelernte Aktorhandlungen werden aktualisiert.",
+        )
+        _behavior(request).train_all()
+        _finish_job(training_job, request, status=JobStatus.COMPLETED, summary="Training abgeschlossen.")
+        training_job = None
+        evaluation_job = _start_job(
+            request,
+            kind="evaluation",
+            trigger=trigger,
+            summary="Aktuelle Vorhersagen werden neu berechnet.",
+        )
+        _behavior(request).evaluate_all()
+        _finish_job(evaluation_job, request, status=JobStatus.COMPLETED, summary="Evaluation abgeschlossen.")
+        evaluation_job = None
+    except Exception as exc:
+        for job in [reconciliation_job, training_job, evaluation_job]:
+            if isinstance(job, JobQueueItem) and job.status is JobStatus.RUNNING:
+                _finish_job(job, request, status=JobStatus.FAILED, summary="Job fehlgeschlagen.", error=str(exc))
+        raise
     return state
 
 
+@router.get("/job-queue/state", response_model=JobQueueState)
+def get_job_queue(request: Request) -> JobQueueState:
+    store = getattr(request.app.state, "actuator_store", None)
+    if not isinstance(store, ActuatorStore):
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail="Actuator Store nicht initialisiert.",
+        )
+    return store.load_job_queue()
+
+
+def _start_job(
+    request: Request,
+    *,
+    kind: str,
+    trigger: str,
+    target: str | None = None,
+    summary: str = "",
+) -> JobQueueItem | None:
+    store = getattr(request.app.state, "actuator_store", None)
+    if not isinstance(store, ActuatorStore):
+        return None
+    return store.start_job(kind=kind, trigger=trigger, target=target, summary=summary)
+
+
+def _finish_job(
+    job: JobQueueItem | None,
+    request: Request,
+    *,
+    status: JobStatus,
+    summary: str,
+    error: str | None = None,
+) -> None:
+    if job is None:
+        return
+    store = getattr(request.app.state, "actuator_store", None)
+    if not isinstance(store, ActuatorStore):
+        return
+    store.finish_job(job.job_id, status=status, summary=summary, error=error)
+
+
 def _service(request: Request) -> ActuatorReconciliationService:
     service = getattr(request.app.state, "actuator_service", None)
     if not isinstance(service, ActuatorReconciliationService):
@@ -485,6 +637,20 @@ def _behavior(request: Request) -> BehaviorEngine:
     return engine
 
 
+def _validate_weight_payload(payload: WeightOverrideRequest) -> None:
+    for entity_id, weight in payload.sensor_weights.items():
+        if "." not in entity_id:
+            raise ValueError(f"Ungültige Entity-ID: {entity_id}")
+        if not 0.0 <= weight <= 1.0:
+            raise ValueError(f"Ungültige Gewichtung für {entity_id}: {weight}")
+    for group in payload.sensor_weight_groups:
+        if not group.entity_ids:
+            raise ValueError(f"Gruppe {group.name} enthält keine Entities.")
+        for entity_id in group.entity_ids:
+            if "." not in entity_id:
+                raise ValueError(f"Ungültige Entity-ID in Gruppe {group.name}: {entity_id}")
+
+
 def _reconciliation_state_or_default(request: Request) -> ReconciliationState:
     store = getattr(request.app.state, "actuator_store", None)
     if not isinstance(store, ActuatorStore):

app/behavior/engine.py

@@ -12,8 +12,11 @@ from app.actuators.models import (
     BehaviorPrediction,
     BehaviorState,
     BehaviorStatus,
+    DecisionFactor,
     ExecutionEvent,
     RelatedAutomation,
+    SafetyProfile,
+    SafetyStage,
 )
 from app.actuators.store import ActuatorStore
 from app.config import Settings
@@ -175,6 +178,10 @@ class BehaviorEngine:
                 "patterns": patterns[-_MAX_PATTERNS:],
                 "last_trained_at": now,
                 "reason": reason,
+                "sample_trend": [*record.behavior.sample_trend, len(patterns)][-30:],
+                "knowledge": _knowledge_lines(record, len(patterns), trusted_actions),
+                "assumptions": _assumption_lines(record),
+                "uncertainties": _uncertainty_lines(record, len(patterns), trusted_actions),
             }
         )
         return self._save_behavior(record, behavior)
@@ -268,16 +275,25 @@ class BehaviorEngine:
             timezone_name=self._settings.timezone,
         )
         if prediction is not None:
+            safety_allowed, safety_blockers = self._assess_safety(
+                record,
+                actuator.state,
+                prediction,
+                now,
+            )
             prediction = prediction.model_copy(
                 update={
-                    "execution_reason": self._prediction_execution_reason(
-                        record,
-                        actuator.state,
-                        prediction,
-                        now,
+                    "execution_reason": (
+                        "Ausführung ist freigegeben."
+                        if safety_allowed
+                        else "Nicht ausgeführt: " + " ".join(safety_blockers)
                     )
                 }
             )
+        else:
+            safety_allowed = False
+            safety_blockers = ["Keine fällige Vorhersage."]
+        decision_factors = _decision_factors_for(record, current_context, prediction)
         behavior = record.behavior.model_copy(
             update={
                 "last_evaluated_at": now,
@@ -287,18 +303,21 @@ class BehaviorEngine:
                     if prediction is not None
                     else "Aktuell ist kein gelerntes Handlungsmuster fällig."
                 ),
+                "decision_factors": decision_factors,
+                "knowledge": _knowledge_lines(record, record.behavior.sample_count, record.behavior.high_confidence_sample_count),
+                "assumptions": _assumption_lines(record),
+                "uncertainties": _uncertainty_lines(record, record.behavior.sample_count, record.behavior.high_confidence_sample_count),
+                "safety_blockers": safety_blockers if prediction is not None else [],
+                "confidence_trend": (
+                    [*record.behavior.confidence_trend, round(prediction.confidence, 4)][-30:]
+                    if prediction is not None
+                    else record.behavior.confidence_trend
+                ),
             }
         )
         if (
             prediction is not None
-            and behavior.mode is BehaviorMode.ACTIVE
-            and prediction.confidence >= self._settings.prediction_confidence
-            and actuator.state != prediction.target_state
-            and self._cooldown_elapsed(
-                behavior,
-                now,
-                prediction.target_state,
-            )
+            and safety_allowed
         ):
             domain = actuator_entity_id.split(".", 1)[0]
             service = service_for_state(domain, prediction.target_state)
@@ -403,6 +422,8 @@ class BehaviorEngine:
                 )
             )
             reason = "Vorhersage wurde vom Nutzer als korrekt bestätigt."
+            correct_count = record.behavior.correct_feedback_count + 1
+            incorrect_count = record.behavior.incorrect_feedback_count
         else:
             target = prediction.target_state if prediction is not None else None
             if target:
@@ -431,6 +452,8 @@ class BehaviorEngine:
                     )
                 )
             reason = "Vorhersage wurde vom Nutzer als falsch markiert."
+            correct_count = record.behavior.correct_feedback_count
+            incorrect_count = record.behavior.incorrect_feedback_count + 1
         behavior = record.behavior.model_copy(
             update={
                 "patterns": patterns[-_MAX_PATTERNS:],
@@ -441,6 +464,23 @@ class BehaviorEngine:
                 ),
                 "reason": reason,
                 "last_trained_at": now,
+                "correct_feedback_count": correct_count,
+                "incorrect_feedback_count": incorrect_count,
+            }
+        )
+        return self._save_behavior(record, behavior)
+
+    def set_safety_profile(
+        self,
+        actuator_entity_id: str,
+        *,
+        profile: SafetyProfile,
+    ) -> ActuatorRecord:
+        record = self._store.get(actuator_entity_id)
+        behavior = record.behavior.model_copy(
+            update={
+                "safety": profile.model_copy(update={"updated_at": datetime.now(timezone.utc)}),
+                "reason": "Sicherheitsprofil wurde manuell aktualisiert.",
             }
         )
         return self._save_behavior(record, behavior)
@@ -527,6 +567,9 @@ class BehaviorEngine:
                 update={
                     "mode": mode,
                     "approved_at": approved_at,
+                    "safety": record.behavior.safety.model_copy(
+                        update={"stage": SafetyStage.ACTIVE, "updated_at": now}
+                    ),
                     "reason": (
                         "Autonomes Lernen und Schalten wurde ausdrücklich freigegeben."
                     ),
@@ -607,6 +650,9 @@ class BehaviorEngine:
             update={
                 "mode": mode,
                 "approved_at": approved_at,
+                "safety": record.behavior.safety.model_copy(
+                    update={"stage": SafetyStage.SHADOW, "updated_at": now}
+                ),
                 "related_automations": [
                     automation.model_copy(update={"enabled": True})
                     if (
@@ -651,6 +697,51 @@ class BehaviorEngine:
             return "Nicht ausgeführt: Sicherheits-Cooldown ist noch aktiv."
         return "Ausführung ist freigegeben."
 
+    def _assess_safety(
+        self,
+        record: ActuatorRecord,
+        current_state: str | None,
+        prediction: BehaviorPrediction,
+        now: datetime,
+    ) -> tuple[bool, list[str]]:
+        profile = record.behavior.safety
+        blockers: list[str] = []
+        domain = record.actuator_entity_id.split(".", 1)[0]
+        if not record.enabled:
+            blockers.append("Aktor ist in SillyHome deaktiviert.")
+        if domain not in _SAFE_ACTIVE_DOMAINS:
+            blockers.append(f"Domain {domain} ist nicht für autonomes Schalten freigegeben.")
+        if profile.manual_block:
+            blockers.append("Manuelle Sicherheitssperre ist aktiv.")
+        stage = profile.stage
+        if (
+            record.behavior.mode is BehaviorMode.ACTIVE
+            and profile.updated_at is None
+            and stage is SafetyStage.SHADOW
+        ):
+            stage = SafetyStage.ACTIVE
+        if stage not in {SafetyStage.ACTIVE, SafetyStage.PARTIAL}:
+            blockers.append(f"Safety-Stufe {stage.value} erlaubt noch kein Schalten.")
+        if record.behavior.mode is not BehaviorMode.ACTIVE:
+            blockers.append("SillyHome ist im Shadow-Modus.")
+        if not record.behavior.activation_ready:
+            blockers.append(record.behavior.activation_reason)
+        threshold = _confidence_threshold_for(profile, prediction.target_state)
+        if prediction.confidence < threshold:
+            blockers.append(
+                f"Sicherheit {prediction.confidence:.0%} liegt unter der Schwelle {threshold:.0%}."
+            )
+        if current_state == prediction.target_state:
+            blockers.append("Zielzustand ist bereits erreicht.")
+        if not self._cooldown_elapsed(
+            record.behavior,
+            now,
+            prediction.target_state,
+            cooldown_seconds=profile.cooldown_seconds,
+        ):
+            blockers.append("Sicherheits-Cooldown ist noch aktiv.")
+        return not blockers, blockers
+
     def _build_patterns(
         self,
         *,
@@ -701,6 +792,8 @@ class BehaviorEngine:
         behavior: BehaviorState,
         now: datetime,
         target_state: str,
+        *,
+        cooldown_seconds: int | None = None,
     ) -> bool:
         if behavior.last_executed_at is None:
             return True
@@ -708,7 +801,9 @@ class BehaviorEngine:
         if last_event is not None and last_event.target_state != target_state:
             return True
         return (now - behavior.last_executed_at) >= timedelta(
-            seconds=self._settings.execution_cooldown_seconds
+            seconds=cooldown_seconds
+            if cooldown_seconds is not None
+            else self._settings.execution_cooldown_seconds
         )
 
     def _save_behavior(
@@ -791,6 +886,110 @@ def _event_changed_at(new_state: dict[str, object] | None) -> datetime | None:
     return parsed
 
