diff --git a/meshai/config.py b/meshai/config.py
index 18eea12..d0c2c99 100644
--- a/meshai/config.py
+++ b/meshai/config.py
@@ -1,344 +1,345 @@
-"""Configuration management for MeshAI."""
-
-import logging
-import os
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Optional
-
-import yaml
-
-_config_logger = logging.getLogger(__name__)
-
-
-@dataclass
-class BotConfig:
-    """Bot identity and trigger settings."""
-
-    name: str = "ai"
-    owner: str = ""
-    respond_to_dms: bool = True
-    filter_bbs_protocols: bool = True
-
-
-@dataclass
-class ConnectionConfig:
-    """Meshtastic connection settings."""
-
-    type: str = "serial"  # serial or tcp
-    serial_port: str = "/dev/ttyUSB0"
-    tcp_host: str = "192.168.1.100"
-    tcp_port: int = 4403
-
-
-@dataclass
-class ResponseConfig:
-    """Response behavior settings."""
-
-    delay_min: float = 2.2
-    delay_max: float = 3.0
-    max_length: int = 150
-    max_messages: int = 2
-
-
-@dataclass
-class HistoryConfig:
-    """Conversation history settings."""
-
-    database: str = "conversations.db"
-    max_messages_per_user: int = 50
-    conversation_timeout: int = 86400  # 24 hours
-
-    # Cleanup settings
-    auto_cleanup: bool = True
-    cleanup_interval_hours: int = 24
-    max_age_days: int = 30  # Delete conversations older than this
-
-
-@dataclass
-class MemoryConfig:
-    """Rolling summary memory settings."""
-
-    enabled: bool = True  # Enable memory optimization
-    window_size: int = 4  # Recent message pairs to keep in full
-    summarize_threshold: int = 8  # Messages before re-summarizing
-
-
-@dataclass
-class ContextConfig:
-    """Passive mesh context settings."""
-
-    enabled: bool = True
-    observe_channels: list[int] = field(default_factory=list)  # Empty = all channels
-    ignore_nodes: list[str] = field(default_factory=list)  # Node IDs to ignore
-    max_age: int = 2_592_000  # 30 days in seconds
-    max_context_items: int = 20  # Max observations injected into LLM context
-
-
-@dataclass
-class CommandsConfig:
-    """Command settings."""
-
-    enabled: bool = True
-    prefix: str = "!"
-    disabled_commands: list[str] = field(default_factory=list)
-    custom_commands: dict = field(default_factory=dict)
-
-
-@dataclass
-class LLMConfig:
-    """LLM backend settings."""
-
-    backend: str = "openai"  # openai, anthropic, google
-    api_key: str = ""
-    base_url: str = "https://api.openai.com/v1"
-    model: str = "gpt-4o-mini"
-    timeout: int = 30
-
-    system_prompt: str = (
-        "YOUR COMMANDS (handled directly by you via DM):\n"
-        "!help — List available commands.\n"
-        "!ping — Connectivity test, responds with pong.\n"
-        "!status — Shows your version, uptime, user count, and message count.\n"
-        "!weather [location] — Weather lookup using Open-Meteo API.\n"
-        "!reset — Clears conversation history and memory.\n"
-        "!clear — Same as !reset.\n\n"
-        "YOUR ARCHITECTURE: Modular Python — pluggable LLM backends (OpenAI, Anthropic, "
-        "Google, local), per-user SQLite conversation history, rolling summary memory, "
-        "passive mesh context buffer (observes channel traffic), smart chunking for LoRa "
-        "message limits, prompt injection defense, advBBS filtering.\n\n"
-        "RESPONSE RULES:\n"
-        "- Keep responses very brief — 1-2 short sentences, under 300 characters. Only give longer answers if the user explicitly asks for detail or explanation.\n"
-        "- Be concise but friendly. No markdown formatting.\n"
-        "- If asked about mesh activity and no recent traffic is shown, say you haven't "
-        "observed any yet.\n"
-        "- When asked about yourself or commands, answer conversationally. Don't dump lists.\n"
-        "- You are part of the freq51 mesh in the Twin Falls, Idaho area."
