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https://github.com/AstrBotDevs/AstrBot
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fix(docs):多份文档汉译英并整理 (#8001)
* docs(en): translate plugin-platform-adapter.md from Chinese to English * docs(en): translate plugin-platform-adapter.md from Chinese to English * Update ppio.md * Update provider-lmstudio.md * Update function-calling.md * Update skills.md * Update ai.md * Update simple.md * Update mcp.md * Update config.mjs kook * fix(docs): fix MessageSesion import path in platform adapter example Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> --------- Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
This commit is contained in:
@@ -343,6 +343,7 @@ export default defineConfig({
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{ text: "Mattermost", link: "/mattermost" },
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{ text: "Misskey", link: "/misskey" },
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{ text: "Discord", link: "/discord" },
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{ text: "KOOK", link: "/kook" },
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{
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text: "Satori",
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base: "/en/platform/satori",
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@@ -357,7 +358,6 @@ export default defineConfig({
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collapsed: false,
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items: [
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{ text: "Matrix", link: "/matrix" },
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{ text: "KOOK", link: "/kook" },
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{ text: "VoceChat", link: "/vocechat" },
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],
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},
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@@ -2,23 +2,23 @@
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outline: deep
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---
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# 开发一个平台适配器
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# Developing a Platform Adapter
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AstrBot 支持以插件的形式接入平台适配器,你可以自行接入 AstrBot 没有的平台。如飞书、钉钉甚至是哔哩哔哩私信、Minecraft。
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AstrBot supports integrating platform adapters in plugin form, allowing you to connect platforms that AstrBot does not natively support — such as Lark, DingTalk, Bilibili private messages, or even Minecraft.
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我们以一个平台 `FakePlatform` 为例展开讲解。
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We will use a platform called `FakePlatform` as an example.
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首先,在插件目录下新增 `fake_platform_adapter.py` 和 `fake_platform_event.py` 文件。前者主要是平台适配器的实现,后者是平台事件的定义。
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First, add `fake_platform_adapter.py` and `fake_platform_event.py` to your plugin directory. The former handles the platform adapter implementation, while the latter defines the platform event.
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## 平台适配器
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## Platform Adapter
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假设 FakePlatform 的客户端 SDK 是这样:
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Assume FakePlatform's client SDK looks like this:
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```py
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import asyncio
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class FakeClient():
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'''模拟一个消息平台,这里 5 秒钟下发一个消息'''
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'''Simulates a messaging platform that sends a message every 5 seconds'''
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def __init__(self, token: str, username: str):
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self.token = token
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self.username = username
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@@ -29,7 +29,7 @@ class FakeClient():
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await asyncio.sleep(5)
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await getattr(self, 'on_message_received')({
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'bot_id': '123',
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'content': '新消息',
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'content': 'new message',
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'username': 'zhangsan',
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'userid': '123',
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'message_id': 'asdhoashd',
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@@ -37,28 +37,28 @@ class FakeClient():
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})
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async def send_text(self, to: str, message: str):
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print('发了消息:', to, message)
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print('Message sent:', to, message)
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async def send_image(self, to: str, image_path: str):
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print('发了消息:', to, image_path)
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print('Image sent:', to, image_path)
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```
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我们创建 `fake_platform_adapter.py`:
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Now create `fake_platform_adapter.py`:
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```py
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import asyncio
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from astrbot.api.platform import Platform, AstrBotMessage, MessageMember, PlatformMetadata, MessageType
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from astrbot.api.event import MessageChain
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from astrbot.api.message_components import Plain, Image, Record # 消息链中的组件,可以根据需要导入
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from astrbot.core.platform.astr_message_event import MessageSesion
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from astrbot.api.message_components import Plain, Image, Record # Message chain components, import as needed
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from astrbot.core.platform.message_session import MessageSesion
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from astrbot.api.platform import register_platform_adapter
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from astrbot import logger
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from .client import FakeClient
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from .fake_platform_event import FakePlatformEvent
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# 注册平台适配器。第一个参数为平台名,第二个为描述。第三个为默认配置。
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@register_platform_adapter("fake", "fake 适配器", default_config_tmpl={
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# Register the platform adapter. First param: platform name, second: description, third: default config.
