merge origin/main into pr-1327

Made-with: Cursor

nanobot/agent/context.py

@@ -10,7 +10,7 @@ from typing import Any
 
 from nanobot.agent.memory import MemoryStore
 from nanobot.agent.skills import SkillsLoader
-from nanobot.utils.helpers import detect_image_mime
+from nanobot.utils.helpers import build_assistant_message, detect_image_mime
 
 
 class ContextBuilder:
@@ -182,12 +182,10 @@ Reply directly with text for conversations. Only use the 'message' tool to send
         thinking_blocks: list[dict] | None = None,
     ) -> list[dict[str, Any]]:
         """Add an assistant message to the message list."""
-        msg: dict[str, Any] = {"role": "assistant", "content": content}
-        if tool_calls:
-            msg["tool_calls"] = tool_calls
-        if reasoning_content is not None:
-            msg["reasoning_content"] = reasoning_content
-        if thinking_blocks:
-            msg["thinking_blocks"] = thinking_blocks
-        messages.append(msg)
+        messages.append(build_assistant_message(
+            content,
+            tool_calls=tool_calls,
+            reasoning_content=reasoning_content,
+            thinking_blocks=thinking_blocks,
+        ))
         return messages

nanobot/agent/loop.py

@@ -5,7 +5,6 @@ from __future__ import annotations
 import asyncio
 import json
 import re
-import weakref
 from contextlib import AsyncExitStack
 from pathlib import Path
 from typing import TYPE_CHECKING, Any, Awaitable, Callable
@@ -13,7 +12,7 @@ from typing import TYPE_CHECKING, Any, Awaitable, Callable
 from loguru import logger
 
 from nanobot.agent.context import ContextBuilder
-from nanobot.agent.memory import MemoryStore
+from nanobot.agent.memory import MemoryConsolidator
 from nanobot.agent.subagent import SubagentManager
 from nanobot.agent.tools.cron import CronTool
 from nanobot.agent.tools.filesystem import EditFileTool, ListDirTool, ReadFileTool, WriteFileTool
@@ -55,8 +54,8 @@ class AgentLoop:
         max_iterations: int = 40,
         temperature: float = 0.1,
         max_tokens: int = 4096,
-        memory_window: int = 100,
         reasoning_effort: str | None = None,
+        context_window_tokens: int = 65_536,
         brave_api_key: str | None = None,
         web_proxy: str | None = None,
         exec_config: ExecToolConfig | None = None,
@@ -75,8 +74,8 @@ class AgentLoop:
         self.max_iterations = max_iterations
         self.temperature = temperature
         self.max_tokens = max_tokens
-        self.memory_window = memory_window
         self.reasoning_effort = reasoning_effort
+        self.context_window_tokens = context_window_tokens
         self.brave_api_key = brave_api_key
         self.web_proxy = web_proxy
         self.exec_config = exec_config or ExecToolConfig()
@@ -105,11 +104,17 @@ class AgentLoop:
         self._mcp_stack: AsyncExitStack | None = None
         self._mcp_connected = False
         self._mcp_connecting = False
-        self._consolidating: set[str] = set()  # Session keys with consolidation in progress
-        self._consolidation_tasks: set[asyncio.Task] = set()  # Strong refs to in-flight tasks
-        self._consolidation_locks: weakref.WeakValueDictionary[str, asyncio.Lock] = weakref.WeakValueDictionary()
         self._active_tasks: dict[str, list[asyncio.Task]] = {}  # session_key -> tasks
         self._processing_lock = asyncio.Lock()
+        self.memory_consolidator = MemoryConsolidator(
+            workspace=workspace,
+            provider=provider,
+            model=self.model,
+            sessions=self.sessions,
+            context_window_tokens=context_window_tokens,
+            build_messages=self.context.build_messages,
+            get_tool_definitions=self.tools.get_definitions,
+        )
         self._register_default_tools()
 
     def _register_default_tools(self) -> None:
@@ -182,7 +187,7 @@ class AgentLoop:
         initial_messages: list[dict],
         on_progress: Callable[..., Awaitable[None]] | None = None,
     ) -> tuple[str | None, list[str], list[dict]]:
-        """Run the agent iteration loop. Returns (final_content, tools_used, messages)."""
+        """Run the agent iteration loop."""
         messages = initial_messages
         iteration = 0
         final_content = None
@@ -191,9 +196,11 @@ class AgentLoop:
         while iteration < self.max_iterations:
             iteration += 1
 
-            response = await self.provider.chat(
+            tool_defs = self.tools.get_definitions()
+
+            response = await self.provider.chat_with_retry(
                 messages=messages,
-                tools=self.tools.get_definitions(),
+                tools=tool_defs,
                 model=self.model,
                 temperature=self.temperature,
                 max_tokens=self.max_tokens,
@@ -341,8 +348,9 @@ class AgentLoop:
             logger.info("Processing system message from {}", msg.sender_id)
             key = f"{channel}:{chat_id}"
             session = self.sessions.get_or_create(key)
+            await self.memory_consolidator.maybe_consolidate_by_tokens(session)
             self._set_tool_context(channel, chat_id, msg.metadata.get("message_id"))
-            history = session.get_history(max_messages=self.memory_window)
+            history = session.get_history(max_messages=0)
             messages = self.context.build_messages(
                 history=history,
                 current_message=msg.content, channel=channel, chat_id=chat_id,
@@ -350,6 +358,7 @@ class AgentLoop:
             final_content, _, all_msgs = await self._run_agent_loop(messages)
             self._save_turn(session, all_msgs, 1 + len(history))
             self.sessions.save(session)
+            await self.memory_consolidator.maybe_consolidate_by_tokens(session)
             return OutboundMessage(channel=channel, chat_id=chat_id,
                                   content=final_content or "Background task completed.")
 
@@ -362,27 +371,20 @@ class AgentLoop:
         # Slash commands
         cmd = msg.content.strip().lower()
         if cmd == "/new":
-            lock = self._consolidation_locks.setdefault(session.key, asyncio.Lock())
-            self._consolidating.add(session.key)
             try:
-                async with lock:
-                    snapshot = session.messages[session.last_consolidated:]
-                    if snapshot:
-                        temp = Session(key=session.key)
-                        temp.messages = list(snapshot)
-                        if not await self._consolidate_memory(temp, archive_all=True):
-                            return OutboundMessage(
-                                channel=msg.channel, chat_id=msg.chat_id,
-                                content="Memory archival failed, session not cleared. Please try again.",
-                            )
+                if not await self.memory_consolidator.archive_unconsolidated(session):
+                    return OutboundMessage(
+                        channel=msg.channel,
+                        chat_id=msg.chat_id,
+                        content="Memory archival failed, session not cleared. Please try again.",
+                    )
             except Exception:
                 logger.exception("/new archival failed for {}", session.key)
                 return OutboundMessage(
-                    channel=msg.channel, chat_id=msg.chat_id,
+                    channel=msg.channel,
+                    chat_id=msg.chat_id,
                     content="Memory archival failed, session not cleared. Please try again.",
                 )
-            finally:
-                self._consolidating.discard(session.key)
 
             session.clear()
             self.sessions.save(session)
@@ -393,30 +395,14 @@ class AgentLoop:
             return OutboundMessage(channel=msg.channel, chat_id=msg.chat_id,
                                   content="🐈 nanobot commands:\n/new — Start a new conversation\n/stop — Stop the current task\n/help — Show available commands")
 
-        unconsolidated = len(session.messages) - session.last_consolidated
-        if (unconsolidated >= self.memory_window and session.key not in self._consolidating):
-            self._consolidating.add(session.key)
-            lock = self._consolidation_locks.setdefault(session.key, asyncio.Lock())
-
-            async def _consolidate_and_unlock():
-                try:
-                    async with lock:
-                        await self._consolidate_memory(session)
-                finally:
-                    self._consolidating.discard(session.key)
-                    _task = asyncio.current_task()
-                    if _task is not None:
-                        self._consolidation_tasks.discard(_task)
-
-            _task = asyncio.create_task(_consolidate_and_unlock())
-            self._consolidation_tasks.add(_task)
+        await self.memory_consolidator.maybe_consolidate_by_tokens(session)
 
         self._set_tool_context(msg.channel, msg.chat_id, msg.metadata.get("message_id"))
         if message_tool := self.tools.get("message"):
             if isinstance(message_tool, MessageTool):
                 message_tool.start_turn()
 