-    )
-    use_system_prompt: bool = True  # Toggle to disable sending system prompt
-    web_search: bool = False  # Enable web search (Open WebUI feature)
-    google_grounding: bool = False  # Enable Google Search grounding (Gemini only)
-
-
-@dataclass
-class OpenMeteoConfig:
-    """Open-Meteo weather provider settings."""
-
-    url: str = "https://api.open-meteo.com/v1"
-
-
-@dataclass
-class WttrConfig:
-    """wttr.in weather provider settings."""
-
-    url: str = "https://wttr.in"
-
-
-@dataclass
-class WeatherConfig:
-    """Weather command settings."""
-
-    primary: str = "openmeteo"  # openmeteo, wttr, llm
-    fallback: str = "llm"  # openmeteo, wttr, llm, none
-    default_location: str = ""
-    openmeteo: OpenMeteoConfig = field(default_factory=OpenMeteoConfig)
-    wttr: WttrConfig = field(default_factory=WttrConfig)
-
-
-@dataclass
-class MeshMonitorConfig:
-    """MeshMonitor trigger sync settings."""
-
-    enabled: bool = False
-    url: str = ""  # e.g., http://100.64.0.11:3333
-    inject_into_prompt: bool = True  # Tell LLM about MeshMonitor commands
-    refresh_interval: int = 300  # Seconds between refreshes
-
-
-@dataclass
-class KnowledgeConfig:
-    """FTS5 knowledge base settings."""
-
-    enabled: bool = False
-    db_path: str = ""
-    top_k: int = 5
-
-
-@dataclass
-class MeshSourceConfig:
-    """Configuration for a mesh data source."""
-
-    name: str = ""
-    type: str = ""  # "meshview" or "meshmonitor"
-    url: str = ""
-    api_token: str = ""  # MeshMonitor only, supports ${ENV_VAR}
-    refresh_interval: int = 300
-    enabled: bool = True
-
-
-@dataclass
-class RegionAnchor:
-    """A fixed region anchor point."""
-
-    name: str = ""
-    lat: float = 0.0
-    lon: float = 0.0
-
-
-@dataclass
-class MeshIntelligenceConfig:
-    """Mesh intelligence and health scoring settings."""
-
-    enabled: bool = False
-    regions: list[RegionAnchor] = field(default_factory=list)  # Fixed region anchors
-    locality_radius_miles: float = 8.0  # Radius for locality clustering within regions
-    offline_threshold_hours: int = 24  # Hours before node considered offline
-    packet_threshold: int = 500  # Non-text packets per 24h to flag
-    battery_warning_percent: int = 20  # Battery level for warnings
-
-
-@dataclass
-class Config:
-    """Main configuration container."""
-
-    bot: BotConfig = field(default_factory=BotConfig)
-    connection: ConnectionConfig = field(default_factory=ConnectionConfig)
-    response: ResponseConfig = field(default_factory=ResponseConfig)
-    history: HistoryConfig = field(default_factory=HistoryConfig)
-    memory: MemoryConfig = field(default_factory=MemoryConfig)
-    context: ContextConfig = field(default_factory=ContextConfig)
-    commands: CommandsConfig = field(default_factory=CommandsConfig)
-    llm: LLMConfig = field(default_factory=LLMConfig)
-    weather: WeatherConfig = field(default_factory=WeatherConfig)
-    meshmonitor: MeshMonitorConfig = field(default_factory=MeshMonitorConfig)
-    knowledge: KnowledgeConfig = field(default_factory=KnowledgeConfig)
-    mesh_sources: list[MeshSourceConfig] = field(default_factory=list)
-    mesh_intelligence: MeshIntelligenceConfig = field(default_factory=MeshIntelligenceConfig)
-
-    _config_path: Optional[Path] = field(default=None, repr=False)
-
-    def resolve_api_key(self) -> str:
-        """Resolve API key from config or environment."""
-        if self.llm.api_key:
-            # Check if it's an env var reference like ${LLM_API_KEY}
-            if self.llm.api_key.startswith("${") and self.llm.api_key.endswith("}"):
-                env_var = self.llm.api_key[2:-1]
-                return os.environ.get(env_var, "")
-            return self.llm.api_key
-        # Fall back to common env vars
-        for env_var in ["LLM_API_KEY", "OPENAI_API_KEY", "ANTHROPIC_API_KEY"]:
-            if value := os.environ.get(env_var):
-                return value
-        return ""
-
-
-def _dict_to_dataclass(cls, data: dict):
-    """Recursively convert dict to dataclass, handling nested structures."""