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@register_platform_adapter("fake", "fake adapter", default_config_tmpl={
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"token": "your_token",
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"username": "bot_username"
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})
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@@ -66,52 +66,53 @@ class FakePlatformAdapter(Platform):
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def __init__(self, platform_config: dict, platform_settings: dict, event_queue: asyncio.Queue) -> None:
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super().__init__(event_queue)
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self.config = platform_config # 上面的默认配置,用户填写后会传到这里
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self.settings = platform_settings # platform_settings 平台设置。
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self.config = platform_config # The default config above; filled in by the user and passed here
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self.settings = platform_settings # platform_settings: platform settings
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async def send_by_session(self, session: MessageSesion, message_chain: MessageChain):
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# 必须实现
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# Must be implemented
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await super().send_by_session(session, message_chain)
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def meta(self) -> PlatformMetadata:
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# 必须实现,直接像下面一样返回即可。
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# Must be implemented. Simply return as shown below.
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return PlatformMetadata(
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"fake",
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"fake 适配器",
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"fake adapter",
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)
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async def run(self):
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# 必须实现,这里是主要逻辑。
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# Must be implemented. This is the main logic.
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# FakeClient 是我们自己定义的,这里只是示例。这个是其回调函数
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# FakeClient is defined by us — this is just an example. This is its callback function.
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async def on_received(data):
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logger.info(data)
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abm = await self.convert_message(data=data) # 转换成 AstrBotMessage
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abm = await self.convert_message(data=data) # Convert to AstrBotMessage
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await self.handle_msg(abm)
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# 初始化 FakeClient
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# Initialize FakeClient
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self.client = FakeClient(self.config['token'], self.config['username'])
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self.client.on_message_received = on_received
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await self.client.start_polling() # 持续监听消息,这是个堵塞方法。
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await self.client.start_polling() # Continuously listens for messages; this is a blocking call.
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async def convert_message(self, data: dict) -> AstrBotMessage:
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# 将平台消息转换成 AstrBotMessage
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# 这里就体现了适配程度,不同平台的消息结构不一样,这里需要根据实际情况进行转换。
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# Convert the platform message to AstrBotMessage.
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# The degree of adaptation is reflected here. Different platforms have different message
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# structures; convert accordingly.
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abm = AstrBotMessage()
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abm.type = MessageType.GROUP_MESSAGE # 还有 friend_message,对应私聊。具体平台具体分析。重要!
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abm.group_id = data['group_id'] # 如果是私聊,这里可以不填
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abm.message_str = data['content'] # 纯文本消息。重要!
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abm.sender = MessageMember(user_id=data['userid'], nickname=data['username']) # 发送者。重要!
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abm.message = [Plain(text=data['content'])] # 消息链。如果有其他类型的消息,直接 append 即可。重要!
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abm.raw_message = data # 原始消息。
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abm.type = MessageType.GROUP_MESSAGE # Also friend_message for private chats. Analyze per platform. Important!
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abm.group_id = data['group_id'] # Can be omitted for private chats
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abm.message_str = data['content'] # Plain text message. Important!
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abm.sender = MessageMember(user_id=data['userid'], nickname=data['username']) # Sender. Important!
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abm.message = [Plain(text=data['content'])] # Message chain. Append other message types as needed. Important!
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abm.raw_message = data # Raw message.
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abm.self_id = data['bot_id']
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abm.session_id = data['userid'] # 会话 ID。重要!
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abm.message_id = data['message_id'] # 消息 ID。
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abm.session_id = data['userid'] # Session ID. Important!
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abm.message_id = data['message_id'] # Message ID.
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return abm
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async def handle_msg(self, message: AstrBotMessage):
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# 处理消息
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# Handle the message
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message_event = FakePlatformEvent(
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message_str=message.message_str,
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message_obj=message,
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@@ -119,11 +120,11 @@ class FakePlatformAdapter(Platform):
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session_id=message.session_id,
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client=self.client
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)
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self.commit_event(message_event) # 提交事件到事件队列。不要忘记!
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self.commit_event(message_event) # Submit the event to the event queue. Don't forget this!
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```
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`fake_platform_event.py`:
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`fake_platform_event.py`:
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```py
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from astrbot.api.event import AstrMessageEvent, MessageChain
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@@ -138,13 +139,13 @@ class FakePlatformEvent(AstrMessageEvent):
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self.client = client
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async def send(self, message: MessageChain):
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for i in message.chain: # 遍历消息链
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if isinstance(i, Plain): # 如果是文字类型的
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for i in message.chain: # Iterate over the message chain
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if isinstance(i, Plain): # If it's a text message
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await self.client.send_text(to=self.get_sender_id(), message=i.text)
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elif isinstance(i, Image): # 如果是图片类型的
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elif isinstance(i, Image): # If it's an image
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img_url = i.file
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img_path = ""
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# 下面的三个条件可以直接参考一下。
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# The three conditions below can be used as a reference.