 
+def _confidence_threshold_for(profile: SafetyProfile, target_state: str) -> float:
+    if target_state == "on" and profile.min_confidence_on is not None:
+        return profile.min_confidence_on
+    if target_state in {"off", "closed"} and profile.min_confidence_off is not None:
+        return profile.min_confidence_off
+    return profile.min_confidence
+
+
+def _decision_factors_for(
+    record: ActuatorRecord,
+    current_context: dict[str, str | None],
+    prediction: BehaviorPrediction | None,
+) -> list[DecisionFactor]:
+    factors: list[DecisionFactor] = []
+    candidates = {
+        candidate.entity_id: candidate
+        for candidate in [*record.numeric_candidates, *record.context_candidates]
+    }
+    for entity_id, state in current_context.items():
+        candidate = candidates.get(entity_id)
+        weight = candidate.effective_weight if candidate is not None else 1.0
+        relevance = candidate.confidence if candidate is not None else 0.5
+        contribution = round(min(1.0, weight * relevance), 4)
+        factors.append(
+            DecisionFactor(
+                entity_id=entity_id,
+                label=(
+                    candidate.friendly_name
+                    if candidate is not None and candidate.friendly_name
+                    else entity_id
+                ),
+                factor_type="context",
+                state=state,
+                weight=round(weight, 4),
+                contribution=contribution,
+                evidence=(
+                    candidate.evidence[:4]
+                    if candidate is not None
+                    else ["Aktuell ausgewähltes Kontextsignal."]
+                ),
+            )
+        )
+    if prediction is not None:
+        factors.append(
+            DecisionFactor(
+                label=f"Vorhersage {prediction.target_state}",
+                factor_type="prediction",
+                state=prediction.target_state,
+                weight=1.0,
+                contribution=prediction.confidence,
+                evidence=[prediction.reason],
+            )
+        )
+    return sorted(factors, key=lambda item: (-item.contribution, item.label))[:12]
+
+
+def _knowledge_lines(
+    record: ActuatorRecord,
+    sample_count: int,
+    trusted_actions: int,
+) -> list[str]:
+    lines = [
+        f"{sample_count} historische Aktorhandlungen sind ausgewertet.",
+        f"{trusted_actions} Handlungen stammen eindeutig von Nutzer oder HA-Automationen.",
+    ]
+    if record.assignment.selected_numeric_entity_id:
+        lines.append(f"Hauptsensor: {record.assignment.selected_numeric_entity_id}.")
+    if record.assignment.selected_context_entity_ids:
+        lines.append(
+            f"{len(record.assignment.selected_context_entity_ids)} Kontextsignale sind verbunden."
+        )
+    return lines
+
+
+def _assumption_lines(record: ActuatorRecord) -> list[str]:
+    lines = [
+        "Ähnliche Zeitfenster und ähnliche Kontextzustände deuten auf ähnliche Nutzerabsicht hin."
+    ]
+    if record.manual_override is not None:
+        lines.append("Manuelle Sensor-/Kontextkorrekturen werden höher gewichtet.")
+    if record.behavior.related_automations:
+        lines.append("Passende HA-Automationen gelten als starker Hinweis auf vorhandene Logik.")
+    return lines
+
+
+def _uncertainty_lines(
+    record: ActuatorRecord,
+    sample_count: int,
+    trusted_actions: int,
+) -> list[str]:
+    lines: list[str] = []
+    if sample_count < trusted_actions + 3:
+        lines.append("Noch wenig Varianz in den gelernten Handlungen.")
+    if trusted_actions < sample_count:
+        lines.append("Ein Teil der Handlungen ist nicht eindeutig Nutzer oder Automation zugeordnet.")
+    if record.assignment.review_required:
+        lines.append("Die automatische Kontextzuordnung verlangt noch Prüfung.")
+    if record.behavior.incorrect_feedback_count:
+        lines.append(
+            f"{record.behavior.incorrect_feedback_count} negative Feedbacks senken Vertrauen."
+        )
+    return lines or ["Keine kritische Unsicherheit aus den lokalen Daten erkannt."]
+
+
 def predict_behavior(
     patterns: list[BehaviorPattern],
     *,

app/main.py

@@ -105,7 +105,7 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
 app = FastAPI(
     title="SillyHome Next API",
     description="Lokales Smart-Home-Intelligenzsystem für Home Assistant.",
-    version="1.0.0",
+    version="1.1.0",
     lifespan=lifespan,
 )
 app.state.settings = load_settings()