-    if data is None:
-        return cls()
-
-    field_types = {f.name: f.type for f in cls.__dataclass_fields__.values()}
-    kwargs = {}
-
-    for key, value in data.items():
-        if key.startswith("_"):
-            continue
-        if key not in field_types:
-            continue
-
-        field_type = field_types[key]
-
-        # Handle nested dataclasses
-        if hasattr(field_type, "__dataclass_fields__") and isinstance(value, dict):
-            kwargs[key] = _dict_to_dataclass(field_type, value)
-        # Handle list of MeshSourceConfig
-        elif key == "mesh_sources" and isinstance(value, list):
-            kwargs[key] = [
-                _dict_to_dataclass(MeshSourceConfig, item)
-                if isinstance(item, dict) else item
-                for item in value
-            ]
-        # Handle list of RegionAnchor
-        elif key == "regions" and isinstance(value, list):
-            kwargs[key] = [
-                _dict_to_dataclass(RegionAnchor, item)
-                if isinstance(item, dict) else item
-                for item in value
-            ]
-        else:
-            kwargs[key] = value
-
-    return cls(**kwargs)
-
-
-def _dataclass_to_dict(obj) -> dict:
-    """Recursively convert dataclass to dict for YAML serialization."""
-    if not hasattr(obj, "__dataclass_fields__"):
-        return obj
-
-    result = {}
-    for field_name in obj.__dataclass_fields__:
-        if field_name.startswith("_"):
-            continue
-        value = getattr(obj, field_name)
-        if hasattr(value, "__dataclass_fields__"):
-            result[field_name] = _dataclass_to_dict(value)
-        elif isinstance(value, list):
-            # Handle list of dataclasses (like mesh_sources)
-            result[field_name] = [
-                _dataclass_to_dict(item) if hasattr(item, "__dataclass_fields__") else item
-                for item in value
-            ]
-        else:
-            result[field_name] = value
-    return result
-
-
-def load_config(config_path: Optional[Path] = None) -> Config:
-    """Load configuration from YAML file.
-
-    Args:
-        config_path: Path to config file. Defaults to ./config.yaml
-
-    Returns:
-        Config object with loaded settings
-    """
-    if config_path is None:
-        config_path = Path("config.yaml")
-
-    config_path = Path(config_path)
-
-    if not config_path.exists():
-        # Return default config if file doesn't exist
-        config = Config()
-        config._config_path = config_path
-        return config
-
-    with open(config_path, "r") as f:
-        data = yaml.safe_load(f) or {}
-
-    config = _dict_to_dataclass(Config, data)
-    config._config_path = config_path
-    return config
-
-
-def save_config(config: Config, config_path: Optional[Path] = None) -> None:
-    """Save configuration to YAML file.
-
-    Args:
-        config: Config object to save
-        config_path: Path to save to. Uses config._config_path if not specified
-    """
-    if config_path is None:
-        config_path = config._config_path or Path("config.yaml")
-
-    config_path = Path(config_path)
-
-    data = _dataclass_to_dict(config)
-
-    # Add header comment
-    header = "# MeshAI Configuration\n# Generated by meshai --config\n\n"
-
-    with open(config_path, "w") as f:
-        f.write(header)
-        yaml.dump(data, f, default_flow_style=False, sort_keys=False, allow_unicode=True)
+"""Configuration management for MeshAI."""
+
+import logging
+import os
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Optional
+
+import yaml
+
+_config_logger = logging.getLogger(__name__)
+
+
+@dataclass
+class BotConfig:
+    """Bot identity and trigger settings."""
+
+    name: str = "ai"
+    owner: str = ""
+    respond_to_dms: bool = True
+    filter_bbs_protocols: bool = True
+
+
+@dataclass
+class ConnectionConfig:
+    """Meshtastic connection settings."""
+
+    type: str = "serial"  # serial or tcp
+    serial_port: str = "/dev/ttyUSB0"
+    tcp_host: str = "192.168.1.100"
+    tcp_port: int = 4403
+
+
+@dataclass
+class ResponseConfig:
+    """Response behavior settings."""