-        history = session.get_history(max_messages=self.memory_window)
+        history = session.get_history(max_messages=0)
         initial_messages = self.context.build_messages(
             history=history,
             current_message=msg.content,
@@ -441,6 +427,7 @@ class AgentLoop:
 
         self._save_turn(session, all_msgs, 1 + len(history))
         self.sessions.save(session)
+        await self.memory_consolidator.maybe_consolidate_by_tokens(session)
 
         if (mt := self.tools.get("message")) and isinstance(mt, MessageTool) and mt._sent_in_turn:
             return None
@@ -487,13 +474,6 @@ class AgentLoop:
             session.messages.append(entry)
         session.updated_at = datetime.now()
 
-    async def _consolidate_memory(self, session, archive_all: bool = False) -> bool:
-        """Delegate to MemoryStore.consolidate(). Returns True on success."""
-        return await MemoryStore(self.workspace).consolidate(
-            session, self.provider, self.model,
-            archive_all=archive_all, memory_window=self.memory_window,
-        )
-
     async def process_direct(
         self,
         content: str,

nanobot/agent/memory.py

@@ -2,17 +2,19 @@
 
 from __future__ import annotations
 
+import asyncio
 import json
+import weakref
 from pathlib import Path
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Any, Callable
 
 from loguru import logger
 
-from nanobot.utils.helpers import ensure_dir
+from nanobot.utils.helpers import ensure_dir, estimate_message_tokens, estimate_prompt_tokens_chain
 
 if TYPE_CHECKING:
     from nanobot.providers.base import LLMProvider
-    from nanobot.session.manager import Session
+    from nanobot.session.manager import Session, SessionManager
 
 
 _SAVE_MEMORY_TOOL = [
@@ -26,7 +28,7 @@ _SAVE_MEMORY_TOOL = [
                 "properties": {
                     "history_entry": {
                         "type": "string",
-                        "description": "A paragraph (2-5 sentences) summarizing key events/decisions/topics. "
+                        "description": "A paragraph summarizing key events/decisions/topics. "
                         "Start with [YYYY-MM-DD HH:MM]. Include detail useful for grep search.",
                     },
                     "memory_update": {
@@ -42,6 +44,20 @@ _SAVE_MEMORY_TOOL = [
 ]
 
 
+def _ensure_text(value: Any) -> str:
+    """Normalize tool-call payload values to text for file storage."""
+    return value if isinstance(value, str) else json.dumps(value, ensure_ascii=False)
+
+
+def _normalize_save_memory_args(args: Any) -> dict[str, Any] | None:
+    """Normalize provider tool-call arguments to the expected dict shape."""
+    if isinstance(args, str):
+        args = json.loads(args)
+    if isinstance(args, list):
+        return args[0] if args and isinstance(args[0], dict) else None
+    return args if isinstance(args, dict) else None
+
+
 class MemoryStore:
     """Two-layer memory: MEMORY.md (long-term facts) + HISTORY.md (grep-searchable log)."""
 
@@ -66,40 +82,27 @@ class MemoryStore:
         long_term = self.read_long_term()
         return f"## Long-term Memory\n{long_term}" if long_term else ""
 
+    @staticmethod
+    def _format_messages(messages: list[dict]) -> str:
+        lines = []
+        for message in messages:
+            if not message.get("content"):
+                continue
+            tools = f" [tools: {', '.join(message['tools_used'])}]" if message.get("tools_used") else ""
+            lines.append(
+                f"[{message.get('timestamp', '?')[:16]}] {message['role'].upper()}{tools}: {message['content']}"
+            )
+        return "\n".join(lines)
+
     async def consolidate(
         self,
-        session: Session,
+        messages: list[dict],
         provider: LLMProvider,
         model: str,
-        *,
-        archive_all: bool = False,
-        memory_window: int = 50,
     ) -> bool:
-        """Consolidate old messages into MEMORY.md + HISTORY.md via LLM tool call.
-
-        Returns True on success (including no-op), False on failure.
-        """
-        if archive_all:
-            old_messages = session.messages
-            keep_count = 0
-            logger.info("Memory consolidation (archive_all): {} messages", len(session.messages))
-        else:
-            keep_count = memory_window // 2
-            if len(session.messages) <= keep_count:
-                return True
-            if len(session.messages) - session.last_consolidated <= 0:
-                return True
-            old_messages = session.messages[session.last_consolidated:-keep_count]
-            if not old_messages:
-                return True
-            logger.info("Memory consolidation: {} to consolidate, {} keep", len(old_messages), keep_count)
-
-        lines = []
-        for m in old_messages:
-            if not m.get("content"):
-                continue
-            tools = f" [tools: {', '.join(m['tools_used'])}]" if m.get("tools_used") else ""
-            lines.append(f"[{m.get('timestamp', '?')[:16]}] {m['role'].upper()}{tools}: {m['content']}")
+        """Consolidate the provided message chunk into MEMORY.md + HISTORY.md."""
+        if not messages:
+            return True
 
         current_memory = self.read_long_term()
         prompt = f"""Process this conversation and call the save_memory tool with your consolidation.
@@ -108,10 +111,10 @@ class MemoryStore:
 {current_memory or "(empty)"}
 
 ## Conversation to Process
-{chr(10).join(lines)}"""
+{self._format_messages(messages)}"""
 
         try:
-            response = await provider.chat(
+            response = await provider.chat_with_retry(
                 messages=[
                     {"role": "system", "content": "You are a memory consolidation agent. Call the save_memory tool with your consolidation of the conversation."},
                     {"role": "user", "content": prompt},
@@ -124,34 +127,158 @@ class MemoryStore:
                 logger.warning("Memory consolidation: LLM did not call save_memory, skipping")
                 return False
 
-            args = response.tool_calls[0].arguments
-            # Some providers return arguments as a JSON string instead of dict
-            if isinstance(args, str):
-                args = json.loads(args)
-            # Some providers return arguments as a list (handle edge case)
-            if isinstance(args, list):
-                if args and isinstance(args[0], dict):
-                    args = args[0]
-                else:
-                    logger.warning("Memory consolidation: unexpected arguments as empty or non-dict list")
-                    return False
-            if not isinstance(args, dict):
-                logger.warning("Memory consolidation: unexpected arguments type {}", type(args).__name__)
+            args = _normalize_save_memory_args(response.tool_calls[0].arguments)
+            if args is None:
+                logger.warning("Memory consolidation: unexpected save_memory arguments")
                 return False
 
             if entry := args.get("history_entry"):
-                if not isinstance(entry, str):
-                    entry = json.dumps(entry, ensure_ascii=False)
-                self.append_history(entry)
+                self.append_history(_ensure_text(entry))
             if update := args.get("memory_update"):
-                if not isinstance(update, str):
-                    update = json.dumps(update, ensure_ascii=False)
+                update = _ensure_text(update)
                 if update != current_memory:
                     self.write_long_term(update)
 