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if img_url.startswith("file:///"):
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img_path = img_url[8:]
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elif i.file and i.file.startswith("http"):
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@@ -152,14 +153,14 @@ class FakePlatformEvent(AstrMessageEvent):
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else:
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img_path = img_url
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# 请善于 Debug!
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# Make good use of debugging!
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await self.client.send_image(to=self.get_sender_id(), image_path=img_path)
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await super().send(message) # 需要最后加上这一段,执行父类的 send 方法。
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await super().send(message) # Must be called at the end to invoke the parent class's send method.
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```
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最后,main.py 只需这样,在初始化的时候导入 fake_platform_adapter 模块。装饰器会自动注册。
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Finally, in `main.py`, simply import the `fake_platform_adapter` module during initialization. The decorator will handle registration automatically.
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```py
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from astrbot.api.star import Context, Star
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@@ -169,17 +170,17 @@ class MyPlugin(Star):
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from .fake_platform_adapter import FakePlatformAdapter # noqa
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```
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搞好后,运行 AstrBot:
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Once set up, run AstrBot:
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这里出现了我们创建的 fake。
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The `fake` adapter we created now appears here.
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启动后,可以看到正常工作:
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After starting, you can see it working correctly:
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有任何疑问欢迎加群询问~
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If you have any questions, feel free to join the community group and ask~
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@@ -257,7 +257,7 @@ curr_cid = await conv_mgr.get_curr_conversation_id(uid)
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conversation = await conv_mgr.get_conversation(uid, curr_cid) # Conversation
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```
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::: details Conversation 类型定义
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::: details Conversation Type Definition
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```py
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@dataclass
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@@ -438,7 +438,7 @@ persona_mgr = self.context.persona_manager
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- **Returns**
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`Personality` – Default persona object in v3 format
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::: details Persona / Personality 类型定义
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::: details Persona / Personality Type Definition
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```py
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@@ -40,19 +40,3 @@ Explanation:
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All handler functions must be written within the plugin class. To keep content concise, in subsequent sections, we may omit the plugin class definition.
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```
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解释如下:
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- 插件需要继承 `Star` 类。
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- `Context` 类用于插件与 AstrBot Core 交互,可以由此调用 AstrBot Core 提供的各种 API。
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- 具体的处理函数 `Handler` 在插件类中定义,如这里的 `helloworld` 函数。
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- `AstrMessageEvent` 是 AstrBot 的消息事件对象,存储了消息发送者、消息内容等信息。
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- `AstrBotMessage` 是 AstrBot 的消息对象,存储了消息平台下发的消息的具体内容。可以通过 `event.message_obj` 获取。
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> [!TIP]
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>
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> `Handler` 一定需要在插件类中注册,前两个参数必须为 `self` 和 `event`。如果文件行数过长,可以将服务写在外部,然后在 `Handler` 中调用。
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>
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> 插件类所在的文件名需要命名为 `main.py`。
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所有的处理函数都需写在插件类中。为了精简内容,在之后的章节中,我们可能会忽略插件类的定义。
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@@ -1,21 +1,21 @@
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# 接入 302.AI
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# Connect 302.AI
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302.AI 是企业级 AI 应用平台,支持快捷接入全球各类 AI 模型。
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[302.AI](https://302.ai) is an enterprise-grade AI application platform that provides quick access to a wide range of AI models worldwide.
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## 使用
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## Getting Started
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||||
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||||
点击[此链接](https://share.302.ai/rr1M3l) 注册账户。
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||||
Click [this link](https://share.302.ai/rr1M3l) to register an account.
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||||
|
||||
注册完毕之后,点击[此链接](https://302.ai/apis/)选择需要接入的模型。
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After registering, click [this link](https://302.ai/apis/) to select the model you want to use.
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||||
根据需求,进入[此链接](https://dash.302.ai/charge) 充值对应的金额。
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If needed, visit [this link](https://dash.302.ai/charge) to top up your account balance.