+
+    delay_min: float = 2.2
+    delay_max: float = 3.0
+    max_length: int = 150
+    max_messages: int = 2
+
+
+@dataclass
+class HistoryConfig:
+    """Conversation history settings."""
+
+    database: str = "conversations.db"
+    max_messages_per_user: int = 50
+    conversation_timeout: int = 86400  # 24 hours
+
+    # Cleanup settings
+    auto_cleanup: bool = True
+    cleanup_interval_hours: int = 24
+    max_age_days: int = 30  # Delete conversations older than this
+
+
+@dataclass
+class MemoryConfig:
+    """Rolling summary memory settings."""
+
+    enabled: bool = True  # Enable memory optimization
+    window_size: int = 4  # Recent message pairs to keep in full
+    summarize_threshold: int = 8  # Messages before re-summarizing
+
+
+@dataclass
+class ContextConfig:
+    """Passive mesh context settings."""
+
+    enabled: bool = True
+    observe_channels: list[int] = field(default_factory=list)  # Empty = all channels
+    ignore_nodes: list[str] = field(default_factory=list)  # Node IDs to ignore
+    max_age: int = 2_592_000  # 30 days in seconds
+    max_context_items: int = 20  # Max observations injected into LLM context
+
+
+@dataclass
+class CommandsConfig:
+    """Command settings."""
+
+    enabled: bool = True
+    prefix: str = "!"
+    disabled_commands: list[str] = field(default_factory=list)
+    custom_commands: dict = field(default_factory=dict)
+
+
+@dataclass
+class LLMConfig:
+    """LLM backend settings."""
+
+    backend: str = "openai"  # openai, anthropic, google
+    api_key: str = ""
+    base_url: str = "https://api.openai.com/v1"
+    model: str = "gpt-4o-mini"
+    timeout: int = 30
+
+    system_prompt: str = (
+        "YOUR COMMANDS (handled directly by you via DM):\n"
+        "!help — List available commands.\n"
+        "!ping — Connectivity test, responds with pong.\n"
+        "!status — Shows your version, uptime, user count, and message count.\n"
+        "!weather [location] — Weather lookup using Open-Meteo API.\n"
+        "!reset — Clears conversation history and memory.\n"
+        "!clear — Same as !reset.\n\n"
+        "YOUR ARCHITECTURE: Modular Python — pluggable LLM backends (OpenAI, Anthropic, "
+        "Google, local), per-user SQLite conversation history, rolling summary memory, "
+        "passive mesh context buffer (observes channel traffic), smart chunking for LoRa "
+        "message limits, prompt injection defense, advBBS filtering.\n\n"
+        "RESPONSE RULES:\n"
+        "- For casual conversation, keep responses brief (1-2 sentences).\n"
+        "- For mesh health questions, give detailed data-driven responses.\n"
+        "- Be concise but friendly. No markdown formatting.\n"
+        "- If asked about mesh activity and no recent traffic is shown, say you haven't "
+        "observed any yet.\n"
+        "- When asked about yourself or commands, answer conversationally. Don't dump lists.\n"
+        "- You are part of the freq51 mesh in the Twin Falls, Idaho area."
+    )
+    use_system_prompt: bool = True  # Toggle to disable sending system prompt
+    web_search: bool = False  # Enable web search (Open WebUI feature)
+    google_grounding: bool = False  # Enable Google Search grounding (Gemini only)
+
+
+@dataclass
+class OpenMeteoConfig:
+    """Open-Meteo weather provider settings."""
+
+    url: str = "https://api.open-meteo.com/v1"
+
+
+@dataclass
+class WttrConfig:
+    """wttr.in weather provider settings."""
+
+    url: str = "https://wttr.in"
+
+
+@dataclass
+class WeatherConfig:
+    """Weather command settings."""
+
+    primary: str = "openmeteo"  # openmeteo, wttr, llm
+    fallback: str = "llm"  # openmeteo, wttr, llm, none
+    default_location: str = ""
+    openmeteo: OpenMeteoConfig = field(default_factory=OpenMeteoConfig)
+    wttr: WttrConfig = field(default_factory=WttrConfig)
+
+
+@dataclass
+class MeshMonitorConfig:
+    """MeshMonitor trigger sync settings."""