-            session.last_consolidated = 0 if archive_all else len(session.messages) - keep_count
-            logger.info("Memory consolidation done: {} messages, last_consolidated={}", len(session.messages), session.last_consolidated)
+            logger.info("Memory consolidation done for {} messages", len(messages))
             return True
         except Exception:
             logger.exception("Memory consolidation failed")
             return False
+
+
+class MemoryConsolidator:
+    """Owns consolidation policy, locking, and session offset updates."""
+
+    _MAX_CONSOLIDATION_ROUNDS = 5
+
+    def __init__(
+        self,
+        workspace: Path,
+        provider: LLMProvider,
+        model: str,
+        sessions: SessionManager,
+        context_window_tokens: int,
+        build_messages: Callable[..., list[dict[str, Any]]],
+        get_tool_definitions: Callable[[], list[dict[str, Any]]],
+    ):
+        self.store = MemoryStore(workspace)
+        self.provider = provider
+        self.model = model
+        self.sessions = sessions
+        self.context_window_tokens = context_window_tokens
+        self._build_messages = build_messages
+        self._get_tool_definitions = get_tool_definitions
+        self._locks: weakref.WeakValueDictionary[str, asyncio.Lock] = weakref.WeakValueDictionary()
+
+    def get_lock(self, session_key: str) -> asyncio.Lock:
+        """Return the shared consolidation lock for one session."""
+        return self._locks.setdefault(session_key, asyncio.Lock())
+
+    async def consolidate_messages(self, messages: list[dict[str, object]]) -> bool:
+        """Archive a selected message chunk into persistent memory."""
+        return await self.store.consolidate(messages, self.provider, self.model)
+
+    def pick_consolidation_boundary(
+        self,
+        session: Session,
+        tokens_to_remove: int,
+    ) -> tuple[int, int] | None:
+        """Pick a user-turn boundary that removes enough old prompt tokens."""
+        start = session.last_consolidated
+        if start >= len(session.messages) or tokens_to_remove <= 0:
+            return None
+
+        removed_tokens = 0
+        last_boundary: tuple[int, int] | None = None
+        for idx in range(start, len(session.messages)):
+            message = session.messages[idx]
+            if idx > start and message.get("role") == "user":
+                last_boundary = (idx, removed_tokens)
+                if removed_tokens >= tokens_to_remove:
+                    return last_boundary
+            removed_tokens += estimate_message_tokens(message)
+
+        return last_boundary
+
+    def estimate_session_prompt_tokens(self, session: Session) -> tuple[int, str]:
+        """Estimate current prompt size for the normal session history view."""
+        history = session.get_history(max_messages=0)
+        channel, chat_id = (session.key.split(":", 1) if ":" in session.key else (None, None))
+        probe_messages = self._build_messages(
+            history=history,
+            current_message="[token-probe]",
+            channel=channel,
+            chat_id=chat_id,
+        )
+        return estimate_prompt_tokens_chain(
+            self.provider,
+            self.model,
+            probe_messages,
+            self._get_tool_definitions(),
+        )
+
+    async def archive_unconsolidated(self, session: Session) -> bool:
+        """Archive the full unconsolidated tail for /new-style session rollover."""
+        lock = self.get_lock(session.key)
+        async with lock:
+            snapshot = session.messages[session.last_consolidated:]
+            if not snapshot:
+                return True
+            return await self.consolidate_messages(snapshot)
+
+    async def maybe_consolidate_by_tokens(self, session: Session) -> None:
+        """Loop: archive old messages until prompt fits within half the context window."""
+        if not session.messages or self.context_window_tokens <= 0:
+            return
+
+        lock = self.get_lock(session.key)
+        async with lock:
+            target = self.context_window_tokens // 2
+            estimated, source = self.estimate_session_prompt_tokens(session)
+            if estimated <= 0:
+                return
+            if estimated < self.context_window_tokens:
+                logger.debug(
+                    "Token consolidation idle {}: {}/{} via {}",
+                    session.key,
+                    estimated,
+                    self.context_window_tokens,
+                    source,
+                )
+                return
+
+            for round_num in range(self._MAX_CONSOLIDATION_ROUNDS):
+                if estimated <= target:
+                    return
+
+                boundary = self.pick_consolidation_boundary(session, max(1, estimated - target))
+                if boundary is None:
+                    logger.debug(
+                        "Token consolidation: no safe boundary for {} (round {})",
+                        session.key,
+                        round_num,
+                    )
+                    return
+
+                end_idx = boundary[0]
+                chunk = session.messages[session.last_consolidated:end_idx]
+                if not chunk:
+                    return
+
+                logger.info(
+                    "Token consolidation round {} for {}: {}/{} via {}, chunk={} msgs",
+                    round_num,
+                    session.key,
+                    estimated,
+                    self.context_window_tokens,
+                    source,
+                    len(chunk),
+                )
+                if not await self.consolidate_messages(chunk):
+                    return
+                session.last_consolidated = end_idx
+                self.sessions.save(session)
+
+                estimated, source = self.estimate_session_prompt_tokens(session)
+                if estimated <= 0:
+                    return

nanobot/agent/subagent.py

@@ -16,6 +16,7 @@ from nanobot.bus.events import InboundMessage
 from nanobot.bus.queue import MessageBus
 from nanobot.config.schema import ExecToolConfig
 from nanobot.providers.base import LLMProvider
+from nanobot.utils.helpers import build_assistant_message
 
 
 class SubagentManager:
@@ -123,7 +124,7 @@ class SubagentManager:
             while iteration < max_iterations:
                 iteration += 1
 
-                response = await self.provider.chat(
+                response = await self.provider.chat_with_retry(
                     messages=messages,
                     tools=tools.get_definitions(),
                     model=self.model,
@@ -133,7 +134,6 @@ class SubagentManager:
                 )
 
                 if response.has_tool_calls:
-                    # Add assistant message with tool calls
                     tool_call_dicts = [
                         {
                             "id": tc.id,
@@ -145,11 +145,12 @@ class SubagentManager:
                         }
                         for tc in response.tool_calls
                     ]
-                    messages.append({
-                        "role": "assistant",
-                        "content": response.content or "",
-                        "tool_calls": tool_call_dicts,
-                    })
+                    messages.append(build_assistant_message(
+                        response.content or "",
+                        tool_calls=tool_call_dicts,
+                        reasoning_content=response.reasoning_content,
+                        thinking_blocks=response.thinking_blocks,
+                    ))
 
                     # Execute tools
                     for tool_call in response.tool_calls:

nanobot/channels/dingtalk.py

@@ -57,6 +57,8 @@ class NanobotDingTalkHandler(CallbackHandler):
             content = ""
             if chatbot_msg.text:
                 content = chatbot_msg.text.content.strip()
+            elif chatbot_msg.extensions.get("content", {}).get("recognition"):
+                content = chatbot_msg.extensions["content"]["recognition"].strip()
             if not content:
                 content = message.data.get("text", {}).get("content", "").strip()
 

nanobot/channels/feishu.py

@@ -753,8 +753,9 @@ class FeishuChannel(BaseChannel):
                     None, self._download_file_sync, message_id, file_key, msg_type
                 )
                 if not filename:
-                    ext = {"audio": ".opus", "media": ".mp4"}.get(msg_type, "")
-                    filename = f"{file_key[:16]}{ext}"
+                    filename = file_key[:16]
+                if msg_type == "audio" and not filename.endswith(".opus"):
+                    filename = f"{filename}.opus"
 
         if data and filename:
             file_path = media_dir / filename

nanobot/channels/slack.py

@@ -81,8 +81,8 @@ class SlackChannel(BaseChannel):
             slack_meta = msg.metadata.get("slack", {}) if msg.metadata else {}
             thread_ts = slack_meta.get("thread_ts")
             channel_type = slack_meta.get("channel_type")
-            # Only reply in thread for channel/group messages; DMs don't use threads
-            thread_ts_param = thread_ts if use_thread else None
+            # Slack DMs don't use threads; channel/group replies may keep thread_ts.
+            thread_ts_param = thread_ts if thread_ts and channel_type != "im" else None
 
             # Slack rejects empty text payloads. Keep media-only messages media-only,
             # but send a single blank message when the bot has no text or files to send.
@@ -278,4 +278,3 @@ class SlackChannel(BaseChannel):
             if parts:
                 rows.append(" · ".join(parts))
         return "\n".join(rows)
-

nanobot/channels/telegram.py

@@ -179,6 +179,8 @@ class TelegramChannel(BaseChannel):
         self._media_group_buffers: dict[str, dict] = {}
         self._media_group_tasks: dict[str, asyncio.Task] = {}
         self._message_threads: dict[tuple[str, int], int] = {}
+        self._bot_user_id: int | None = None
+        self._bot_username: str | None = None
 
     def is_allowed(self, sender_id: str) -> bool:
         """Preserve Telegram's legacy id|username allowlist matching."""
@@ -242,6 +244,8 @@ class TelegramChannel(BaseChannel):
 
         # Get bot info and register command menu
         bot_info = await self._app.bot.get_me()
+        self._bot_user_id = getattr(bot_info, "id", None)
+        self._bot_username = getattr(bot_info, "username", None)
         logger.info("Telegram bot @{} connected", bot_info.username)
 
         try:
@@ -462,6 +466,70 @@ class TelegramChannel(BaseChannel):
             "is_forum": bool(getattr(message.chat, "is_forum", False)),
         }
 