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## 接入
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## Connect
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||||
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打开 AstrBot 控制台 -> 服务提供商页面,点击新增提供商,找到并点击 `302.AI`(需要版本 >= 3.5.18)
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Open the AstrBot dashboard → Service Providers page, click **Add Provider**, find and click `302.AI` (requires version >= 3.5.18).
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修改 ID,并将 API Key 和模型名称填入对话框表单,点击保存,即可完成创建。
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Set an ID, fill in the API Key and model name in the dialog form, then click **Save** to complete the setup.
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## 使用
|
||||
## Usage
|
||||
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||||
对机器人输入 `/provider` 指令,将提供商切换到刚刚添加的 302.AI 提供商,即可使用。
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||||
Send the `/provider` command to the bot to switch to the 302.AI provider you just added.
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||||
@@ -1,43 +1,41 @@
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||||
# 接入 PPIO 派欧云
|
||||
# Connect PPIO Cloud
|
||||
|
||||
PPIO 派欧云是中国领先的独立分布式云计算服务商,您可以在派欧云上使用稳定、低价甚至免费的模型服务。
|
||||
PPIO Cloud is a leading independent distributed cloud computing provider in China, offering stable, affordable, and even free model services.
|
||||
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||||
## 准备
|
||||
## Preparation
|
||||
|
||||
打开 [PPIO 派欧云官网](https://ppio.cn/user/register?invited_by=AIOONE),并注册账户(通过此链接注册的账户将会获得 15 元人民币的代金券)。
|
||||
Open the [PPIO Cloud website](https://ppio.cn/user/register?invited_by=AIOONE) and register an account (accounts registered through this link will receive a ¥15 voucher).
|
||||
|
||||
进入 [模型 API 服务](https://ppio.cn/model-api/console),找到你想接入的模型。你可以通过筛选器选择不同厂商或者免费的模型。
|
||||
Go to [Model API Service](https://ppio.cn/model-api/console) and find the model you want to use. You can filter by provider or select free models.
|
||||
|
||||

|
||||
|
||||
找到你想要接入的模型后,点击模型卡片,侧边会展开一个模型详情卡片,找到下方的 API 接入指南,如果您还没创建过 Key 可以点击创建。
|
||||
Once you find the model, click its card to expand a detail panel on the right. Scroll down to the API integration guide — if you haven't created a key yet, click to create one.
|
||||
|
||||

|
||||
|
||||
打开 AstrBot 控制台 -> 服务提供商页面,点击新增提供商,找到并点击 `PPIO派欧云`(需要版本 >= 3.5.10,旧版本也可使用,见下文)。
|
||||
Open the AstrBot dashboard → Service Providers page, click **Add Provider**, find and click `PPIO Cloud` (requires version >= 3.5.10; older versions are also supported, see below).
|
||||
|
||||

|
||||
|
||||
将 API Key 和模型名称填入对话框表单,点击保存,即可完成创建。
|
||||
Fill in the API Key and model name in the dialog form, then click **Save** to complete the setup.
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||||
|
||||
> [!TIP]
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||||
> 如果您是 AstrBot 旧版本(< 3.5.10)的用户,请打开 AstrBot 控制台 -> 服务提供商页面,点击新增提供商,找到 `OpenAI`,点击进入。
|
||||
> 1. 将 ID 命名为 `ppio`(随意)
|
||||
> 2. 然后将 `API Base URL` 设置为 `https://api.ppinfra.com/v3/openai`
|
||||
> 3. 然后将 API Key 和模型名称填入对话框表单,点击保存,即可完成创建。
|
||||
> If you are using an older version of AstrBot (< 3.5.10), open the AstrBot dashboard → Service Providers page, click **Add Provider**, find `OpenAI`, and click to enter.
|
||||
> 1. Set the ID to `ppio` (any name works)
|
||||
> 2. Set `API Base URL` to `https://api.ppinfra.com/v3/openai`
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||||
> 3. Fill in the API Key and model name in the dialog form, then click **Save** to complete the setup.
|
||||
|
||||
## Usage
|
||||
|
||||
## 使用
|
||||
Send the `/provider` command to the bot to switch to the PPIO Cloud provider you just added.