+
+    enabled: bool = False
+    url: str = ""  # e.g., http://100.64.0.11:3333
+    inject_into_prompt: bool = True  # Tell LLM about MeshMonitor commands
+    refresh_interval: int = 300  # Seconds between refreshes
+
+
+@dataclass
+class KnowledgeConfig:
+    """FTS5 knowledge base settings."""
+
+    enabled: bool = False
+    db_path: str = ""
+    top_k: int = 5
+
+
+@dataclass
+class MeshSourceConfig:
+    """Configuration for a mesh data source."""
+
+    name: str = ""
+    type: str = ""  # "meshview" or "meshmonitor"
+    url: str = ""
+    api_token: str = ""  # MeshMonitor only, supports ${ENV_VAR}
+    refresh_interval: int = 300
+    enabled: bool = True
+
+
+@dataclass
+class RegionAnchor:
+    """A fixed region anchor point."""
+
+    name: str = ""
+    lat: float = 0.0
+    lon: float = 0.0
+
+
+@dataclass
+class MeshIntelligenceConfig:
+    """Mesh intelligence and health scoring settings."""
+
+    enabled: bool = False
+    regions: list[RegionAnchor] = field(default_factory=list)  # Fixed region anchors
+    locality_radius_miles: float = 8.0  # Radius for locality clustering within regions
+    offline_threshold_hours: int = 24  # Hours before node considered offline
+    packet_threshold: int = 500  # Non-text packets per 24h to flag
+    battery_warning_percent: int = 20  # Battery level for warnings
+
+
+@dataclass
+class Config:
+    """Main configuration container."""
+
+    bot: BotConfig = field(default_factory=BotConfig)
+    connection: ConnectionConfig = field(default_factory=ConnectionConfig)
+    response: ResponseConfig = field(default_factory=ResponseConfig)
+    history: HistoryConfig = field(default_factory=HistoryConfig)
+    memory: MemoryConfig = field(default_factory=MemoryConfig)
+    context: ContextConfig = field(default_factory=ContextConfig)
+    commands: CommandsConfig = field(default_factory=CommandsConfig)
+    llm: LLMConfig = field(default_factory=LLMConfig)
+    weather: WeatherConfig = field(default_factory=WeatherConfig)
+    meshmonitor: MeshMonitorConfig = field(default_factory=MeshMonitorConfig)
+    knowledge: KnowledgeConfig = field(default_factory=KnowledgeConfig)
+    mesh_sources: list[MeshSourceConfig] = field(default_factory=list)
+    mesh_intelligence: MeshIntelligenceConfig = field(default_factory=MeshIntelligenceConfig)
+
+    _config_path: Optional[Path] = field(default=None, repr=False)
+
+    def resolve_api_key(self) -> str:
+        """Resolve API key from config or environment."""
+        if self.llm.api_key:
+            # Check if it's an env var reference like ${LLM_API_KEY}
+            if self.llm.api_key.startswith("${") and self.llm.api_key.endswith("}"):
+                env_var = self.llm.api_key[2:-1]
+                return os.environ.get(env_var, "")
+            return self.llm.api_key
+        # Fall back to common env vars
+        for env_var in ["LLM_API_KEY", "OPENAI_API_KEY", "ANTHROPIC_API_KEY"]:
+            if value := os.environ.get(env_var):
+                return value
+        return ""
+
+
+def _dict_to_dataclass(cls, data: dict):
+    """Recursively convert dict to dataclass, handling nested structures."""
+    if data is None:
+        return cls()
+
+    field_types = {f.name: f.type for f in cls.__dataclass_fields__.values()}
+    kwargs = {}
+
+    for key, value in data.items():
+        if key.startswith("_"):
+            continue
+        if key not in field_types:
+            continue
+
+        field_type = field_types[key]
+
+        # Handle nested dataclasses
+        if hasattr(field_type, "__dataclass_fields__") and isinstance(value, dict):
+            kwargs[key] = _dict_to_dataclass(field_type, value)
+        # Handle list of MeshSourceConfig
+        elif key == "mesh_sources" and isinstance(value, list):
+            kwargs[key] = [
+                _dict_to_dataclass(MeshSourceConfig, item)
+                if isinstance(item, dict) else item
+                for item in value
+            ]
+        # Handle list of RegionAnchor
+        elif key == "regions" and isinstance(value, list):
+            kwargs[key] = [
+                _dict_to_dataclass(RegionAnchor, item)
+                if isinstance(item, dict) else item
+                for item in value
+            ]
+        else:
+            kwargs[key] = value
+
+    return cls(**kwargs)
+
+
+def _dataclass_to_dict(obj) -> dict:
+    """Recursively convert dataclass to dict for YAML serialization."""