app/static/index.html

@@ -5,18 +5,51 @@
   <meta name="viewport" content="width=device-width,initial-scale=1">
   <title>SillyHome Next</title>
   <style>
-    :root { color-scheme: dark; font-family: system-ui, sans-serif; background: #111317; color: #f3f5f7; scroll-behavior:smooth; --accent:#ff8a1c; --accent-strong:#ffab45; --panel:#181c22; --panel-soft:#20262e; --border:#303842; }
-    body { margin: 0; font-size:15px; }
-    header { padding: 18px 20px; background: linear-gradient(135deg,#1c1f26,#3a2411 70%,#5c2d08); }
-    h1,h2,h3 { margin: 0 0 12px; }
-    header p { margin: 5px 0; color: #c3d1dc; }
-    main { display: grid; grid-template-columns: minmax(280px,.82fr) minmax(0,1.38fr); gap: 12px; padding: 12px; max-width:1380px; margin:0 auto; }
-    section { background: var(--panel); border: 1px solid var(--border); border-radius: 8px; padding: 12px; min-width:0; }
-    section:target { outline:2px solid var(--accent); outline-offset:2px; }
-    .wide { grid-column: 1 / -1; }
-    .quick-nav { position:sticky; top:0; z-index:10; display:flex; gap:8px; overflow-x:auto; padding:10px 14px; background:rgba(17,19,23,.94); border-bottom:1px solid var(--border); backdrop-filter:blur(8px); }
-    .quick-nav a { flex:0 0 auto; padding:10px 12px; border-radius:999px; background:#222831; border:1px solid var(--border); color:#f3f5f7; text-decoration:none; font-weight:700; font-size:.92rem; }
-    .quick-nav a.primary { background:#8f4208; border-color:var(--accent); }
+    :root {
+      color-scheme: dark;
+      font-family: Inter, ui-sans-serif, system-ui, sans-serif;
+      background:#0e1218;
+      color:#f4f7fb;
+      scroll-behavior:smooth;
+      --accent:#ff8a1c;
+      --accent-strong:#ffb45b;
+      --complement:#1cc7ff;
+      --complement-soft:#123447;
+      --panel:#171d25;
+      --panel-soft:#202833;
+      --panel-quiet:#111720;
+      --border:#314050;
+      --text-soft:#aebdcc;
+      --ok:#53e0a5;
+      --warn:#f3c969;
+      --bad:#8fb8ff;
+    }
+    * { box-sizing:border-box; }
+    html, body { max-width:100%; overflow-x:hidden; }
+    body { margin:0; font-size:15px; background:var(--panel-quiet); }
+    h1,h2,h3 { margin:0 0 10px; letter-spacing:0; }
+    p { margin:6px 0; }
+    header { padding:18px 18px 14px; border-bottom:1px solid var(--border); background:#121821; }
+    .topbar { max-width:1480px; margin:0 auto; display:flex; align-items:flex-start; justify-content:space-between; gap:16px; }
+    .brand { display:grid; gap:5px; min-width:0; }
+    .brand-row { display:flex; align-items:center; gap:10px; flex-wrap:wrap; }
+    .brand-mark { width:34px; height:34px; border-radius:8px; display:grid; place-items:center; background:var(--accent); color:#201204; font-weight:900; }
+    header p { color:var(--text-soft); max-width:820px; }
+    .header-actions { display:grid; gap:8px; min-width:230px; }
+    .header-actions label { margin:0; font-size:.82rem; }
+    .status-pill { display:flex; align-items:center; gap:8px; padding:8px 10px; border:1px solid var(--border); border-radius:8px; background:#101722; color:#d9e6f0; white-space:nowrap; }
+    .dot { width:9px; height:9px; border-radius:50%; background:var(--complement); box-shadow:0 0 0 3px rgba(28,199,255,.15); }
+    main { display:grid; grid-template-columns:minmax(270px,.72fr) minmax(0,1.58fr); grid-template-areas:"control board" "control detail" "status status" "guide guide"; gap:12px; padding:12px; max-width:1480px; margin:0 auto; }
+    section { background:var(--panel); border:1px solid var(--border); border-radius:8px; padding:12px; min-width:0; }
+    section:target { outline:2px solid var(--complement); outline-offset:2px; }
+    .control-panel { grid-area:control; align-self:start; position:sticky; top:58px; }
+    .board-panel { grid-area:board; }
+    .detail-panel { grid-area:detail; }
+    .status-panel { grid-area:status; }
+    .guide-panel { grid-area:guide; }
+    .panel-title { display:flex; align-items:center; justify-content:space-between; gap:10px; margin-bottom:8px; }
+    .toolbar { display:flex; flex-wrap:wrap; gap:8px; align-items:center; margin:10px 0; }
+    .toolbar button { margin-top:0; }
     details.collapsible > summary,
     .manual-context > summary,
     .group-panel > summary { cursor:pointer; font-weight:800; color:#eaf1f8; }
@@ -31,48 +64,60 @@
     .manual-context[open] > summary::after,
     .group-panel[open] > summary::after { content:"zuklappen"; }
     .steps { display:grid; grid-template-columns:repeat(auto-fit,minmax(190px,1fr)); gap:10px; margin-top:12px; }
-    .step { background:#12161c; border:1px solid var(--border); border-radius:8px; padding:10px; }
-    .step-number { display:inline-grid; place-items:center; width:28px; height:28px; border-radius:50%; background:var(--accent); color:#211204; font-weight:800; margin-bottom:8px; }
+    .step { background:#121922; border:1px solid var(--border); border-radius:8px; padding:10px; }
+    .step-number { display:inline-grid; place-items:center; width:28px; height:28px; border-radius:8px; background:var(--complement); color:#03151d; font-weight:900; margin-bottom:8px; }
     .step p { margin:5px 0; }
     .ok { color: #73e0a9; }
     .warn { color: #f3c969; }
-    .bad { color: #ff8f8f; }
-    label { display: block; margin: 9px 0 4px; color: #b9c9d6; }
-    select,input,button { box-sizing: border-box; width: 100%; border-radius: 8px; border: 1px solid #3b4b5b; padding: 10px; background: #11161d; color: #fff; font:inherit; min-width:0; }
+    .bad { color: var(--bad); }
+    label { display:block; margin:9px 0 4px; color:#c3d2df; font-weight:700; }
+    select,input,button { width:100%; border-radius:8px; border:1px solid #3b4b5b; padding:10px; background:#111821; color:#fff; font:inherit; min-width:0; }
+    input[type="checkbox"] { width:auto; min-width:0; vertical-align:middle; margin-right:8px; }
     select[multiple] { min-height:150px; }
-    button { min-height:44px; margin-top: 10px; background: var(--accent); color:#211204; border: 0; font-weight: 800; cursor: pointer; }
-    button.secondary { background: #37495c; }
-    button.danger { background: #7b3434; }
-    button.compact { width:auto; min-width:120px; margin-right:8px; }
-    table { width: 100%; border-collapse: collapse; font-size: .92rem; }
-    td,th { padding: 8px; border-bottom: 1px solid #2d3a47; text-align: left; vertical-align: top; }
+    button { min-height:42px; margin-top:10px; background:var(--accent); color:#211204; border:0; font-weight:850; cursor:pointer; }
+    button.secondary { background:var(--complement-soft); color:#dff6ff; border:1px solid #22607c; }
+    button.danger { background:#2a3441; color:#f2f6fb; border:1px solid #536273; }
+    button.compact { width:auto; min-width:112px; margin-right:8px; padding:8px 10px; min-height:36px; }
+    table { width: 100%; border-collapse: collapse; table-layout:fixed; font-size: .92rem; }
+    td,th { padding: 8px; border-bottom: 1px solid #2d3a47; text-align: left; vertical-align: top; overflow-wrap:anywhere; word-break:break-word; }
     ul { margin: 8px 0; padding-left: 18px; }
-    .notice { border-left: 4px solid var(--accent); padding-left: 10px; }
+    .notice { border-left:4px solid var(--complement); padding-left:10px; }
     .grid-two { display:grid; grid-template-columns:repeat(auto-fit,minmax(240px,1fr)); gap:12px; }
     .chips { display:flex; flex-wrap:wrap; gap:6px; margin-top:8px; }
-    .chip { padding:4px 8px; border-radius:999px; background:#222831; border:1px solid var(--border); font-size:.85rem; }
-    .muted { color:#9fb0be; }
-    .card-list { display:grid; grid-template-columns:repeat(auto-fit,minmax(260px,1fr)); gap:10px; }
-    .actuator-card { background:#12161c; border:1px solid var(--border); border-radius:8px; padding:10px; min-width:0; }
-    .actuator-card.selected { border-color:var(--accent); box-shadow:0 0 0 1px rgba(255,138,28,.35); }
-    .card-title { display:flex; justify-content:space-between; gap:10px; align-items:flex-start; margin-bottom:8px; }
-    .entity-id { overflow-wrap:anywhere; font-weight:800; }
+    .chip { padding:4px 8px; border-radius:8px; background:#222b36; border:1px solid var(--border); font-size:.85rem; max-width:100%; overflow-wrap:anywhere; word-break:break-word; }
+    .muted { color:var(--text-soft); }
+    .card-list { display:grid; grid-template-columns:repeat(auto-fit,minmax(250px,1fr)); gap:10px; }
+    .actuator-card { background:#121922; border:1px solid var(--border); border-radius:8px; padding:10px; min-width:0; }
+    .actuator-card.selected { border-color:var(--complement); box-shadow:0 0 0 1px rgba(28,199,255,.35); }
+    .card-title { display:flex; flex-wrap:wrap; justify-content:space-between; gap:10px; align-items:flex-start; margin-bottom:8px; min-width:0; }
+    .card-title > div { min-width:0; flex:1 1 160px; overflow-wrap:anywhere; word-break:break-word; }
+    .card-title .chip { flex:0 1 auto; white-space:normal; text-align:center; }
+    .actuator-card strong { overflow-wrap:anywhere; word-break:break-word; }
+    .entity-id { overflow-wrap:anywhere; word-break:break-word; font-weight:800; }
     .metric-grid { display:grid; grid-template-columns:repeat(auto-fit,minmax(120px,1fr)); gap:6px; margin:8px 0; }
-    .metric { background:var(--panel-soft); border:1px solid var(--border); border-radius:8px; padding:8px; min-width:0; }
+    .metric { background:var(--panel-soft); border:1px solid var(--border); border-radius:8px; padding:8px; min-width:0; overflow-wrap:anywhere; word-break:break-word; }
     .metric strong { display:block; margin-bottom:4px; color:#cfe0ec; font-size:.84rem; }
+    .decision-list { display:grid; gap:8px; margin:10px 0; }
+    .decision-row { background:#121922; border:1px solid var(--border); border-radius:8px; padding:9px; min-width:0; overflow-wrap:anywhere; }
+    .decision-row header { padding:0; border:0; background:transparent; display:flex; justify-content:space-between; gap:10px; flex-wrap:wrap; }
     .actions { display:flex; flex-wrap:wrap; gap:8px; margin-top:10px; }
     .actions button { flex:1 1 180px; margin-top:0; }
     .detail-header { display:flex; justify-content:space-between; gap:12px; align-items:flex-start; flex-wrap:wrap; }
-    .manual-context { margin-top:12px; background:#12161c; border:1px solid var(--border); border-radius:8px; padding:10px; }
+    .manual-context { margin-top:12px; background:#121922; border:1px solid var(--border); border-radius:8px; padding:10px; min-width:0; overflow-wrap:anywhere; word-break:break-word; }
     .inline-controls { display:grid; grid-template-columns:repeat(auto-fit,minmax(160px,1fr)); gap:8px; margin:8px 0; }
     .manual-entry { min-height:80px; resize:vertical; }
-    textarea { box-sizing:border-box; width:100%; border-radius:10px; border:1px solid #3b4b5b; padding:12px; background:#101820; color:#fff; font:inherit; }
+    textarea { width:100%; border-radius:8px; border:1px solid #3b4b5b; padding:12px; background:#101820; color:#fff; font:inherit; }
     optgroup { color:#cfe0ec; background:#101820; }
     code { color:#cfe0ec; overflow-wrap:anywhere; }
+    .empty-state { min-height:140px; display:grid; place-items:center; text-align:center; border:1px dashed var(--border); border-radius:8px; background:#111821; color:var(--text-soft); padding:16px; }
     @media (max-width: 760px) {
-      header { padding:18px 14px; }
+      header { padding:16px 12px; }
       header h1 { font-size:1.55rem; }
+      .topbar { display:grid; }
+      .header-actions { min-width:0; }
+      .status-pill { width:max-content; max-width:100%; white-space:normal; }
       main { display:block; padding:8px; }
+      .control-panel { position:static; }
       section { margin-bottom:10px; padding:10px; border-radius:8px; }
       .steps { grid-template-columns:1fr; }
       .grid-two { grid-template-columns:1fr; }
@@ -80,8 +125,6 @@
       .card-list { grid-template-columns:1fr; }
       .actions { display:grid; grid-template-columns:1fr; }
       .actions button, button.compact { width:100%; min-width:0; margin-right:0; }
-      .quick-nav { padding:8px 10px; }
-      .quick-nav a { padding:10px 11px; }
     }
     @media (max-width: 430px) {
       .metric-grid { grid-template-columns:1fr; }
@@ -91,55 +134,34 @@
 </head>
 <body>
 <header>
-  <h1>SillyHome Next</h1>
-  <p>Hier wählst du nur Geräte aus, deren Bedienung SillyHome lernen soll. Sensoren, Zusammenhänge und Modelle werden automatisch verwaltet.</p>
-  <p class="notice">Sicherer Start: Zuerst wird nur beobachtet und vorhergesagt. Ohne deine spätere Freigabe wird nichts geschaltet.</p>
-</header>
-<nav class="quick-nav" aria-label="Schnellnavigation">
-  <a class="primary" href="#choose">Gerät wählen</a>
-  <a href="#observed">Beobachtet</a>
-  <a href="#detail">Details</a>
-  <a href="#status-section">Status</a>
-  <a href="#guide">Ablauf</a>
-</nav>
-<main>
-  <section class="wide" id="guide">
-    <details class="collapsible">
-    <summary><span>So gehst du vor</span></summary>
-    <div class="steps">
-      <div class="step">
-        <span class="step-number">1</span>
-        <h3>Aktor auswählen</h3>
-        <p><strong>Wo?</strong> Unten im Feld „Gerät auswählen“.</p>
-        <p><strong>Was passiert?</strong> SillyHome ordnet Raum, Sensoren, Zustände und vorhandene Historie automatisch zu.</p>
-      </div>
-      <div class="step">
-        <span class="step-number">2</span>
-        <h3>Wie gewohnt bedienen</h3>
-        <p><strong>Wo?</strong> Weiterhin in Home Assistant, an Schaltern oder über deine bisherigen Bedienwege.</p>
-        <p><strong>Was passiert?</strong> SillyHome lernt deine Handlungen und zeigt Vorhersagen an, schaltet aber noch nicht selbst.</p>
-      </div>
-      <div class="step">
-        <span class="step-number">3</span>
-        <h3>Später freigeben</h3>
-        <p><strong>Wo?</strong> In den Details des ausgewählten Geräts, sobald genug Verhalten gelernt wurde.</p>
-        <p><strong>Was passiert?</strong> Erst dann darf SillyHome passende Vorhersagen automatisch ausführen. Die Freigabe kann jederzeit gestoppt werden.</p>
+  <div class="topbar">
+    <div class="brand">
+      <div class="brand-row">
+        <span class="brand-mark">SH</span>
+        <h1>SillyHome Next</h1>
       </div>
+      <p>Arbeitsdashboard für gelernte Home-Assistant-Bedienung: Geräte auswählen, Lernstand prüfen, Freigaben steuern.</p>
+      <p class="notice">Sicherer Start: Zuerst wird nur beobachtet und vorhergesagt. Ohne deine spätere Freigabe wird nichts geschaltet.</p>
+    </div>
+    <div class="header-actions">
+      <label for="section-jump">Menü</label>
+      <select id="section-jump" onchange="jumpToSection(this.value)">
+        <option value="#choose">Steuerung</option>
+        <option value="#observed">Geräte</option>
+        <option value="#detail">Freigabe</option>
+        <option value="#status-section">System</option>
+        <option value="#guide">Ablauf</option>
+      </select>
+      <div class="status-pill"><span class="dot"></span><span id="load-budget">Seite bereit, Status folgt ...</span></div>
+    </div>
+  </div>
+</header>
+<main>
+  <section class="control-panel" id="choose">
+    <div class="panel-title">
+      <h2>Steuerung</h2>
+      <span class="chip">v1</span>
     </div>
-    </details>
-  </section>
-
-  <section id="status-section">
-    <h2>System & Cache</h2>
-    <p class="muted">Die Startansicht nutzt lokale Summaries und Cache-Daten. Home-Assistant-Discovery lädt im Hintergrund nach.</p>
-    <div id="status">Prüfung läuft ...</div>
-    <div class="chips" id="status-chips"></div>
-    <div id="dashboard-stats" class="metric-grid"></div>
-    <button class="secondary" onclick="loadOverview()">Dashboard aktualisieren</button>
-  </section>
-
-  <section id="choose">
-    <h2>1. Gerät zum Lernen auswählen</h2>
     <p class="muted">Wähle eine Lampe, einen Rollladen oder einen anderen unterstützten Aktor. Du wählst keine Sensoren und erstellst keine Regeln.</p>
     <label for="actuator-input">Entitätsname oder Gerät aus Home Assistant</label>
     <input id="actuator-input" list="actuator-options" placeholder="z. B. light.licht_abstellraum" autocomplete="off">
@@ -168,29 +190,80 @@
       </div>
       <div>
         <label for="actuator-search">Liste durchsuchen</label>
-        <input id="actuator-search" placeholder="Raum, Gerät oder Entity" oninput="renderActuatorSelect()" autocomplete="off">
+        <input id="actuator-search" placeholder="Raum, Gerät oder Entity" oninput="renderActuatorSelect()" onfocus="ensureActuatorDiscovery()" autocomplete="off">
       </div>
     </div>
     <label for="actuator-select">Oder aus Liste wählen</label>
-    <select id="actuator-select" onchange="selectActuatorFromList()">
-      <option value="">Geräteliste wird geladen ...</option>
+    <select id="actuator-select" onchange="selectActuatorFromList()" onfocus="ensureActuatorDiscovery()">
+      <option value="">Geräteliste bei Bedarf laden</option>
     </select>
     <button onclick="configureActuator()">Gerät hinzufügen und Beobachtung starten</button>
+    <button class="secondary" onclick="ensureActuatorDiscovery()">Geräteliste laden</button>
     <p id="actuator-config-result" class="muted">Noch kein Aktor ausgewählt.</p>
+    <details class="manual-context">
+      <summary>Vorschläge anzeigen</summary>
+      <p class="muted">Vorschläge können Home Assistant stark abfragen und werden deshalb nicht beim Start geladen.</p>
+      <button class="secondary" onclick="loadActuatorSuggestions()">Vorschläge laden</button>
+    </details>
     <div id="actuator-suggestions" class="card-list"></div>
   </section>
 