+    async def _ensure_bot_identity(self) -> tuple[int | None, str | None]:
+        """Load bot identity once and reuse it for mention/reply checks."""
+        if self._bot_user_id is not None or self._bot_username is not None:
+            return self._bot_user_id, self._bot_username
+        if not self._app:
+            return None, None
+        bot_info = await self._app.bot.get_me()
+        self._bot_user_id = getattr(bot_info, "id", None)
+        self._bot_username = getattr(bot_info, "username", None)
+        return self._bot_user_id, self._bot_username
+
+    @staticmethod
+    def _has_mention_entity(
+        text: str,
+        entities,
+        bot_username: str,
+        bot_id: int | None,
+    ) -> bool:
+        """Check Telegram mention entities against the bot username."""
+        handle = f"@{bot_username}".lower()
+        for entity in entities or []:
+            entity_type = getattr(entity, "type", None)
+            if entity_type == "text_mention":
+                user = getattr(entity, "user", None)
+                if user is not None and bot_id is not None and getattr(user, "id", None) == bot_id:
+                    return True
+                continue
+            if entity_type != "mention":
+                continue
+            offset = getattr(entity, "offset", None)
+            length = getattr(entity, "length", None)
+            if offset is None or length is None:
+                continue
+            if text[offset : offset + length].lower() == handle:
+                return True
+        return handle in text.lower()
+
+    async def _is_group_message_for_bot(self, message) -> bool:
+        """Allow group messages when policy is open, @mentioned, or replying to the bot."""
+        if message.chat.type == "private" or self.config.group_policy == "open":
+            return True
+
+        bot_id, bot_username = await self._ensure_bot_identity()
+        if bot_username:
+            text = message.text or ""
+            caption = message.caption or ""
+            if self._has_mention_entity(
+                text,
+                getattr(message, "entities", None),
+                bot_username,
+                bot_id,
+            ):
+                return True
+            if self._has_mention_entity(
+                caption,
+                getattr(message, "caption_entities", None),
+                bot_username,
+                bot_id,
+            ):
+                return True
+
+        reply_user = getattr(getattr(message, "reply_to_message", None), "from_user", None)
+        return bool(bot_id and reply_user and reply_user.id == bot_id)
+
     def _remember_thread_context(self, message) -> None:
         """Cache topic thread id by chat/message id for follow-up replies."""
         message_thread_id = getattr(message, "message_thread_id", None)
@@ -501,6 +569,9 @@ class TelegramChannel(BaseChannel):
         # Store chat_id for replies
         self._chat_ids[sender_id] = chat_id
 
+        if not await self._is_group_message_for_bot(message):
+            return
+
         # Build content from text and/or media
         content_parts = []
         media_paths = []

nanobot/cli/commands.py

@@ -191,6 +191,8 @@ def onboard():
         save_config(Config())
         console.print(f"[green]✓[/green] Created config at {config_path}")
 
+    console.print("[dim]Config template now uses `maxTokens` + `contextWindowTokens`; `memoryWindow` is no longer a runtime setting.[/dim]")
+
     # Create workspace
     workspace = get_workspace_path()
 
@@ -283,6 +285,16 @@ def _load_runtime_config(config: str | None = None, workspace: str | None = None
     return loaded
 
 
+def _print_deprecated_memory_window_notice(config: Config) -> None:
+    """Warn when running with old memoryWindow-only config."""
+    if config.agents.defaults.should_warn_deprecated_memory_window:
+        console.print(
+            "[yellow]Hint:[/yellow] Detected deprecated `memoryWindow` without "
+            "`contextWindowTokens`. `memoryWindow` is ignored; run "
+            "[cyan]nanobot onboard[/cyan] to refresh your config template."
+        )
+
+
 # ============================================================================
 # Gateway / Server
 # ============================================================================
@@ -290,7 +302,7 @@ def _load_runtime_config(config: str | None = None, workspace: str | None = None
 
 @app.command()
 def gateway(
-    port: int = typer.Option(18790, "--port", "-p", help="Gateway port"),
+    port: int | None = typer.Option(None, "--port", "-p", help="Gateway port"),
     workspace: str | None = typer.Option(None, "--workspace", "-w", help="Workspace directory"),
     verbose: bool = typer.Option(False, "--verbose", "-v", help="Verbose output"),
     config: str | None = typer.Option(None, "--config", "-c", help="Path to config file"),
@@ -310,6 +322,8 @@ def gateway(
         logging.basicConfig(level=logging.DEBUG)
 
     config = _load_runtime_config(config, workspace)
+    _print_deprecated_memory_window_notice(config)
+    port = port if port is not None else config.gateway.port
 
     console.print(f"{__logo__} Starting nanobot gateway on port {port}...")
     sync_workspace_templates(config.workspace_path)
@@ -330,8 +344,8 @@ def gateway(
         temperature=config.agents.defaults.temperature,
         max_tokens=config.agents.defaults.max_tokens,
         max_iterations=config.agents.defaults.max_tool_iterations,
-        memory_window=config.agents.defaults.memory_window,
         reasoning_effort=config.agents.defaults.reasoning_effort,
+        context_window_tokens=config.agents.defaults.context_window_tokens,
         brave_api_key=config.tools.web.search.api_key or None,
         web_proxy=config.tools.web.proxy or None,
         exec_config=config.tools.exec,
@@ -493,6 +507,7 @@ def agent(
     from nanobot.cron.service import CronService
 
     config = _load_runtime_config(config, workspace)
+    _print_deprecated_memory_window_notice(config)
     sync_workspace_templates(config.workspace_path)
 
     bus = MessageBus()
@@ -515,8 +530,8 @@ def agent(
         temperature=config.agents.defaults.temperature,
         max_tokens=config.agents.defaults.max_tokens,
         max_iterations=config.agents.defaults.max_tool_iterations,
-        memory_window=config.agents.defaults.memory_window,
         reasoning_effort=config.agents.defaults.reasoning_effort,
+        context_window_tokens=config.agents.defaults.context_window_tokens,
         brave_api_key=config.tools.web.search.api_key or None,
         web_proxy=config.tools.web.proxy or None,
         exec_config=config.tools.exec,

nanobot/config/schema.py

@@ -33,6 +33,7 @@ class TelegramConfig(Base):
         None  # HTTP/SOCKS5 proxy URL, e.g. "http://127.0.0.1:7890" or "socks5://127.0.0.1:1080"
     )
     reply_to_message: bool = False  # If true, bot replies quote the original message
+    group_policy: Literal["open", "mention"] = "mention"  # "mention" responds when @mentioned or replied to, "open" responds to all
 
 
 class FeishuConfig(Base):
@@ -236,11 +237,18 @@ class AgentDefaults(Base):
         "auto"  # Provider name (e.g. "anthropic", "openrouter") or "auto" for auto-detection
     )
     max_tokens: int = 8192
+    context_window_tokens: int = 65_536
     temperature: float = 0.1
     max_tool_iterations: int = 40
-    memory_window: int = 100
+    # Deprecated compatibility field: accepted from old configs but ignored at runtime.
+    memory_window: int | None = Field(default=None, exclude=True)
     reasoning_effort: str | None = None  # low / medium / high — enables LLM thinking mode
 
+    @property
+    def should_warn_deprecated_memory_window(self) -> bool:
+        """Return True when old memoryWindow is present without contextWindowTokens."""
+        return self.memory_window is not None and "context_window_tokens" not in self.model_fields_set
+
 
 class AgentsConfig(Base):
     """Agent configuration."""

nanobot/heartbeat/service.py

@@ -87,7 +87,7 @@ class HeartbeatService:
 
         Returns (action, tasks) where action is 'skip' or 'run'.
         """
-        response = await self.provider.chat(
+        response = await self.provider.chat_with_retry(
             messages=[
                 {"role": "system", "content": "You are a heartbeat agent. Call the heartbeat tool to report your decision."},
                 {"role": "user", "content": (

nanobot/providers/base.py

@@ -1,9 +1,12 @@
 """Base LLM provider interface."""
 
+import asyncio
 from abc import ABC, abstractmethod
 from dataclasses import dataclass, field
 from typing import Any
 
+from loguru import logger
+
 
 @dataclass
 class ToolCallRequest:
@@ -37,6 +40,22 @@ class LLMProvider(ABC):
     while maintaining a consistent interface.
     """
 
+    _CHAT_RETRY_DELAYS = (1, 2, 4)
+    _TRANSIENT_ERROR_MARKERS = (
+        "429",
+        "rate limit",
+        "500",
+        "502",
+        "503",
+        "504",
+        "overloaded",
+        "timeout",
+        "timed out",
+        "connection",
+        "server error",
+        "temporarily unavailable",
+    )
+
     def __init__(self, api_key: str | None = None, api_base: str | None = None):
         self.api_key = api_key
         self.api_base = api_base
@@ -126,6 +145,71 @@ class LLMProvider(ABC):
         """
         pass
 