+    if not hasattr(obj, "__dataclass_fields__"):
+        return obj
+
+    result = {}
+    for field_name in obj.__dataclass_fields__:
+        if field_name.startswith("_"):
+            continue
+        value = getattr(obj, field_name)
+        if hasattr(value, "__dataclass_fields__"):
+            result[field_name] = _dataclass_to_dict(value)
+        elif isinstance(value, list):
+            # Handle list of dataclasses (like mesh_sources)
+            result[field_name] = [
+                _dataclass_to_dict(item) if hasattr(item, "__dataclass_fields__") else item
+                for item in value
+            ]
+        else:
+            result[field_name] = value
+    return result
+
+
+def load_config(config_path: Optional[Path] = None) -> Config:
+    """Load configuration from YAML file.
+
+    Args:
+        config_path: Path to config file. Defaults to ./config.yaml
+
+    Returns:
+        Config object with loaded settings
+    """
+    if config_path is None:
+        config_path = Path("config.yaml")
+
+    config_path = Path(config_path)
+
+    if not config_path.exists():
+        # Return default config if file doesn't exist
+        config = Config()
+        config._config_path = config_path
+        return config
+
+    with open(config_path, "r") as f:
+        data = yaml.safe_load(f) or {}
+
+    config = _dict_to_dataclass(Config, data)
+    config._config_path = config_path
+    return config
+
+
+def save_config(config: Config, config_path: Optional[Path] = None) -> None:
+    """Save configuration to YAML file.
+
+    Args:
+        config: Config object to save
+        config_path: Path to save to. Uses config._config_path if not specified
+    """
+    if config_path is None:
+        config_path = config._config_path or Path("config.yaml")
+
+    config_path = Path(config_path)
+
+    data = _dataclass_to_dict(config)
+
+    # Add header comment
+    header = "# MeshAI Configuration\n# Generated by meshai --config\n\n"
+
+    with open(config_path, "w") as f:
+        f.write(header)
+        yaml.dump(data, f, default_flow_style=False, sort_keys=False, allow_unicode=True)
diff --git a/meshai/router.py b/meshai/router.py
index baf0b22..19a9ec2 100644
--- a/meshai/router.py
+++ b/meshai/router.py
@@ -125,14 +125,19 @@ _CITY_TO_REGION = {
 
 # Mesh awareness instruction for LLM
 _MESH_AWARENESS_PROMPT = """
-When the user asks about mesh health, network status, or optimization:
-- Use the LIVE MESH HEALTH DATA injected above to answer with real numbers
-- Be specific: name nodes, cite utilization percentages, reference actual scores
-- Give actionable recommendations based on the data
-- If asked about a region or node you have detail for, use that detail
-- If asked about something the data doesn't cover, say so - don't fabricate
-- Keep responses concise - these go over LoRa with limited message size
-- Users can run !health for a quick mesh summary or !region [name] for regional info
+MESH DATA RESPONSE RULES (these OVERRIDE the brevity rules above for mesh questions):
+- When answering about mesh health, nodes, coverage, or network status: give DETAILED responses
+- Include actual numbers: scores, percentages, node names, packet counts, battery levels
+- Use the data injected above — don't summarize it to one sentence
+- Structure your response with the key data points the user asked about
+- For node questions: include hardware, region, battery, channel utilization, coverage, neighbors, packets
+- For region questions: include score, infrastructure status, coverage breakdown, flagged nodes, environment
+- For mesh questions: include overall score by pillar, regional breakdown, top issues, coverage gaps
+- For coverage questions: break down by region showing node counts, avg gateways, single-gateway nodes
+- For "where do we need infrastructure": name specific regions with poor coverage, how many nodes are affected
+- You CAN use 3-5 messages if needed — LoRa chunking handles splitting
+- Be specific and data-driven, not vague summaries
+- Still no markdown formatting — plain text only
 """
 
 
@@ -673,4 +678,4 @@ class MessageRouter:
             connector=self.connector,
             history=self.history,
         )
-
+