-  <section class="wide" id="observed">
-    <h2>2. Beobachtete Geräte</h2>
-    <p class="muted">Öffne „Details“, um Lernfortschritt, aktuelle Vorhersage und den automatisch gefundenen Kontext zu sehen.</p>
+  <section class="board-panel" id="observed">
+    <div class="panel-title">
+      <div>
+        <h2>Beobachtete Geräte</h2>
+        <p class="muted">Öffne „Details“, um Lernfortschritt, aktuelle Vorhersage und den automatisch gefundenen Kontext zu sehen.</p>
+      </div>
+      <button class="secondary compact" onclick="loadOverview()">Aktualisieren</button>
+    </div>
     <div id="configured-actuators">Noch nicht geladen.</div>
   </section>
 
-  <section class="wide" id="detail">
-    <h2>3. Lernfortschritt und Freigabe</h2>
+  <section class="detail-panel" id="detail">
+    <h2>Lernfortschritt und Freigabe</h2>
     <p class="muted">Die Freigabe erscheint erst, wenn genug eindeutig zugeordnete Handlungen gelernt wurden. Vorher bleibt das Gerät sicher im Beobachtungsmodus.</p>
     <div id="actuator-detail" class="muted">Öffne bei einem beobachteten Gerät die Details.</div>
   </section>
+
+  <section class="status-panel" id="status-section">
+    <div class="panel-title">
+      <div>
+        <h2>System & Cache</h2>
+        <p class="muted">Die Startansicht nutzt lokale Summaries und Cache-Daten. Home-Assistant-Discovery lädt erst bei Bedarf.</p>
+      </div>
+      <button class="secondary compact" onclick="loadStatus()">Status prüfen</button>
+    </div>
+    <div id="status">Prüfung läuft ...</div>
+    <div class="chips" id="status-chips"></div>
+    <div id="dashboard-stats" class="metric-grid"></div>
+    <div id="job-queue" class="decision-list"></div>
+  </section>
+
+  <section class="guide-panel" id="guide">
+    <details class="collapsible">
+    <summary><span>So gehst du vor</span></summary>
+    <div class="steps">
+      <div class="step">
+        <span class="step-number">1</span>
+        <h3>Aktor auswählen</h3>
+        <p><strong>Wo?</strong> Links im Feld „Gerät auswählen“.</p>
+        <p><strong>Was passiert?</strong> SillyHome ordnet Raum, Sensoren, Zustände und vorhandene Historie automatisch zu.</p>
+      </div>
+      <div class="step">
+        <span class="step-number">2</span>
+        <h3>Wie gewohnt bedienen</h3>
+        <p><strong>Wo?</strong> Weiterhin in Home Assistant, an Schaltern oder über deine bisherigen Bedienwege.</p>
+        <p><strong>Was passiert?</strong> SillyHome lernt deine Handlungen und zeigt Vorhersagen an, schaltet aber noch nicht selbst.</p>
+      </div>
+      <div class="step">
+        <span class="step-number">3</span>
+        <h3>Später freigeben</h3>
+        <p><strong>Wo?</strong> In den Details des ausgewählten Geräts, sobald genug Verhalten gelernt wurde.</p>
+        <p><strong>Was passiert?</strong> Erst dann darf SillyHome passende Vorhersagen automatisch ausführen. Die Freigabe kann jederzeit gestoppt werden.</p>
+      </div>
+    </div>
+    </details>
+  </section>
 </main>
 <script>
 const escapeHtml = value => String(value ?? "")
@@ -206,8 +279,16 @@ let manualContextState = {options: [], selected: new Set()};
 let cachedActuators = null;
 let cachedEntities = null;
 let cachedDiscovery = null;
+let discoveryLoadPromise = null;
+let currentSensorWeightGroups = [];
 const ACTUATOR_RESULT_LIMIT = 50;
-const STATUS_TIMEOUT_MS = 2500;
+const STATUS_TIMEOUT_MS = 2000;
+const DASHBOARD_TIMEOUT_MS = 4500;
+
+function jumpToSection(target) {
+  if (!target) return;
+  document.querySelector(target)?.scrollIntoView({behavior: "smooth", block: "start"});
+}
 
 function uniqueValues(values) {
   return [...new Set(values.filter(Boolean))];
@@ -352,21 +433,27 @@ function optionGroups(entities, selectedIds = new Set()) {
 }
 
 async function loadOverview() {
+  const startedAt = performance.now();
+  const budget = document.getElementById("load-budget");
+  if (budget) budget.textContent = "Startdaten laden ...";
   document.getElementById("configured-actuators").innerHTML = "<p class='muted'>Beobachtete Geräte werden geladen ...</p>";
   try {
-    const dashboard = await api("v1/actuators/dashboard");
+    const dashboard = await apiWithTimeout("v1/actuators/dashboard", DASHBOARD_TIMEOUT_MS);
     cachedActuators = dashboard.actuators || [];
     cachedEntities = [];
     renderDashboardStatus(dashboard);
     renderConfiguredActuators();
+    if (budget) budget.textContent = `Bereit in ${Math.round(performance.now() - startedAt)} ms`;
   } catch (error) {
     document.getElementById("configured-actuators").innerHTML = `<p class="bad">${escapeHtml(error.message)}</p>`;
-    await loadSummaryData();
-    renderConfiguredActuators();
-    void loadStatus();
+    if (budget) budget.textContent = "Startdaten verzögert";
+    try {
+      await loadSummaryData();
+      renderConfiguredActuators();
+    } catch (_) {
+      document.getElementById("configured-actuators").innerHTML = "<div class='empty-state'>Startdaten sind gerade nicht verfügbar.</div>";
+    }
   }
-  void loadActuatorDiscovery();
-  void loadActuatorSuggestions();
 }
 