+    @classmethod
+    def _is_transient_error(cls, content: str | None) -> bool:
+        err = (content or "").lower()
+        return any(marker in err for marker in cls._TRANSIENT_ERROR_MARKERS)
+
+    async def chat_with_retry(
+        self,
+        messages: list[dict[str, Any]],
+        tools: list[dict[str, Any]] | None = None,
+        model: str | None = None,
+        max_tokens: int = 4096,
+        temperature: float = 0.7,
+        reasoning_effort: str | None = None,
+    ) -> LLMResponse:
+        """Call chat() with retry on transient provider failures."""
+        for attempt, delay in enumerate(self._CHAT_RETRY_DELAYS, start=1):
+            try:
+                response = await self.chat(
+                    messages=messages,
+                    tools=tools,
+                    model=model,
+                    max_tokens=max_tokens,
+                    temperature=temperature,
+                    reasoning_effort=reasoning_effort,
+                )
+            except asyncio.CancelledError:
+                raise
+            except Exception as exc:
+                response = LLMResponse(
+                    content=f"Error calling LLM: {exc}",
+                    finish_reason="error",
+                )
+
+            if response.finish_reason != "error":
+                return response
+            if not self._is_transient_error(response.content):
+                return response
+
+            err = (response.content or "").lower()
+            logger.warning(
+                "LLM transient error (attempt {}/{}), retrying in {}s: {}",
+                attempt,
+                len(self._CHAT_RETRY_DELAYS),
+                delay,
+                err[:120],
+            )
+            await asyncio.sleep(delay)
+
+        try:
+            return await self.chat(
+                messages=messages,
+                tools=tools,
+                model=model,
+                max_tokens=max_tokens,
+                temperature=temperature,
+                reasoning_effort=reasoning_effort,
+            )
+        except asyncio.CancelledError:
+            raise
+        except Exception as exc:
+            return LLMResponse(
+                content=f"Error calling LLM: {exc}",
+                finish_reason="error",
+            )
+
     @abstractmethod
     def get_default_model(self) -> str:
         """Get the default model for this provider."""

nanobot/skills/skill-creator/SKILL.md

@@ -268,6 +268,8 @@ Skip this step only if the skill being developed already exists, and iteration o
 
 When creating a new skill from scratch, always run the `init_skill.py` script. The script conveniently generates a new template skill directory that automatically includes everything a skill requires, making the skill creation process much more efficient and reliable.
 
+For `nanobot`, custom skills should live under the active workspace `skills/` directory so they can be discovered automatically at runtime (for example, `<workspace>/skills/my-skill/SKILL.md`).
+
 Usage:
 
 ```bash
@@ -277,9 +279,9 @@ scripts/init_skill.py <skill-name> --path <output-directory> [--resources script
 Examples:
 
