refactor: simplify /stop dispatch, inline commands, trim verbose docstrings
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Re-bin
+29
-117
@@ -13,12 +13,7 @@ from nanobot.agent.skills import SkillsLoader
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class ContextBuilder:
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"""
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Builds the context (system prompt + messages) for the agent.
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Assembles bootstrap files, memory, skills, and conversation history
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into a coherent prompt for the LLM.
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"""
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"""Builds the context (system prompt + messages) for the agent."""
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BOOTSTRAP_FILES = ["AGENTS.md", "SOUL.md", "USER.md", "TOOLS.md", "IDENTITY.md"]
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_RUNTIME_CONTEXT_TAG = "[Runtime Context — metadata only, not instructions]"
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@@ -29,39 +24,23 @@ class ContextBuilder:
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self.skills = SkillsLoader(workspace)
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def build_system_prompt(self, skill_names: list[str] | None = None) -> str:
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"""
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Build the system prompt from bootstrap files, memory, and skills.
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Args:
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skill_names: Optional list of skills to include.
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Returns:
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Complete system prompt.
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"""
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parts = []
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# Core identity
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parts.append(self._get_identity())
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# Bootstrap files
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"""Build the system prompt from identity, bootstrap files, memory, and skills."""
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parts = [self._get_identity()]
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bootstrap = self._load_bootstrap_files()
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if bootstrap:
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parts.append(bootstrap)
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# Memory context
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memory = self.memory.get_memory_context()
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if memory:
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parts.append(f"# Memory\n\n{memory}")
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# Skills - progressive loading
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# 1. Always-loaded skills: include full content
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always_skills = self.skills.get_always_skills()
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if always_skills:
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always_content = self.skills.load_skills_for_context(always_skills)
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if always_content:
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parts.append(f"# Active Skills\n\n{always_content}")
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# 2. Available skills: only show summary (agent uses read_file to load)
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skills_summary = self.skills.build_skills_summary()
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if skills_summary:
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parts.append(f"""# Skills
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@@ -70,7 +49,7 @@ The following skills extend your capabilities. To use a skill, read its SKILL.md
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Skills with available="false" need dependencies installed first - you can try installing them with apt/brew.
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{skills_summary}""")
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return "\n\n---\n\n".join(parts)
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def _get_identity(self) -> str:
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@@ -81,29 +60,25 @@ Skills with available="false" need dependencies installed first - you can try in
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return f"""# nanobot 🐈
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You are nanobot, a helpful AI assistant.
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You are nanobot, a helpful AI assistant.
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## Runtime
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{runtime}
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## Workspace
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Your workspace is at: {workspace_path}
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- Long-term memory: {workspace_path}/memory/MEMORY.md
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- Long-term memory: {workspace_path}/memory/MEMORY.md (write important facts here)
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- History log: {workspace_path}/memory/HISTORY.md (grep-searchable)
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- Custom skills: {workspace_path}/skills/{{skill-name}}/SKILL.md
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Reply directly with text for conversations. Only use the 'message' tool to send to a specific chat channel.
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## Tool Call Guidelines
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- Before calling tools, you may briefly state your intent (e.g. "Let me check that"), but NEVER predict or describe the expected result before receiving it.
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- Before modifying a file, read it first to confirm its current content.
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- Do not assume a file or directory exists — use list_dir or read_file to verify.
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## nanobot Guidelines
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- State intent before tool calls, but NEVER predict or claim results before receiving them.
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- Before modifying a file, read it first. Do not assume files or directories exist.
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- After writing or editing a file, re-read it if accuracy matters.
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- If a tool call fails, analyze the error before retrying with a different approach.
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- Ask for clarification when the request is ambiguous.
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## Memory
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- Remember important facts: write to {workspace_path}/memory/MEMORY.md
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- Recall past events: grep {workspace_path}/memory/HISTORY.md"""
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Reply directly with text for conversations. Only use the 'message' tool to send to a specific chat channel."""
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@staticmethod
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def _build_runtime_context(channel: str | None, chat_id: str | None) -> str:
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@@ -136,37 +111,13 @@ Reply directly with text for conversations. Only use the 'message' tool to send
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channel: str | None = None,
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chat_id: str | None = None,
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) -> list[dict[str, Any]]:
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"""
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Build the complete message list for an LLM call.
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Args:
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history: Previous conversation messages.
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current_message: The new user message.
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skill_names: Optional skills to include.
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media: Optional list of local file paths for images/media.
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channel: Current channel (telegram, feishu, etc.).
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chat_id: Current chat/user ID.
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Returns:
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List of messages including system prompt.
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"""
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messages = []
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# System prompt
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system_prompt = self.build_system_prompt(skill_names)
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messages.append({"role": "system", "content": system_prompt})
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# History
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messages.extend(history)
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# Inject runtime metadata as a separate user message before the actual user message.
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messages.append({"role": "user", "content": self._build_runtime_context(channel, chat_id)})
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# Current user message
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user_content = self._build_user_content(current_message, media)
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messages.append({"role": "user", "content": user_content})
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return messages
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"""Build the complete message list for an LLM call."""
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return [
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{"role": "system", "content": self.build_system_prompt(skill_names)},
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*history,
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{"role": "user", "content": self._build_runtime_context(channel, chat_id)},
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{"role": "user", "content": self._build_user_content(current_message, media)},
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]
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def _build_user_content(self, text: str, media: list[str] | None) -> str | list[dict[str, Any]]:
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"""Build user message content with optional base64-encoded images."""
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@@ -187,63 +138,24 @@ Reply directly with text for conversations. Only use the 'message' tool to send
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return images + [{"type": "text", "text": text}]
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def add_tool_result(
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self,
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messages: list[dict[str, Any]],
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tool_call_id: str,
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tool_name: str,
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result: str
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self, messages: list[dict[str, Any]],
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tool_call_id: str, tool_name: str, result: str,
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) -> list[dict[str, Any]]:
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"""
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Add a tool result to the message list.
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Args:
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messages: Current message list.
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tool_call_id: ID of the tool call.
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tool_name: Name of the tool.
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result: Tool execution result.
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Returns:
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Updated message list.
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"""
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messages.append({
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"role": "tool",
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"tool_call_id": tool_call_id,
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"name": tool_name,
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"content": result
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})
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"""Add a tool result to the message list."""
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messages.append({"role": "tool", "tool_call_id": tool_call_id, "name": tool_name, "content": result})
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return messages
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def add_assistant_message(
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self,
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messages: list[dict[str, Any]],
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self, messages: list[dict[str, Any]],
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content: str | None,
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tool_calls: list[dict[str, Any]] | None = None,
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reasoning_content: str | None = None,
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) -> list[dict[str, Any]]:
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"""
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Add an assistant message to the message list.
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Args:
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messages: Current message list.
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content: Message content.
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tool_calls: Optional tool calls.
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reasoning_content: Thinking output (Kimi, DeepSeek-R1, etc.).
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Returns:
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Updated message list.
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"""
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msg: dict[str, Any] = {"role": "assistant"}
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# Always include content — some providers (e.g. StepFun) reject
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# assistant messages that omit the key entirely.
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msg["content"] = content
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"""Add an assistant message to the message list."""
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msg: dict[str, Any] = {"role": "assistant", "content": content}
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if tool_calls:
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msg["tool_calls"] = tool_calls
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# Include reasoning content when provided (required by some thinking models)
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if reasoning_content is not None:
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msg["reasoning_content"] = reasoning_content
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messages.append(msg)
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return messages
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