 async function loadStatus() {
@@ -374,17 +461,16 @@ async function loadStatus() {
   const chips = document.getElementById("status-chips");
   status.innerHTML = "<p class='muted'>Status wird geprüft ...</p>";
   try {
-    const [health, websocket, ml, reconciliation, actuators] = await Promise.allSettled([
+    const [health, websocket, ml, reconciliation] = await Promise.allSettled([
       apiWithTimeout("health"),
       apiWithTimeout("health/websocket"),
       apiWithTimeout("ml/health"),
       apiWithTimeout("v1/actuators/reconciliation/state"),
-      apiWithTimeout("v1/actuators/summary"),
     ]);
-    const values = [health, websocket, ml, reconciliation, actuators].map(result =>
+    const values = [health, websocket, ml, reconciliation].map(result =>
       result.status === "fulfilled" ? result.value : null
     );
-    const [healthValue, websocketValue, mlValue, reconciliationValue, actuatorValue] = values;
+    const [healthValue, websocketValue, mlValue, reconciliationValue] = values;
     const hasError = values.some(value => value === null);
     status.innerHTML = hasError
       ? "<p class='warn'>Status teilweise verfügbar. Das Dashboard bleibt bedienbar.</p>"
@@ -393,7 +479,6 @@ async function loadStatus() {
       `<span class="chip">API: ${escapeHtml(healthValue?.status || "offen")}</span>`,
       `<span class="chip">WebSocket: ${escapeHtml(websocketValue?.status || "offen")}</span>`,
       `<span class="chip">Lernsystem: ${escapeHtml(mlValue?.status || "offen")}</span>`,
-      `<span class="chip">Aktoren: ${Array.isArray(actuatorValue) ? actuatorValue.length : "offen"}</span>`,
       `<span class="chip">Lernbereite Geräte: ${escapeHtml(reconciliationValue?.trained_models ?? "offen")}</span>`,
     ].join("");
   } catch (error) {
@@ -406,9 +491,21 @@ function renderDashboardStatus(dashboard) {
   const status = document.getElementById("status");
   const chips = document.getElementById("status-chips");
   const stats = document.getElementById("dashboard-stats");
+  const jobsBox = document.getElementById("job-queue");
   const system = dashboard.system || {};
   const cache = dashboard.cache || {};
+  const actuators = dashboard.actuators || [];
   const discoveryGroups = dashboard.discovery_groups || [];
+  const activeCount = actuators.filter(record => record.behavior_mode === "active").length;
+  const shadowCount = actuators.filter(record => record.behavior_mode !== "active").length;
+  const readyCount = actuators.filter(record => record.activation_ready).length;
+  const pendingCount = actuators.filter(record =>
+    ["pending_history", "pending_assignment", "archived"].includes(record.lifecycle_status)
+  ).length;
+  const trainedCount = actuators.filter(record => record.behavior_status === "trained").length;
+  const sampleTotal = actuators.reduce((sum, record) => sum + Number(record.sample_count || 0), 0);
+  const jobs = dashboard.jobs?.jobs || [];
+  const runningJobs = jobs.filter(job => job.status === "running").length;
   const cacheLabel = cache.available
     ? `Cache aktuell mit ${cache.entity_count} Entities`
     : "Cache wird nach Discovery aufgebaut";
@@ -424,12 +521,32 @@ function renderDashboardStatus(dashboard) {
     `<span class="chip">Aktoren: ${escapeHtml(system.configured_actuators ?? 0)}</span>`,
     `<span class="chip">Lernbereit: ${escapeHtml(system.trained_models ?? 0)}</span>`,
     `<span class="chip">Prüfen: ${escapeHtml(system.review_required ?? 0)}</span>`,
+    `<span class="chip">Jobs aktiv: ${escapeHtml(runningJobs)}</span>`,
   ].join("");
   stats.innerHTML = [
-    `<div class="metric"><strong>Geladene Startdaten</strong>${escapeHtml((dashboard.actuators || []).length)} Geräte</div>`,
+    `<div class="metric"><strong>Geladene Startdaten</strong>${escapeHtml(actuators.length)} Geräte</div>`,
+    `<div class="metric"><strong>Freigabebereit</strong>${escapeHtml(readyCount)} Geräte</div>`,
+    `<div class="metric"><strong>Aktiv / Shadow</strong>${escapeHtml(activeCount)} / ${escapeHtml(shadowCount)}</div>`,
+    `<div class="metric"><strong>Gelernt / Wartet</strong>${escapeHtml(trainedCount)} / ${escapeHtml(pendingCount)}</div>`,
+    `<div class="metric"><strong>Gelernte Handlungen</strong>${escapeHtml(sampleTotal)}</div>`,
     `<div class="metric"><strong>Discovery-Gruppen</strong>${escapeHtml(discoveryGroups.length)} Kategorien</div>`,
     `<div class="metric"><strong>Cache-Zeitpunkt</strong>${escapeHtml(cache.updated_at || "noch offen")}</div>`,
   ].join("");
+  jobsBox.innerHTML = jobs.length ? `
+    <h3>Job-Queue</h3>
+    ${jobs.slice(-6).reverse().map(job => `
+      <div class="decision-row">
+        <header>
+          <strong>${escapeHtml(job.kind)}${job.target ? `: ${escapeHtml(job.target)}` : ""}</strong>
+          <span class="chip">${escapeHtml(job.status)}</span>
+        </header>
+        <p class="muted">${escapeHtml(job.summary || "Keine Zusammenfassung")}</p>
+        <p class="muted">Start: ${escapeHtml(job.started_at || "offen")} · Dauer: ${escapeHtml(job.duration_ms == null ? "läuft/offen" : `${job.duration_ms} ms`)}</p>
+        ${job.error ? `<p class="bad">${escapeHtml(job.error)}</p>` : ""}
+        ${job.status === "failed" ? "<p class='warn'>Retry: Aktion im Dashboard erneut starten; der nächste Lauf schreibt einen neuen Queue-Eintrag.</p>" : ""}
+      </div>
+    `).join("")}
+  ` : "";
 }
 
 async function loadSummaryData() {
@@ -472,6 +589,21 @@ async function loadActuatorDiscovery() {
   renderActuatorDiscovery();
 }
 