 ```bash
-scripts/init_skill.py my-skill --path skills/public
-scripts/init_skill.py my-skill --path skills/public --resources scripts,references
-scripts/init_skill.py my-skill --path skills/public --resources scripts --examples
+scripts/init_skill.py my-skill --path ./workspace/skills
+scripts/init_skill.py my-skill --path ./workspace/skills --resources scripts,references
+scripts/init_skill.py my-skill --path ./workspace/skills --resources scripts --examples
 ```
 
 The script:
@@ -326,7 +328,7 @@ Write the YAML frontmatter with `name` and `description`:
   - Include all "when to use" information here - Not in the body. The body is only loaded after triggering, so "When to Use This Skill" sections in the body are not helpful to the agent.
   - Example description for a `docx` skill: "Comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. Use when the agent needs to work with professional documents (.docx files) for: (1) Creating new documents, (2) Modifying or editing content, (3) Working with tracked changes, (4) Adding comments, or any other document tasks"
 
-Do not include any other fields in YAML frontmatter.
+Keep frontmatter minimal. In `nanobot`, `metadata` and `always` are also supported when needed, but avoid adding extra fields unless they are actually required.
 
 ##### Body
 
@@ -349,7 +351,6 @@ scripts/package_skill.py <path/to/skill-folder> ./dist
 The packaging script will:
 
 1. **Validate** the skill automatically, checking:
-
    - YAML frontmatter format and required fields
    - Skill naming conventions and directory structure
    - Description completeness and quality
@@ -357,6 +358,8 @@ The packaging script will:
 
 2. **Package** the skill if validation passes, creating a .skill file named after the skill (e.g., `my-skill.skill`) that includes all files and maintains the proper directory structure for distribution. The .skill file is a zip file with a .skill extension.
 
+   Security restriction: symlinks are rejected and packaging fails when any symlink is present.
+
 If validation fails, the script will report the errors and exit without creating a package. Fix any validation errors and run the packaging command again.
 
 ### Step 6: Iterate

nanobot/skills/skill-creator/scripts/init_skill.py

@@ -0,0 +1,378 @@
+#!/usr/bin/env python3
+"""
+Skill Initializer - Creates a new skill from template
+
+Usage:
+    init_skill.py <skill-name> --path <path> [--resources scripts,references,assets] [--examples]
+
+Examples:
+    init_skill.py my-new-skill --path skills/public
+    init_skill.py my-new-skill --path skills/public --resources scripts,references
+    init_skill.py my-api-helper --path skills/private --resources scripts --examples
+    init_skill.py custom-skill --path /custom/location
+"""
+
+import argparse
+import re
+import sys
+from pathlib import Path
+
+MAX_SKILL_NAME_LENGTH = 64
+ALLOWED_RESOURCES = {"scripts", "references", "assets"}
+
+SKILL_TEMPLATE = """---
+name: {skill_name}
+description: [TODO: Complete and informative explanation of what the skill does and when to use it. Include WHEN to use this skill - specific scenarios, file types, or tasks that trigger it.]
+---
+
+# {skill_title}
+
+## Overview
+
+[TODO: 1-2 sentences explaining what this skill enables]
+
+## Structuring This Skill
+
+[TODO: Choose the structure that best fits this skill's purpose. Common patterns:
+
+**1. Workflow-Based** (best for sequential processes)
+- Works well when there are clear step-by-step procedures
+- Example: DOCX skill with "Workflow Decision Tree" -> "Reading" -> "Creating" -> "Editing"
+- Structure: ## Overview -> ## Workflow Decision Tree -> ## Step 1 -> ## Step 2...
+
+**2. Task-Based** (best for tool collections)
+- Works well when the skill offers different operations/capabilities
+- Example: PDF skill with "Quick Start" -> "Merge PDFs" -> "Split PDFs" -> "Extract Text"
+- Structure: ## Overview -> ## Quick Start -> ## Task Category 1 -> ## Task Category 2...
+
+**3. Reference/Guidelines** (best for standards or specifications)
+- Works well for brand guidelines, coding standards, or requirements
+- Example: Brand styling with "Brand Guidelines" -> "Colors" -> "Typography" -> "Features"
+- Structure: ## Overview -> ## Guidelines -> ## Specifications -> ## Usage...
+
+**4. Capabilities-Based** (best for integrated systems)
+- Works well when the skill provides multiple interrelated features
+- Example: Product Management with "Core Capabilities" -> numbered capability list
+- Structure: ## Overview -> ## Core Capabilities -> ### 1. Feature -> ### 2. Feature...
+
+Patterns can be mixed and matched as needed. Most skills combine patterns (e.g., start with task-based, add workflow for complex operations).
+
+Delete this entire "Structuring This Skill" section when done - it's just guidance.]
+
+## [TODO: Replace with the first main section based on chosen structure]
+
+[TODO: Add content here. See examples in existing skills:
+- Code samples for technical skills
+- Decision trees for complex workflows
+- Concrete examples with realistic user requests
+- References to scripts/templates/references as needed]
+
+## Resources (optional)
+
+Create only the resource directories this skill actually needs. Delete this section if no resources are required.
+
+### scripts/
+Executable code (Python/Bash/etc.) that can be run directly to perform specific operations.
+
+**Examples from other skills:**
+- PDF skill: `fill_fillable_fields.py`, `extract_form_field_info.py` - utilities for PDF manipulation
+- DOCX skill: `document.py`, `utilities.py` - Python modules for document processing
+
+**Appropriate for:** Python scripts, shell scripts, or any executable code that performs automation, data processing, or specific operations.
+
+**Note:** Scripts may be executed without loading into context, but can still be read by Codex for patching or environment adjustments.
+
+### references/
+Documentation and reference material intended to be loaded into context to inform Codex's process and thinking.
+
+**Examples from other skills:**
+- Product management: `communication.md`, `context_building.md` - detailed workflow guides
+- BigQuery: API reference documentation and query examples
+- Finance: Schema documentation, company policies
+
+**Appropriate for:** In-depth documentation, API references, database schemas, comprehensive guides, or any detailed information that Codex should reference while working.
+
+### assets/
+Files not intended to be loaded into context, but rather used within the output Codex produces.
+
+**Examples from other skills:**
+- Brand styling: PowerPoint template files (.pptx), logo files
+- Frontend builder: HTML/React boilerplate project directories
+- Typography: Font files (.ttf, .woff2)
+
+**Appropriate for:** Templates, boilerplate code, document templates, images, icons, fonts, or any files meant to be copied or used in the final output.
+
+---
+
+**Not every skill requires all three types of resources.**
+"""
+
+EXAMPLE_SCRIPT = '''#!/usr/bin/env python3
+"""
+Example helper script for {skill_name}
+
+This is a placeholder script that can be executed directly.
+Replace with actual implementation or delete if not needed.
+
+Example real scripts from other skills:
+- pdf/scripts/fill_fillable_fields.py - Fills PDF form fields
+- pdf/scripts/convert_pdf_to_images.py - Converts PDF pages to images
+"""
+
+def main():
+    print("This is an example script for {skill_name}")
+    # TODO: Add actual script logic here
+    # This could be data processing, file conversion, API calls, etc.
+
+if __name__ == "__main__":
+    main()
+'''
+
+EXAMPLE_REFERENCE = """# Reference Documentation for {skill_title}
+
+This is a placeholder for detailed reference documentation.
+Replace with actual reference content or delete if not needed.
+
+Example real reference docs from other skills:
+- product-management/references/communication.md - Comprehensive guide for status updates
+- product-management/references/context_building.md - Deep-dive on gathering context
+- bigquery/references/ - API references and query examples
+
+## When Reference Docs Are Useful
+
+Reference docs are ideal for:
+- Comprehensive API documentation
+- Detailed workflow guides
+- Complex multi-step processes
+- Information too lengthy for main SKILL.md
+- Content that's only needed for specific use cases
+
+## Structure Suggestions
+
+### API Reference Example
+- Overview
+- Authentication
+- Endpoints with examples
+- Error codes
+- Rate limits
+
+### Workflow Guide Example
+- Prerequisites
+- Step-by-step instructions
+- Common patterns
+- Troubleshooting
+- Best practices
+"""
+
+EXAMPLE_ASSET = """# Example Asset File
+
+This placeholder represents where asset files would be stored.
+Replace with actual asset files (templates, images, fonts, etc.) or delete if not needed.
+
+Asset files are NOT intended to be loaded into context, but rather used within
+the output Codex produces.
+
+Example asset files from other skills:
+- Brand guidelines: logo.png, slides_template.pptx
+- Frontend builder: hello-world/ directory with HTML/React boilerplate
+- Typography: custom-font.ttf, font-family.woff2
+- Data: sample_data.csv, test_dataset.json
+
+## Common Asset Types
+
+- Templates: .pptx, .docx, boilerplate directories
+- Images: .png, .jpg, .svg, .gif
+- Fonts: .ttf, .otf, .woff, .woff2
+- Boilerplate code: Project directories, starter files
+- Icons: .ico, .svg
+- Data files: .csv, .json, .xml, .yaml
+
+Note: This is a text placeholder. Actual assets can be any file type.
+"""
+
+
+def normalize_skill_name(skill_name):
+    """Normalize a skill name to lowercase hyphen-case."""
+    normalized = skill_name.strip().lower()
+    normalized = re.sub(r"[^a-z0-9]+", "-", normalized)
+    normalized = normalized.strip("-")
+    normalized = re.sub(r"-{2,}", "-", normalized)
+    return normalized
+
+
+def title_case_skill_name(skill_name):
+    """Convert hyphenated skill name to Title Case for display."""
+    return " ".join(word.capitalize() for word in skill_name.split("-"))
+
+
+def parse_resources(raw_resources):
+    if not raw_resources:
+        return []
+    resources = [item.strip() for item in raw_resources.split(",") if item.strip()]
+    invalid = sorted({item for item in resources if item not in ALLOWED_RESOURCES})
+    if invalid:
+        allowed = ", ".join(sorted(ALLOWED_RESOURCES))
+        print(f"[ERROR] Unknown resource type(s): {', '.join(invalid)}")
+        print(f"   Allowed: {allowed}")
+        sys.exit(1)
+    deduped = []
+    seen = set()
+    for resource in resources:
+        if resource not in seen:
+            deduped.append(resource)
+            seen.add(resource)
+    return deduped
+
+
+def create_resource_dirs(skill_dir, skill_name, skill_title, resources, include_examples):
+    for resource in resources:
+        resource_dir = skill_dir / resource
+        resource_dir.mkdir(exist_ok=True)
+        if resource == "scripts":
+            if include_examples:
+                example_script = resource_dir / "example.py"
+                example_script.write_text(EXAMPLE_SCRIPT.format(skill_name=skill_name))
+                example_script.chmod(0o755)
+                print("[OK] Created scripts/example.py")
+            else:
+                print("[OK] Created scripts/")
+        elif resource == "references":
+            if include_examples:
+                example_reference = resource_dir / "api_reference.md"
+                example_reference.write_text(EXAMPLE_REFERENCE.format(skill_title=skill_title))
+                print("[OK] Created references/api_reference.md")
+            else:
+                print("[OK] Created references/")
+        elif resource == "assets":
+            if include_examples:
+                example_asset = resource_dir / "example_asset.txt"
+                example_asset.write_text(EXAMPLE_ASSET)
+                print("[OK] Created assets/example_asset.txt")
+            else:
+                print("[OK] Created assets/")
+
+
+def init_skill(skill_name, path, resources, include_examples):
+    """
+    Initialize a new skill directory with template SKILL.md.
+
+    Args:
+        skill_name: Name of the skill
+        path: Path where the skill directory should be created
+        resources: Resource directories to create
+        include_examples: Whether to create example files in resource directories
+
+    Returns:
+        Path to created skill directory, or None if error
+    """
+    # Determine skill directory path
+    skill_dir = Path(path).resolve() / skill_name
+
+    # Check if directory already exists
+    if skill_dir.exists():
+        print(f"[ERROR] Skill directory already exists: {skill_dir}")
+        return None
+
+    # Create skill directory
+    try:
+        skill_dir.mkdir(parents=True, exist_ok=False)
+        print(f"[OK] Created skill directory: {skill_dir}")
+    except Exception as e:
+        print(f"[ERROR] Error creating directory: {e}")
+        return None
+
+    # Create SKILL.md from template
+    skill_title = title_case_skill_name(skill_name)
+    skill_content = SKILL_TEMPLATE.format(skill_name=skill_name, skill_title=skill_title)
+
+    skill_md_path = skill_dir / "SKILL.md"
+    try:
+        skill_md_path.write_text(skill_content)
+        print("[OK] Created SKILL.md")
+    except Exception as e:
+        print(f"[ERROR] Error creating SKILL.md: {e}")
+        return None
+
+    # Create resource directories if requested
+    if resources:
+        try:
+            create_resource_dirs(skill_dir, skill_name, skill_title, resources, include_examples)
+        except Exception as e:
+            print(f"[ERROR] Error creating resource directories: {e}")
+            return None
+
+    # Print next steps
+    print(f"\n[OK] Skill '{skill_name}' initialized successfully at {skill_dir}")
+    print("\nNext steps:")
+    print("1. Edit SKILL.md to complete the TODO items and update the description")
+    if resources:
+        if include_examples:
+            print("2. Customize or delete the example files in scripts/, references/, and assets/")
+        else:
+            print("2. Add resources to scripts/, references/, and assets/ as needed")
+    else:
+        print("2. Create resource directories only if needed (scripts/, references/, assets/)")
+    print("3. Run the validator when ready to check the skill structure")
+
+    return skill_dir
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Create a new skill directory with a SKILL.md template.",
+    )
+    parser.add_argument("skill_name", help="Skill name (normalized to hyphen-case)")
+    parser.add_argument("--path", required=True, help="Output directory for the skill")
+    parser.add_argument(
+        "--resources",
+        default="",
+        help="Comma-separated list: scripts,references,assets",
+    )
+    parser.add_argument(
+        "--examples",
+        action="store_true",
+        help="Create example files inside the selected resource directories",
+    )
+    args = parser.parse_args()
+
+    raw_skill_name = args.skill_name
+    skill_name = normalize_skill_name(raw_skill_name)
+    if not skill_name:
+        print("[ERROR] Skill name must include at least one letter or digit.")
+        sys.exit(1)
+    if len(skill_name) > MAX_SKILL_NAME_LENGTH:
+        print(
+            f"[ERROR] Skill name '{skill_name}' is too long ({len(skill_name)} characters). "
+            f"Maximum is {MAX_SKILL_NAME_LENGTH} characters."
+        )
+        sys.exit(1)
+    if skill_name != raw_skill_name:
+        print(f"Note: Normalized skill name from '{raw_skill_name}' to '{skill_name}'.")
+
+    resources = parse_resources(args.resources)
+    if args.examples and not resources:
+        print("[ERROR] --examples requires --resources to be set.")
+        sys.exit(1)
+
+    path = args.path
+
+    print(f"Initializing skill: {skill_name}")
+    print(f"   Location: {path}")
+    if resources:
+        print(f"   Resources: {', '.join(resources)}")
+        if args.examples:
+            print("   Examples: enabled")
+    else:
+        print("   Resources: none (create as needed)")
+    print()
+
+    result = init_skill(skill_name, path, resources, args.examples)
+
+    if result:
+        sys.exit(0)
+    else:
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

nanobot/skills/skill-creator/scripts/package_skill.py

@@ -0,0 +1,154 @@
+#!/usr/bin/env python3
+"""
+Skill Packager - Creates a distributable .skill file of a skill folder
+
+Usage:
+    python package_skill.py <path/to/skill-folder> [output-directory]
+
+Example:
+    python package_skill.py skills/public/my-skill
+    python package_skill.py skills/public/my-skill ./dist
+"""
+
+import sys
+import zipfile
+from pathlib import Path
+
+from quick_validate import validate_skill
+
+
+def _is_within(path: Path, root: Path) -> bool:
+    try:
+        path.relative_to(root)
+        return True
+    except ValueError:
+        return False
+
+
+def _cleanup_partial_archive(skill_filename: Path) -> None:
+    try:
+        if skill_filename.exists():
+            skill_filename.unlink()
+    except OSError:
+        pass
+
+
+def package_skill(skill_path, output_dir=None):
+    """
+    Package a skill folder into a .skill file.
+
+    Args:
+        skill_path: Path to the skill folder
+        output_dir: Optional output directory for the .skill file (defaults to current directory)
+
+    Returns:
+        Path to the created .skill file, or None if error
+    """
+    skill_path = Path(skill_path).resolve()
+
+    # Validate skill folder exists
+    if not skill_path.exists():
+        print(f"[ERROR] Skill folder not found: {skill_path}")
+        return None
+
+    if not skill_path.is_dir():
+        print(f"[ERROR] Path is not a directory: {skill_path}")
+        return None
+
+    # Validate SKILL.md exists
+    skill_md = skill_path / "SKILL.md"
+    if not skill_md.exists():
+        print(f"[ERROR] SKILL.md not found in {skill_path}")
+        return None
+
+    # Run validation before packaging
+    print("Validating skill...")
+    valid, message = validate_skill(skill_path)
+    if not valid:
+        print(f"[ERROR] Validation failed: {message}")
+        print("   Please fix the validation errors before packaging.")
+        return None
+    print(f"[OK] {message}\n")
+
+    # Determine output location
+    skill_name = skill_path.name
+    if output_dir:
+        output_path = Path(output_dir).resolve()
+        output_path.mkdir(parents=True, exist_ok=True)
+    else:
+        output_path = Path.cwd()
+
+    skill_filename = output_path / f"{skill_name}.skill"
+
+    EXCLUDED_DIRS = {".git", ".svn", ".hg", "__pycache__", "node_modules"}
+
+    files_to_package = []
+    resolved_archive = skill_filename.resolve()
+
+    for file_path in skill_path.rglob("*"):
+        # Fail closed on symlinks so the packaged contents are explicit and predictable.
+        if file_path.is_symlink():
+            print(f"[ERROR] Symlink not allowed in packaged skill: {file_path}")
+            _cleanup_partial_archive(skill_filename)
+            return None
+
+        rel_parts = file_path.relative_to(skill_path).parts
+        if any(part in EXCLUDED_DIRS for part in rel_parts):
+            continue
+
+        if file_path.is_file():
+            resolved_file = file_path.resolve()
+            if not _is_within(resolved_file, skill_path):
+                print(f"[ERROR] File escapes skill root: {file_path}")
+                _cleanup_partial_archive(skill_filename)
+                return None
+            # If output lives under skill_path, avoid writing archive into itself.
+            if resolved_file == resolved_archive:
+                print(f"[WARN] Skipping output archive: {file_path}")
+                continue
+            files_to_package.append(file_path)
+
+    # Create the .skill file (zip format)
+    try:
+        with zipfile.ZipFile(skill_filename, "w", zipfile.ZIP_DEFLATED) as zipf:
+            for file_path in files_to_package:
+                # Calculate the relative path within the zip.
+                arcname = Path(skill_name) / file_path.relative_to(skill_path)
+                zipf.write(file_path, arcname)
+                print(f"  Added: {arcname}")
+
+        print(f"\n[OK] Successfully packaged skill to: {skill_filename}")
+        return skill_filename
+
+    except Exception as e:
+        _cleanup_partial_archive(skill_filename)
+        print(f"[ERROR] Error creating .skill file: {e}")
+        return None
+
+
+def main():
+    if len(sys.argv) < 2:
+        print("Usage: python package_skill.py <path/to/skill-folder> [output-directory]")
+        print("\nExample:")
+        print("  python package_skill.py skills/public/my-skill")
+        print("  python package_skill.py skills/public/my-skill ./dist")
+        sys.exit(1)
+
+    skill_path = sys.argv[1]
+    output_dir = sys.argv[2] if len(sys.argv) > 2 else None
+
+    print(f"Packaging skill: {skill_path}")
+    if output_dir:
+        print(f"   Output directory: {output_dir}")
+    print()
+
+    result = package_skill(skill_path, output_dir)
+
+    if result:
+        sys.exit(0)
+    else:
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

nanobot/skills/skill-creator/scripts/quick_validate.py

@@ -0,0 +1,213 @@
+#!/usr/bin/env python3
+"""
+Minimal validator for nanobot skill folders.
+"""
+
+import re
+import sys
+from pathlib import Path
+from typing import Optional
+
+try:
+    import yaml
+except ModuleNotFoundError:
+    yaml = None
+
+MAX_SKILL_NAME_LENGTH = 64
+ALLOWED_FRONTMATTER_KEYS = {
+    "name",
+    "description",
+    "metadata",
+    "always",
+    "license",
+    "allowed-tools",
+}
+ALLOWED_RESOURCE_DIRS = {"scripts", "references", "assets"}
+PLACEHOLDER_MARKERS = ("[todo", "todo:")
+
+
+def _extract_frontmatter(content: str) -> Optional[str]:
+    lines = content.splitlines()
+    if not lines or lines[0].strip() != "---":
+        return None