+async function ensureActuatorDiscovery() {
+  const select = document.getElementById("actuator-select");
+  if (cachedDiscovery) {
+    renderActuatorDiscovery();
+    return;
+  }
+  if (!discoveryLoadPromise) {
+    if (select) select.innerHTML = `<option value="">Geräteliste wird geladen ...</option>`;
+    discoveryLoadPromise = loadActuatorDiscovery().finally(() => {
+      discoveryLoadPromise = null;
+    });
+  }
+  await discoveryLoadPromise;
+}
+
 function renderActuatorDiscovery() {
   const options = document.getElementById("actuator-options");
   const select = document.getElementById("actuator-select");
@@ -607,7 +739,11 @@ async function configureActuator() {
     || document.getElementById("actuator-select").value.trim()
   );
   const result = document.getElementById("actuator-config-result");
-  if (!actuatorId) return;
+  if (!actuatorId) {
+    await ensureActuatorDiscovery();
+    result.textContent = "Wähle ein Gerät aus der geladenen Liste oder trage eine Entity-ID ein.";
+    return;
+  }
   result.textContent = "Kontext wird automatisch analysiert ...";
   try {
     const record = await api("v1/actuators", {
@@ -644,7 +780,7 @@ function renderConfiguredActuators() {
     const groupedRows = [...groups.entries()].sort(([left], [right]) => left.localeCompare(right));
     box.innerHTML = rows.length ? `
       ${groupedRows.map(([group, items]) => `
-        <details class="group-panel" open>
+        <details class="group-panel">
         <summary>${escapeHtml(group)} (${items.length})</summary>
         <div class="card-list">
         ${items.map(({record}) => `
@@ -684,14 +820,10 @@ function renderConfiguredActuators() {
 async function showActuator(actuatorId, evaluationMessage = "") {
   currentActuatorId = actuatorId;
   const box = document.getElementById("actuator-detail");
+  renderActuatorDetailShell(actuatorId);
   try {
-    let record;
-    try {
-      record = await api(`v1/actuators/${encodeURIComponent(actuatorId)}/related-automations/refresh`, {method: "POST"});
-    } catch (_) {
-      record = await api(`v1/actuators/${encodeURIComponent(actuatorId)}`);
-    }
-    await loadContextOptions(actuatorId);
+    const record = await api(`v1/actuators/${encodeURIComponent(actuatorId)}`);
+    contextOptions = [];
     const contexts = [
       record.assignment.selected_numeric_entity_id,
       ...record.assignment.selected_context_entity_ids,
@@ -700,6 +832,62 @@ async function showActuator(actuatorId, evaluationMessage = "") {
       .filter(candidate => contexts.includes(candidate.entity_id))
       .map(candidate => `<li><strong>${escapeHtml(candidate.friendly_name || candidate.entity_id)}</strong>: ${uniqueValues(candidate.evidence).map(escapeHtml).join(", ") || "statistisch relevanter Kandidat"}</li>`)
       .join("");
+    const weightedCandidates = [...record.numeric_candidates, ...record.context_candidates]
+      .filter(candidate => contexts.includes(candidate.entity_id));
+    const weightGroups = record.manual_override?.sensor_weight_groups || [];
+    currentSensorWeightGroups = weightGroups;
+    const weightControls = weightedCandidates.length ? `
+      <div class="card-list">
+        ${weightedCandidates.map(candidate => {
+          const relevance = Math.round((candidate.confidence ?? 0) * 100);
+          const effective = Math.round((candidate.effective_weight ?? 1) * 100);
+          const manual = candidate.manual_weight == null ? effective : Math.round(candidate.manual_weight * 100);
+          return `
+            <article class="actuator-card">
+              <div class="card-title">
+                <div>
+                  <div><strong>${escapeHtml(candidate.friendly_name || candidate.entity_id)}</strong></div>
+                  <div class="entity-id">${escapeHtml(candidate.entity_id)}</div>
+                </div>
+                <span class="chip">${relevance} % relevant</span>
+              </div>
+              <div class="metric-grid">
+                <div class="metric"><strong>Automatische Relevanz</strong>${relevance} %</div>
+                <div class="metric"><strong>Aktive Gewichtung</strong>${effective} %</div>
+                <div class="metric"><strong>Score</strong>${escapeHtml(candidate.score)}</div>
+              </div>
+              <label for="weight-${escapeHtml(candidate.entity_id)}">Gewichtung korrigieren</label>
+              <input id="weight-${escapeHtml(candidate.entity_id)}" data-weight-entity="${escapeHtml(candidate.entity_id)}" type="number" min="0" max="100" step="5" value="${manual}">
+            </article>
+          `;
+        }).join("")}
+      </div>
+      <div class="actions">
+        <button onclick="saveWeightOverrides('${escapeHtml(record.actuator_entity_id)}')">Gewichtungen speichern</button>
+      </div>
+    ` : "<p class='muted'>Noch keine verwendeten Sensoren oder Zustände für eine Gewichtung ausgewählt.</p>";
+    const weightGroupControls = `
+      <details class="manual-context">
+        <summary>Gruppen-Gewichtung</summary>
+        ${weightGroups.length ? `<ul>${weightGroups.map(group => `
+          <li><strong>${escapeHtml(group.name)}</strong>: ${Math.round(group.weight * 100)} %
+          <span class="muted">${group.entity_ids.map(escapeHtml).join(", ")}</span></li>
+        `).join("")}</ul>` : "<p class='muted'>Noch keine Gruppe gespeichert.</p>"}
+        <div class="inline-controls">
+          <div>
+            <label for="weight-group-name">Gruppenname</label>
+            <input id="weight-group-name" placeholder="z. B. Flur Bewegung + Helligkeit">
+          </div>
+          <div>
+            <label for="weight-group-value">Gruppen-Gewicht in %</label>
+            <input id="weight-group-value" type="number" min="0" max="100" step="5" value="100">
+          </div>
+        </div>
+        <label for="weight-group-entities">Entity-IDs der Gruppe</label>
+        <textarea id="weight-group-entities" class="manual-entry" placeholder="Eine oder mehrere Entity-IDs">${escapeHtml(contexts.join("\n"))}</textarea>
+        <button class="secondary" onclick="saveWeightOverrides('${escapeHtml(record.actuator_entity_id)}', true)">Als Gruppe speichern</button>
+      </details>
+    `;
     const currentContextControls = contexts.length
       ? `<ul>${contexts.map(entityId => `
           <li>
@@ -709,6 +897,71 @@ async function showActuator(actuatorId, evaluationMessage = "") {
         `).join("")}</ul>`
       : "<p class='muted'>Noch keine Kontext-Entity ausgewählt.</p>";
     const prediction = record.behavior.prediction;
+    const safety = record.behavior.safety || {};
+    const blockers = record.behavior.safety_blockers || [];
+    const decisionFactors = record.behavior.decision_factors || [];
+    const knowledge = record.behavior.knowledge || [];
+    const assumptions = record.behavior.assumptions || [];
+    const uncertainties = record.behavior.uncertainties || [];
+    const safetyControls = `
+      <details class="manual-context" open>
+        <summary>Sicherheit und manuelles Gegensteuern</summary>
+        <div class="inline-controls">
+          <div>
+            <label for="safety-stage">Freigabestufe</label>
+            <select id="safety-stage">
+              ${["observe", "suggest", "shadow", "partial", "active"].map(stage => `
+                <option value="${stage}" ${safety.stage === stage ? "selected" : ""}>${stage}</option>
+              `).join("")}
+            </select>
+          </div>
+          <div>
+            <label for="safety-confidence">Mindest-Sicherheit in %</label>
+            <input id="safety-confidence" type="number" min="0" max="100" step="1" value="${Math.round((safety.min_confidence ?? 0.82) * 100)}">
+          </div>
+          <div>
+            <label for="safety-cooldown">Cooldown Sekunden</label>
+            <input id="safety-cooldown" type="number" min="0" step="10" value="${safety.cooldown_seconds ?? ""}" placeholder="Standard">
+          </div>
+        </div>
+        <label>
+          <input id="safety-manual-block" type="checkbox" ${safety.manual_block ? "checked" : ""}>
+          Manuelle Sicherheitssperre aktiv
+        </label>
+        ${blockers.length ? `<p class="warn">Aktuelle Blocker: ${blockers.map(escapeHtml).join(" ")}</p>` : "<p class='ok'>Keine lokalen Sicherheitsblocker für die aktuelle Vorhersage.</p>"}
+        <button class="secondary" onclick="saveSafetyProfile('${escapeHtml(record.actuator_entity_id)}')">Sicherheitsprofil speichern</button>
+      </details>
+    `;
+    const decisionArchive = `
+      <details class="manual-context" open>
+        <summary>Entscheidungsakte</summary>
+        <div class="grid-two">
+          <div>
+            <h3>Wissen</h3>
+            <ul>${knowledge.map(item => `<li>${escapeHtml(item)}</li>`).join("") || "<li>Keine gesicherten Punkte gespeichert.</li>"}</ul>
+          </div>
+          <div>
+            <h3>Annahmen</h3>
+            <ul>${assumptions.map(item => `<li>${escapeHtml(item)}</li>`).join("") || "<li>Keine Annahmen gespeichert.</li>"}</ul>
+          </div>
+        </div>
+        <h3>Unsicherheit</h3>
+        <ul>${uncertainties.map(item => `<li>${escapeHtml(item)}</li>`).join("") || "<li>Keine Unsicherheit gespeichert.</li>"}</ul>
+        <h3>Beitragsfaktoren</h3>
+        <div class="decision-list">
+          ${decisionFactors.length ? decisionFactors.map(factor => `
+            <div class="decision-row">
+              <header>
+                <strong>${escapeHtml(factor.label)}</strong>
+                <span class="chip">${Math.round((factor.contribution || 0) * 100)} % Beitrag</span>
+              </header>
+              <p class="muted">${escapeHtml(factor.entity_id || factor.factor_type)} · Zustand: ${escapeHtml(factor.state || "offen")} · Gewicht: ${Math.round((factor.weight || 0) * 100)} %</p>
+              <p>${(factor.evidence || []).map(escapeHtml).join(" ")}</p>
+            </div>
+          `).join("") : "<p class='muted'>Noch keine aktuelle Entscheidungsfaktoren berechnet.</p>"}
+        </div>
+      </details>
+    `;
     const learnedAutomationActions = record.behavior.patterns.filter(
       pattern => pattern.source === "automation",
     ).length;
@@ -729,7 +982,7 @@ async function showActuator(actuatorId, evaluationMessage = "") {
       selected: manualContextIds,
     };
     const manualAssignment = `
-      <details class="manual-context" open>
+      <details class="manual-context">
         <summary>Kontext selbst festlegen</summary>
         <p class="muted">Die Vorschläge sind aktorbezogen vorsortiert. Wenn etwas fehlt, trage die Entity-ID unten manuell ein, z. B. PIR, Helligkeit außen, Luftfeuchtigkeit oder Lichtzustände.</p>
         <label for="manual-numeric-select">Optionaler Haupt-Messsensor</label>
@@ -758,7 +1011,7 @@ async function showActuator(actuatorId, evaluationMessage = "") {
         <textarea id="manual-context-freeform" class="manual-entry" placeholder="Eine oder mehrere Entity-IDs, getrennt durch Komma, Leerzeichen oder neue Zeilen">${escapeHtml(manualOnlyIds.join("\n"))}</textarea>
         <div class="actions">
           <button onclick="saveManualAssignment('${escapeHtml(record.actuator_entity_id)}')">Diese Kontext-Auswahl speichern</button>
-          <button class="secondary" onclick="loadContextOptions('${escapeHtml(record.actuator_entity_id)}').then(() => showActuator('${escapeHtml(record.actuator_entity_id)}'))">Vorschläge neu laden</button>
+          <button class="secondary" onclick="hydrateCurrentContextOptions('${escapeHtml(record.actuator_entity_id)}')">Vorschläge neu laden</button>
         </div>
       </details>
     `;
@@ -818,21 +1071,116 @@ async function showActuator(actuatorId, evaluationMessage = "") {
         <button class="secondary" onclick="sendFeedback('${escapeHtml(record.actuator_entity_id)}', true)">Vorhersage korrekt</button>
         <button class="secondary" onclick="sendFeedback('${escapeHtml(record.actuator_entity_id)}', false)">Vorhersage falsch</button>
       </div>
+      ${safetyControls}
+      ${decisionArchive}
       <h3>Passende Home-Assistant-Automationen</h3>
       <p class="muted">Bei einer Übernahme pausiert SillyHome diese Automationen. Beim Stoppen können sie gezielt fortgesetzt werden.</p>
+      <button class="secondary compact" onclick="refreshRelatedAutomations('${escapeHtml(record.actuator_entity_id)}')">Automationen neu suchen</button>
       ${automationControls}
       <h3>Welche Zusammenhänge automatisch verwendet werden</h3>
       ${evidence ? `<ul>${evidence}</ul>` : "<p class='warn'>Noch kein geeigneter Kontext erkannt. SillyHome prüft bei neuen HA-Daten erneut.</p>"}
+      <h3>Sensor-Gewichtung</h3>
+      <p class="muted">Automatische Relevanz kommt aus der Zuordnung. Die aktive Gewichtung kannst du korrigieren; Gruppen bündeln mehrere Sensoren/Zustände.</p>
+      ${weightControls}
+      ${weightGroupControls}
       <h3>Verwendete Sensoren/Zustände ändern</h3>
       ${currentContextControls}
       ${manualAssignment}
     `;
+    void hydrateContextOptions(record);
     document.getElementById("detail").scrollIntoView({behavior: "smooth", block: "start"});
   } catch (error) {
     box.textContent = error.message;
   }
 }
 
+function renderActuatorDetailShell(actuatorId) {
+  document.getElementById("actuator-detail").innerHTML = `
+    <div class="detail-header">
+      <div>
+        <h3>${escapeHtml(actuatorId)}</h3>
+        <p class="muted">Basisdaten werden geladen ...</p>
+      </div>
+    </div>
+    <div class="metric-grid">
+      <div class="metric"><strong>Phase 1</strong>Aktuelle Einstellung</div>
+      <div class="metric"><strong>Phase 2</strong>Lernstand</div>
+      <div class="metric"><strong>Phase 3</strong>Kontextvorschläge</div>
+    </div>
+  `;
+}
+
+async function hydrateContextOptions(record) {
+  await loadContextOptions(record.actuator_entity_id);
+  const manualContextSelect = document.getElementById("manual-context-select");
+  const numericSelect = document.getElementById("manual-numeric-select");
+  const categorySelect = document.getElementById("manual-context-category");
+  if (!manualContextSelect || !numericSelect || !categorySelect) return;
+  const manualContextIds = new Set(record.assignment.selected_context_entity_ids || []);
+  const numericOptions = contextOptions.filter(entity => entity.domain === "sensor");
+  const contextCategories = [...new Set(contextOptions
+    .filter(entity => entity.entity_id !== record.actuator_entity_id)
+    .map(categoryForEntity))]
+    .sort();
+  manualContextState = {
+    options: contextOptions.filter(entity => entity.entity_id !== record.actuator_entity_id),
+    selected: manualContextIds,
+  };
+  numericSelect.innerHTML = `
+    <option value="">Keinen numerischen Hauptsensor verwenden</option>
+    ${optionGroups(numericOptions, new Set([record.assignment.selected_numeric_entity_id].filter(Boolean)))}
+  `;
+  categorySelect.innerHTML = `
+    <option value="">Alle relevanten Vorschläge</option>
+    ${contextCategories.map(category => `<option value="${escapeHtml(category)}">${escapeHtml(category)}</option>`).join("")}
+  `;
+  renderManualContextSelect();
+}
+
+async function hydrateCurrentContextOptions(actuatorId) {
+  const record = await api(`v1/actuators/${encodeURIComponent(actuatorId)}`);
+  await hydrateContextOptions(record);
+}
+
+async function saveWeightOverrides(actuatorId, includeNewGroup = false) {
+  const sensorWeights = {};
+  for (const input of document.querySelectorAll("[data-weight-entity]")) {
+    const value = Number(input.value);
+    if (Number.isFinite(value)) {
+      sensorWeights[input.dataset.weightEntity] = Math.max(0, Math.min(100, value)) / 100;
+    }
+  }
+  const groups = [...currentSensorWeightGroups];
+  if (includeNewGroup) {
+    const name = document.getElementById("weight-group-name")?.value.trim();
+    const value = Number(document.getElementById("weight-group-value")?.value || 100);
+    const entityIds = parseEntityIds(document.getElementById("weight-group-entities")?.value || "");
+    if (name && entityIds.length) {
+      groups.push({
+        group_id: name.toLowerCase().replace(/[^a-z0-9]+/g, "_").replace(/^_+|_+$/g, "").slice(0, 64) || "gruppe",
+        name,
+        entity_ids: entityIds,
+        weight: Math.max(0, Math.min(100, Number.isFinite(value) ? value : 100)) / 100,
+      });
+    }
+  }
+  try {
+    await api(`v1/actuators/${encodeURIComponent(actuatorId)}/weights`, {
+      method: "POST",
+      body: JSON.stringify({
+        sensor_weights: sensorWeights,
+        sensor_weight_groups: groups,
+        note: "Gewichtung im Dashboard korrigiert",
+      }),
+    });
+    invalidateDashboardCache();
+    await loadConfiguredActuators();
+    await showActuator(actuatorId, "Sensor-Gewichtung gespeichert.");
+  } catch (error) {
+    alert(error.message);
+  }
+}
+
 async function saveManualAssignment(actuatorId) {
   const numericEntityId = document.getElementById("manual-numeric-select").value || null;
   const selectedContextIds = Array.from(
@@ -927,6 +1275,36 @@ async function sendFeedback(actuatorId, correct) {
   }
 }
 