+    for i in range(1, len(lines)):
+        if lines[i].strip() == "---":
+            return "\n".join(lines[1:i])
+    return None
+
+
+def _parse_simple_frontmatter(frontmatter_text: str) -> Optional[dict[str, str]]:
+    """Fallback parser for simple frontmatter when PyYAML is unavailable."""
+    parsed: dict[str, str] = {}
+    current_key: Optional[str] = None
+    multiline_key: Optional[str] = None
+
+    for raw_line in frontmatter_text.splitlines():
+        stripped = raw_line.strip()
+        if not stripped or stripped.startswith("#"):
+            continue
+
+        is_indented = raw_line[:1].isspace()
+        if is_indented:
+            if current_key is None:
+                return None
+            current_value = parsed[current_key]
+            parsed[current_key] = f"{current_value}\n{stripped}" if current_value else stripped
+            continue
+
+        if ":" not in stripped:
+            return None
+
+        key, value = stripped.split(":", 1)
+        key = key.strip()
+        value = value.strip()
+        if not key:
+            return None
+
+        if value in {"|", ">"}:
+            parsed[key] = ""
+            current_key = key
+            multiline_key = key
+            continue
+
+        if (value.startswith('"') and value.endswith('"')) or (
+            value.startswith("'") and value.endswith("'")
+        ):
+            value = value[1:-1]
+        parsed[key] = value
+        current_key = key
+        multiline_key = None
+
+    if multiline_key is not None and multiline_key not in parsed:
+        return None
+    return parsed
+
+
+def _load_frontmatter(frontmatter_text: str) -> tuple[Optional[dict], Optional[str]]:
+    if yaml is not None:
+        try:
+            frontmatter = yaml.safe_load(frontmatter_text)
+        except yaml.YAMLError as exc:
+            return None, f"Invalid YAML in frontmatter: {exc}"
+        if not isinstance(frontmatter, dict):
+            return None, "Frontmatter must be a YAML dictionary"
+        return frontmatter, None
+
+    frontmatter = _parse_simple_frontmatter(frontmatter_text)
+    if frontmatter is None:
+        return None, "Invalid YAML in frontmatter: unsupported syntax without PyYAML installed"
+    return frontmatter, None
+
+
+def _validate_skill_name(name: str, folder_name: str) -> Optional[str]:
+    if not re.fullmatch(r"[a-z0-9]+(?:-[a-z0-9]+)*", name):
+        return (
+            f"Name '{name}' should be hyphen-case "
+            "(lowercase letters, digits, and single hyphens only)"
+        )
+    if len(name) > MAX_SKILL_NAME_LENGTH:
+        return (
+            f"Name is too long ({len(name)} characters). "
+            f"Maximum is {MAX_SKILL_NAME_LENGTH} characters."
+        )
+    if name != folder_name:
+        return f"Skill name '{name}' must match directory name '{folder_name}'"
+    return None
+
+
+def _validate_description(description: str) -> Optional[str]:
+    trimmed = description.strip()
+    if not trimmed:
+        return "Description cannot be empty"
+    lowered = trimmed.lower()
+    if any(marker in lowered for marker in PLACEHOLDER_MARKERS):
+        return "Description still contains TODO placeholder text"
+    if "<" in trimmed or ">" in trimmed:
+        return "Description cannot contain angle brackets (< or >)"
+    if len(trimmed) > 1024:
+        return f"Description is too long ({len(trimmed)} characters). Maximum is 1024 characters."
+    return None
+
+
+def validate_skill(skill_path):
+    """Validate a skill folder structure and required frontmatter."""
+    skill_path = Path(skill_path).resolve()
+
+    if not skill_path.exists():
+        return False, f"Skill folder not found: {skill_path}"
+    if not skill_path.is_dir():
+        return False, f"Path is not a directory: {skill_path}"
+
+    skill_md = skill_path / "SKILL.md"
+    if not skill_md.exists():
+        return False, "SKILL.md not found"
+
+    try:
+        content = skill_md.read_text(encoding="utf-8")
+    except OSError as exc:
+        return False, f"Could not read SKILL.md: {exc}"
+
+    frontmatter_text = _extract_frontmatter(content)
+    if frontmatter_text is None:
+        return False, "Invalid frontmatter format"
+
+    frontmatter, error = _load_frontmatter(frontmatter_text)
+    if error:
+        return False, error
+
+    unexpected_keys = sorted(set(frontmatter.keys()) - ALLOWED_FRONTMATTER_KEYS)
+    if unexpected_keys:
+        allowed = ", ".join(sorted(ALLOWED_FRONTMATTER_KEYS))
+        unexpected = ", ".join(unexpected_keys)
+        return (
+            False,
+            f"Unexpected key(s) in SKILL.md frontmatter: {unexpected}. Allowed properties are: {allowed}",
+        )
+
+    if "name" not in frontmatter:
+        return False, "Missing 'name' in frontmatter"
+    if "description" not in frontmatter:
+        return False, "Missing 'description' in frontmatter"
+
+    name = frontmatter["name"]
+    if not isinstance(name, str):
+        return False, f"Name must be a string, got {type(name).__name__}"
+    name_error = _validate_skill_name(name.strip(), skill_path.name)
+    if name_error:
+        return False, name_error
+
+    description = frontmatter["description"]
+    if not isinstance(description, str):
+        return False, f"Description must be a string, got {type(description).__name__}"
+    description_error = _validate_description(description)
+    if description_error:
+        return False, description_error
+
+    always = frontmatter.get("always")
+    if always is not None and not isinstance(always, bool):
+        return False, f"'always' must be a boolean, got {type(always).__name__}"
+
+    for child in skill_path.iterdir():
+        if child.name == "SKILL.md":
+            continue
+        if child.is_dir() and child.name in ALLOWED_RESOURCE_DIRS:
+            continue
+        if child.is_symlink():
+            continue
+        return (
+            False,
+            f"Unexpected file or directory in skill root: {child.name}. "
+            "Only SKILL.md, scripts/, references/, and assets/ are allowed.",
+        )
+
+    return True, "Skill is valid!"
+
+
+if __name__ == "__main__":
+    if len(sys.argv) != 2:
+        print("Usage: python quick_validate.py <skill_directory>")
+        sys.exit(1)
+
+    valid, message = validate_skill(sys.argv[1])
+    print(message)
+    sys.exit(0 if valid else 1)

nanobot/utils/helpers.py

@@ -1,8 +1,12 @@
 """Utility functions for nanobot."""
 
+import json
 import re
 from datetime import datetime
 from pathlib import Path
+from typing import Any
+
+import tiktoken
 
 
 def detect_image_mime(data: bytes) -> str | None:
@@ -68,6 +72,104 @@ def split_message(content: str, max_len: int = 2000) -> list[str]:
     return chunks
 
 
+def build_assistant_message(
+    content: str | None,
+    tool_calls: list[dict[str, Any]] | None = None,
+    reasoning_content: str | None = None,
+    thinking_blocks: list[dict] | None = None,
+) -> dict[str, Any]:
+    """Build a provider-safe assistant message with optional reasoning fields."""
+    msg: dict[str, Any] = {"role": "assistant", "content": content}
+    if tool_calls:
+        msg["tool_calls"] = tool_calls
+    if reasoning_content is not None:
+        msg["reasoning_content"] = reasoning_content
+    if thinking_blocks:
+        msg["thinking_blocks"] = thinking_blocks
+    return msg
+
+
+def estimate_prompt_tokens(
+    messages: list[dict[str, Any]],
+    tools: list[dict[str, Any]] | None = None,
+) -> int:
+    """Estimate prompt tokens with tiktoken."""
+    try:
+        enc = tiktoken.get_encoding("cl100k_base")
+        parts: list[str] = []
+        for msg in messages:
+            content = msg.get("content")
+            if isinstance(content, str):
+                parts.append(content)
+            elif isinstance(content, list):
+                for part in content:
+                    if isinstance(part, dict) and part.get("type") == "text":
+                        txt = part.get("text", "")
+                        if txt:
+                            parts.append(txt)
+        if tools:
+            parts.append(json.dumps(tools, ensure_ascii=False))
+        return len(enc.encode("\n".join(parts)))
+    except Exception:
+        return 0
+
+
+def estimate_message_tokens(message: dict[str, Any]) -> int:
+    """Estimate prompt tokens contributed by one persisted message."""
+    content = message.get("content")
+    parts: list[str] = []
+    if isinstance(content, str):
+        parts.append(content)
+    elif isinstance(content, list):
+        for part in content:
+            if isinstance(part, dict) and part.get("type") == "text":
+                text = part.get("text", "")
+                if text:
+                    parts.append(text)
+            else:
+                parts.append(json.dumps(part, ensure_ascii=False))
+    elif content is not None:
+        parts.append(json.dumps(content, ensure_ascii=False))
+
+    for key in ("name", "tool_call_id"):
+        value = message.get(key)
+        if isinstance(value, str) and value:
+            parts.append(value)
+    if message.get("tool_calls"):
+        parts.append(json.dumps(message["tool_calls"], ensure_ascii=False))
+
+    payload = "\n".join(parts)
+    if not payload:
+        return 1
+    try:
+        enc = tiktoken.get_encoding("cl100k_base")
+        return max(1, len(enc.encode(payload)))
+    except Exception:
+        return max(1, len(payload) // 4)
+
+
+def estimate_prompt_tokens_chain(
+    provider: Any,
+    model: str | None,
+    messages: list[dict[str, Any]],
+    tools: list[dict[str, Any]] | None = None,
+) -> tuple[int, str]:
+    """Estimate prompt tokens via provider counter first, then tiktoken fallback."""
+    provider_counter = getattr(provider, "estimate_prompt_tokens", None)
+    if callable(provider_counter):
+        try:
+            tokens, source = provider_counter(messages, tools, model)
+            if isinstance(tokens, (int, float)) and tokens > 0:
+                return int(tokens), str(source or "provider_counter")
+        except Exception:
+            pass
+
+    estimated = estimate_prompt_tokens(messages, tools)
+    if estimated > 0:
+        return int(estimated), "tiktoken"
+    return 0, "none"
+
+
 def sync_workspace_templates(workspace: Path, silent: bool = False) -> list[str]:
     """Sync bundled templates to workspace. Only creates missing files."""
     from importlib.resources import files as pkg_files
@@ -88,7 +190,7 @@ def sync_workspace_templates(workspace: Path, silent: bool = False) -> list[str]
         added.append(str(dest.relative_to(workspace)))
 
     for item in tpl.iterdir():
-        if item.name.endswith(".md"):
+        if item.name.endswith(".md") and not item.name.startswith("."):
             _write(item, workspace / item.name)
     _write(tpl / "memory" / "MEMORY.md", workspace / "memory" / "MEMORY.md")
     _write(None, workspace / "memory" / "HISTORY.md")