+async function saveSafetyProfile(actuatorId) {
+  const confidence = Number(document.getElementById("safety-confidence")?.value || 82);
+  const cooldownRaw = document.getElementById("safety-cooldown")?.value || "";
+  const cooldown = cooldownRaw === "" ? null : Math.max(0, Number(cooldownRaw));
+  const profile = {
+    stage: document.getElementById("safety-stage")?.value || "shadow",
+    manual_block: Boolean(document.getElementById("safety-manual-block")?.checked),
+    min_confidence: Math.max(0, Math.min(100, Number.isFinite(confidence) ? confidence : 82)) / 100,
+    cooldown_seconds: Number.isFinite(cooldown) ? cooldown : null,
+    rules: [
+      {rule_id: "activation_ready", label: "Nur nach Lernfreigabe aktiv schalten", enabled: true, blocking: true, reason: "Der Aktor muss genug eindeutiges Verhalten gelernt haben."},
+      {rule_id: "confidence_threshold", label: "Mindest-Sicherheit einhalten", enabled: true, blocking: true, reason: "Vorhersagen unter der Schaltschwelle bleiben im Shadow-Modus."},
+      {rule_id: "cooldown", label: "Sicherheits-Cooldown gegen Hin-und-her-Schalten", enabled: true, blocking: true, reason: "Gleiche Zielzustände werden nicht zu schnell wiederholt."},
+      {rule_id: "manual_block", label: "Manuelle Sperre respektieren", enabled: true, blocking: true, reason: "Nutzer können jeden Aktor sofort blockieren."},
+    ],
+    note: "Sicherheitsprofil im Dashboard gespeichert",
+  };
+  try {
+    await api(`v1/actuators/${encodeURIComponent(actuatorId)}/safety`, {
+      method: "POST",
+      body: JSON.stringify({safety: profile}),
+    });
+    invalidateDashboardCache();
+    await loadConfiguredActuators();
+    await showActuator(actuatorId, "Sicherheitsprofil gespeichert.");
+  } catch (error) {
+    alert(error.message);
+  }
+}
+
 async function setActivation(actuatorId, active, pauseMatchingAutomations, restorePausedAutomations) {
   const question = active
     ? pauseMatchingAutomations
@@ -972,6 +1350,17 @@ async function setRelatedAutomation(actuatorId, automationEntityId, enabled) {
   }
 }
 
+async function refreshRelatedAutomations(actuatorId) {
+  try {
+    await api(`v1/actuators/${encodeURIComponent(actuatorId)}/related-automations/refresh`, {method: "POST"});
+    invalidateDashboardCache();
+    await loadConfiguredActuators();
+    await showActuator(actuatorId, "Passende Home-Assistant-Automationen neu geprüft.");
+  } catch (error) {
+    alert(error.message);
+  }
+}
+
 async function removeActuator(actuatorId) {
   if (!confirm(`${actuatorId} aus SillyHome entfernen?`)) return;
   try {
@@ -986,7 +1375,16 @@ async function removeActuator(actuatorId) {
   }
 }
 
-loadOverview();
+async function startDashboard() {
+  document.getElementById("status").innerHTML = "<p class='muted'>Status lädt nach ...</p>";
+  document.getElementById("configured-actuators").innerHTML = "<div class='empty-state'>Geräte werden nach dem Status geladen.</div>";
+  document.getElementById("actuator-detail").innerHTML = "<div class='empty-state'>Wähle später ein Gerät aus der Übersicht.</div>";
+  await new Promise(resolve => requestAnimationFrame(resolve));
+  await loadStatus();
+  await loadOverview();
+}
+
+void startDashboard();
 </script>
 </body>
 </html>

docs/V1_0_0_OPERATING_GUIDE.md

@@ -4,6 +4,10 @@ Diese Version stabilisiert den produktiven Kern: schnelle Dashboard-Nutzung,
 lokales Caching, klare Aktor-/Sensor-Kategorien und nachvollziehbare Freigabe
 gelernter Aktionen.
 
+Die detaillierte Abnahme steht in
+[`V1_0_ACCEPTANCE.md`](V1_0_ACCEPTANCE.md). Dort sind erledigte, teilweise
+erledigte und fuer v1.0.x offene Punkte getrennt dokumentiert.
+
 ## Grundprinzip
 
 - Home Assistant bleibt die Quelle fuer aktuelle States und Services.
@@ -97,6 +101,21 @@ wget -qO /tmp/summary.json http://58adbe1e-sillyhome-next:8000/v1/actuators/summ
 wget -qO /tmp/dashboard.json http://58adbe1e-sillyhome-next:8000/v1/actuators/dashboard
 ```
 
+Wenn der Add-on-Container aus dem Agent-Host nicht direkt routbar ist, gilt der
+Home-Assistant-Supervisor als Verifikationsquelle:
+
+- Add-on-Info pruefen: Version, `version_latest`, `update_available`, `state`,
+  `boot` und `watchdog`.
+- Vor Updates eine Home-Assistant-Teil-Sicherung fuer **SillyHome Next**
+  erstellen.
+- Nach einem Store-Reload und Update muss `version == version_latest`,
+  `update_available == false`, `state == started`, `boot == auto` und
+  `watchdog == true` gelten.
+- Den HA-/Ingress-Tab nach jedem Update hart neu laden, weil Home Assistant
+  sonst alte HTML-/JavaScript-Ressourcen aus dem bestehenden Tab verwenden kann.
+- Rollback erfolgt ueber die vorherige Add-on-Teil-Sicherung oder den letzten
+  Git-Tag; beide Referenzen im Release-/Abnahmeprotokoll notieren.
+
 ## Rollback
 
 Der stabile Vor-1.0-Stand ist `v0.7.21`. Vor dem 1.0.0-Umbau wurde ein

docs/V1_0_ACCEPTANCE.md

@@ -0,0 +1,82 @@
+# SillyHome Next v1.0 Acceptance
+
+Stand: 2026-06-17
+
+Diese Abnahme trennt belegte Umsetzung von offenen v1.0.x-Nacharbeiten. Der
+Funktionskern bleibt aktorzentriert: Nutzer waehlen Aktoren, SillyHome lernt
+Kontext und Verhalten, laeuft zuerst im Shadow-Modus und schaltet erst nach
+expliziter Freigabe.
+
+## Erfuellt
+
+- Versioniert, gepusht und installiert:
+  - `v1.0.0`: API-/Cache-Umbau
+  - `v1.0.1`: Dashboard-/Performance-Korrektur
+- Startpfad:
+  - `/v1/actuators/dashboard` liefert lokale Startdaten aus Store und Cache.
+  - Dashboard blockiert nicht mehr auf Discovery, Vorschlaegen oder
+    Automation-Refresh.
+  - Frontend bricht den Startdaten-Request nach 4,5 Sekunden ab und bleibt
+    bedienbar.
+- Cache:
+  - HA-Entity-Metadaten werden als `ha_entity_cache.json` gespeichert.
+  - Summary und Dashboard verwenden Friendly Name, Area und Device aus Cache.
+- Keine externen Abfragen im Dashboard-Startpfad:
+  - Kein Cloud-Ping, keine Fremd-API.
+  - HA-Zugriffe bleiben lokal gegen Home Assistant.
+- Dashboard:
+  - Orange ist Primaerfarbe.
+  - Cyan ist sichtbare Komplementaerfarbe.
+  - Rote UI-Flaechen wurden entfernt.
+  - Steuerung, beobachtete Geraete, Lernfortschritt/Freigabe und Systemstatus
+    sind getrennte Bereiche.
+  - Discovery, Vorschlaege und Automation-Suche laden erst bei Nutzeraktion.
+- Lernfortschritt und Freigabe:
+  - Karten zeigen Modus, Status, Handlungen, Vorhersage und Freigabestatus.
+  - Detailansicht zeigt Zuordnung, Sicherheit, Lernstand, Vorhersage,
+    Feedback, passende HA-Automationen und verwendete Sensoren/Zustaende.
+- Direkte HA-Nutzung:
+  - Aktor-Schaltungen laufen ueber Home-Assistant-Serviceaufrufe.
+  - Automation-Steuerung nutzt Home-Assistant-Endpunkte und gecachte
+    Automation-Metadaten.
+- Qualitaet:
+  - `pytest -q`
+  - `ruff check .`
+  - `mypy app backend tests`
+  - `git diff --check`
+- Performance-Budget:
+  - Automatisierter Test prueft Root-HTML und `/v1/actuators/dashboard` gegen
+    das 5-Sekunden-Budget mit kontrollierten Fake-HA-/Cache-Daten.
+- HA-/Ingress-Verifikation:
+  - Supervisor-Update, Add-on-Status, Watchdog, Backup, Ingress-Hard-Reload
+    und Rollback sind im Operating Guide dokumentiert.
+
+## Teilweise Erfuellt
+
+- Bessere Statistik:
+  - Startbereich zeigt Aktoren, Freigabebereitschaft, Aktiv/Shadow,
+    Gelernt/Wartet, gelernte Handlungen, Discovery-Gruppen und Cache-Zeitpunkt.
+  - Noch offen: Verlaufsgrafiken, p95-Latenzen und Trendstatistik je Aktor.
+- Kontrollierte Abarbeitung und Queue:
+  - Reconciliation/Training laufen kontrolliert im Prozess und sind testbar.
+  - Noch offen: sichtbare Job-Queue mit Laufzeit, Fehlern und Retry-Status im
+    Dashboard.
+- Saubere Issues:
+  - v1.0.0-Issues #41 bis #47 wurden geschlossen.
+  - Rueckblickend waren sie zu grob; v1.0.x bekommt feinere Folgeissues fuer
+    Statistik, Queue-Sichtbarkeit und Performance-Budgets.
+
+## Offen Fuer v1.0.x
+
+- Dashboard-Jobstatus fuer Reconciliation, Training, Discovery und
+  Automation-Refresh.
+- Mehr Entscheidungsstatistik pro Aktor: welche Sensoren wie stark
+  beigetragen haben, wie sich Confidence und Sample Count entwickeln.
+
+## Rollback
+
+- Git-Bundle-Backups liegen unter
+  `/root/.openclaw/workspace/backups/sillyhome-next/`.
+- Vor `v1.0.1` wurde ein Home-Assistant-Teilbackup des Add-ons angelegt.
+  Referenz: `18a5b387`.
+- Letzter Vor-1.0-Stand: `v0.7.21`.

docs/V1_1_0_OPERATING_GUIDE.md

@@ -0,0 +1,72 @@
+# SillyHome Next v1.1.0 Operating Guide
+
+## Ziel
+
+v1.1.0 macht das Dashboard zur Zentrale fuer Visualisierung, Einrichtung,
+Sicherheit und manuelles Gegensteuern. Autonomes Schalten bleibt ein kurzer
+lokaler Pfad: Vorhersage und Safety-Profil werden aus bereits vorhandenen Daten
+bewertet, danach folgt direkt der Home-Assistant-Serviceaufruf.
+
+## Sicherheitsmodell
+
+Jeder Aktor hat ein Safety-Profil:
+
+- `stage`: Beobachten, Vorschlagen, Shadow, Teilaktiv oder Aktiv.
+- `manual_block`: harte manuelle Sperre.
+- `min_confidence`: Mindest-Sicherheit fuer autonomes Schalten.
+- `cooldown_seconds`: optionaler Aktor-Cooldown gegen schnelles Hin-und-her.
+- Safety-Regeln: Freigabe, Confidence, Cooldown und manuelle Sperre.
+
+Ein Aktor schaltet nur, wenn alle lokalen Safety-Regeln frei sind, der
+Behavior-Modus aktiv ist, die Freigabe bereit ist, die Confidence passt, der
+Zielzustand noch nicht erreicht ist und der Cooldown abgelaufen ist.
+
+## Transparenz
+
+Die Aktor-Detailansicht trennt:
+
+- Wissen: belegte Fakten aus Historie, Zuordnung und Automationen.
+- Annahmen: heuristische Schluesse wie Zeit-/Kontext-Aehnlichkeit.
+- Unsicherheiten: geringe Datenmenge, unklare Quellen, Review-Bedarf oder
+  negatives Feedback.
+- Beitragsfaktoren: Sensoren, Kontextsignale, aktive Gewichtung und Beitrag.
+- Safety-Blocker: Gruende, warum nicht geschaltet wird.
+
+## Job-Queue
+
+Das Dashboard zeigt die letzten Jobs mit Status, Dauer, Fehler und
+Zusammenfassung. Sichtbar sind:
+
+- Discovery
+- Reconciliation
+- Training
+- Evaluation
+- Automation-Refresh
+
+Die Queue ist persistent in `job_queue.json` und dient als Betriebsanzeige. Sie
+blockiert nicht den Startpfad und nicht den Schaltpfad.
+
+## Manuelles Gegensteuern
+
+Im Dashboard koennen pro Aktor gesetzt werden:
+
+- manuelle Sicherheitssperre
+- Freigabestufe
+- Mindest-Confidence
+- optionaler Cooldown
+- Sensor-Gewichtungen und Gruppen-Gewichtungen
+- Kontextauswahl
+- Feedback: Vorhersage korrekt/falsch
+- HA-Automationen pausieren/fortsetzen
+
+## Qualitaetspruefung
+
+Vor Release:
+
+```bash
+.venv/bin/pytest -q
+.venv/bin/ruff check .
+.venv/bin/mypy app backend tests
+git diff --check
+node --check /tmp/sillyhome-dashboard.js
+```

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "sillyhome-next"
-version = "1.0.0"
+version = "1.1.0"
 description = "Lokales Smart-Home-Intelligenzsystem für Home Assistant"
 requires-python = ">=3.11"
 dependencies = [

tests/api/test_actuators.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+from time import perf_counter
 from datetime import datetime, timedelta
 from pathlib import Path
 
@@ -29,6 +30,7 @@ class FakeHaReader(HaReader):
         self._entities = entities
         self._history = history
         self.read_entities_calls = 0
+        self.service_calls: list[tuple[str, str, dict[str, object]]] = []
 
     def read_entities(self) -> list[HaEntitySummary]:
         self.read_entities_calls += 1
@@ -85,6 +87,7 @@ class FakeHaReader(HaReader):
         service: str,
         service_data: dict[str, object],
     ) -> list[object]:
+        self.service_calls.append((domain, service, service_data))
         return []
 
     def find_automations_for_entity(
@@ -215,6 +218,88 @@ def test_manual_assignment_endpoint_updates_context(tmp_path: Path) -> None:
     ]
 
 
+def test_weight_override_endpoint_updates_sensor_relevance(tmp_path: Path) -> None:
+    with TestClient(app) as client:
+        _install_service(tmp_path)
+        client.post("/v1/actuators", json={"actuator_entity_id": "light.abstellkammer"})
+        client.post(
+            "/v1/actuators/light.abstellkammer/assignment",
+            json={
+                "numeric_entity_id": "sensor.abstellkammer_illuminance",
+                "context_entity_ids": ["binary_sensor.abstellkammer_motion"],
+            },
+        )
+
+        response = client.post(
+            "/v1/actuators/light.abstellkammer/weights",
+            json={
+                "sensor_weights": {
+                    "sensor.abstellkammer_illuminance": 0.75,
+                    "binary_sensor.abstellkammer_motion": 0.5,
+                },
+                "sensor_weight_groups": [
+                    {
+                        "group_id": "abstellkammer_context",
+                        "name": "Abstellkammer Kontext",
+                        "entity_ids": [
+                            "sensor.abstellkammer_illuminance",
+                            "binary_sensor.abstellkammer_motion",
+                        ],
+                        "weight": 0.8,
+                    }
+                ],
+                "note": "Gewichtung korrigiert",
+            },
+        )
+
+    assert response.status_code == 200
+    payload = response.json()
+    assert payload["manual_override"]["sensor_weights"]["sensor.abstellkammer_illuminance"] == 0.75
+    assert payload["manual_override"]["sensor_weight_groups"][0]["group_id"] == (
+        "abstellkammer_context"
+    )
+    numeric = {
+        candidate["entity_id"]: candidate
+        for candidate in payload["numeric_candidates"]
+    }
+    assert numeric["sensor.abstellkammer_illuminance"]["manual_weight"] == 0.75
+    assert numeric["sensor.abstellkammer_illuminance"]["effective_weight"] == 0.75
+
+
+def test_safety_profile_can_block_actuator_manually(tmp_path: Path) -> None:
+    with TestClient(app) as client:
+        _install_service(tmp_path)
+        client.post("/v1/actuators", json={"actuator_entity_id": "light.abstellkammer"})
+
+        response = client.post(
+            "/v1/actuators/light.abstellkammer/safety",
+            json={
+                "safety": {
+                    "stage": "shadow",
+                    "manual_block": True,
+                    "min_confidence": 0.9,
+                    "cooldown_seconds": 120,
+                    "rules": [
+                        {
+                            "rule_id": "manual_block",
+                            "label": "Manuelle Sperre respektieren",
+                            "enabled": True,
+                            "blocking": True,
+                            "reason": "Test",
+                        }
+                    ],
+                    "note": "Test",
+                }
+            },
+        )
+
+    assert response.status_code == 200
+    payload = response.json()
+    assert payload["behavior"]["safety"]["manual_block"] is True
+    assert payload["behavior"]["safety"]["min_confidence"] == 0.9
+    assert payload["behavior"]["safety"]["cooldown_seconds"] == 120
+
+
 def test_summary_is_lightweight_and_uses_cached_entity_metadata(tmp_path: Path) -> None:
     with TestClient(app) as client:
         _install_service(tmp_path)
@@ -249,6 +334,46 @@ def test_dashboard_overview_uses_cache_without_ha_roundtrip(tmp_path: Path) -> N
     assert payload["cache"]["entity_count"] == 4
     assert payload["actuators"][0]["friendly_name"] == "Abstellkammer Licht"
     assert payload["discovery_groups"]
+    assert payload["jobs"]["jobs"][-1]["kind"] == "discovery"
+
+
+def test_reconciliation_run_records_visible_job_queue(tmp_path: Path) -> None:
+    with TestClient(app) as client:
+        _install_service(tmp_path)
+        client.post("/v1/actuators", json={"actuator_entity_id": "light.abstellkammer"})
+
+        response = client.post("/v1/actuators/reconciliation/run")
+        jobs = client.get("/v1/actuators/job-queue/state")
+
+    assert response.status_code == 200
+    assert jobs.status_code == 200
+    payload = jobs.json()
+    assert [job["kind"] for job in payload["jobs"][-3:]] == [
+        "reconciliation",
+        "training",
+        "evaluation",
+    ]
+    assert payload["jobs"][-1]["status"] == "completed"
+
+
+def test_dashboard_start_path_stays_within_five_second_budget(tmp_path: Path) -> None:
+    with TestClient(app) as client:
+        _install_service(tmp_path)
+        client.get("/v1/actuators/discovery")
+        client.post("/v1/actuators", json={"actuator_entity_id": "light.abstellkammer"})
+
+        root_started_at = perf_counter()
+        root_response = client.get("/")
+        root_elapsed = perf_counter() - root_started_at
+
+        dashboard_started_at = perf_counter()
+        dashboard_response = client.get("/v1/actuators/dashboard")
+        dashboard_elapsed = perf_counter() - dashboard_started_at
+
+    assert root_response.status_code == 200
+    assert dashboard_response.status_code == 200
+    assert root_elapsed < 5.0
+    assert dashboard_elapsed < 5.0
 
 
 def test_discovery_reads_entities_once_and_reuses_them(tmp_path: Path) -> None:

tests/test_dashboard.py

@@ -9,11 +9,14 @@ def test_dashboard_is_served_at_root() -> None:
 
     assert response.status_code == 200
     assert "SillyHome Next" in response.text
+    assert "Arbeitsdashboard für gelernte Home-Assistant-Bedienung" in response.text
     assert "So gehst du vor" in response.text
-    assert "Gerät zum Lernen auswählen" in response.text
+    assert "Steuerung" in response.text
     assert "Entitätsname oder Gerät aus Home Assistant" in response.text
     assert "Oder aus Liste wählen" in response.text
     assert "Liste durchsuchen" in response.text
+    assert "Geräteliste bei Bedarf laden" in response.text
+    assert "Vorschläge können Home Assistant stark abfragen" in response.text
     assert "Wie gewohnt bedienen" in response.text
     assert "Ohne deine spätere Freigabe wird nichts geschaltet" in response.text
     assert "Du wählst keine Sensoren und erstellst keine Regeln" in response.text