|
||||
|
||||
对机器人输入 `/provider` 指令,将提供商切换到刚刚添加的 PPIO 派欧云提供商,即可使用。
|
||||
## FAQ
|
||||
|
||||
## 常见问题
|
||||
|
||||
#### 显示 `400` 错误
|
||||
#### `400` Error
|
||||
|
||||
```log
|
||||
Error code: 400 - {'code': 400, 'message': '"auto" tool choice requires --enable-auto-tool-choice and --tool-call-parser to be set', 'type': 'BadRequestError'}
|
||||
```
|
||||
|
||||
|
||||
请暂时使用 `/tool off_all` 禁用所有的函数调用工具即可使用,或者换用其他模型。
|
||||
Temporarily disable all function calling tools with `/tool off_all`, or switch to a different model.
|
||||
|
||||
@@ -1,38 +1,37 @@
|
||||
# 接入 LM Studio 使用 DeepSeek-R1 等模型
|
||||
# Connect LM Studio to Use DeepSeek-R1 and Other Models
|
||||
|
||||
LMStudio 允许在本地电脑上部署模型(需要电脑硬件配置符合要求)
|
||||
LM Studio allows you to deploy models locally on your computer (hardware requirements must be met).
|
||||
|
||||
### 下载并安装 LMStudio
|
||||
### Download and Install LM Studio
|
||||
|
||||
https://lmstudio.ai/download
|
||||
<https://lmstudio.ai/download>
|
||||
|
||||
### 下载并运行模型
|
||||
### Download and Run a Model
|
||||
|
||||
https://lmstudio.ai/models
|
||||
<https://lmstudio.ai/models>
|
||||
|
||||
跟随 LMStudio 下载并运行想要的模型,如 deepseek-r1-qwen-7b:
|
||||
Follow the LM Studio instructions to download and run your desired model, e.g. `deepseek-r1-qwen-7b`:
|
||||
|
||||
```bash
|
||||
lms get deepseek-r1-qwen-7b
|
||||
```
|
||||
|
||||
### 配置 AstrBot
|
||||
### Configure AstrBot
|
||||
|
||||
在 AstrBot 上:
|
||||
In AstrBot:
|
||||
|
||||
点击 配置->服务提供商配置->加号->openai
|
||||
Go to **Configuration → Service Providers → + → OpenAI**
|
||||
|
||||
API Base URL 填写 `http://localhost:1234/v1`
|
||||
Set `API Base URL` to `http://localhost:1234/v1`
|
||||
|
||||
API Key 填写 `lm-studio`
|
||||
Set `API Key` to `lm-studio`
|
||||
|
||||
> 对于 Mac/Windows 使用 Docker Desktop 部署 AstrBot 部署的用户,API Base URL 请填写为 `http://host.docker.internal:1234/v1`。
|
||||
> 对于 Linux 使用 Docker 部署 AstrBot 部署的用户,API Base URL 请填写为 `http://172.17.0.1:1234/v1`,或者将 `172.17.0.1` 替换为你的公网 IP IP(确保宿主机系统放行了 1234 端口)。
|
||||
> For users deploying AstrBot via Docker Desktop on Mac or Windows, set `API Base URL` to `http://host.docker.internal:1234/v1`.
|
||||
>
|
||||
> For users deploying AstrBot via Docker on Linux, set `API Base URL` to `http://172.17.0.1:1234/v1`, or replace `172.17.0.1` with your server's public IP (make sure port 1234 is open on the host).
|
||||
|
||||
如果 LM Studio 使用了 Docker 部署,请确保 1234 端口已经映射到宿主机。
|
||||
If LM Studio itself is deployed in Docker, ensure port 1234 is mapped to the host.
|
||||
|
||||
模型名填写上一步选好的
|
||||
Set the model name to the one you selected in the previous step, then save the configuration.
|
||||
|
||||
保存配置即可。
|
||||
|
||||
> 输入 /provider 查看 AstrBot 配置的模型
|
||||
> Run `/provider` to view the models configured in AstrBot.
|
||||
|
||||
@@ -51,4 +51,4 @@ Below are some common tool calling demos:
|
||||
|
||||
## MCP
|
||||
|
||||
Please refer to this documentation: [AstrBot - MCP](/use/mcp).
|
||||
Please refer to this documentation: [AstrBot - MCP](/en/use/mcp).
|
||||
|
||||
@@ -99,4 +99,4 @@ That's it.
|
||||
Reference links:
|
||||
|
||||
1. Learn how to use MCP here: [Model Context Protocol](https://modelcontextprotocol.io/introduction)
|
||||
2. Get commonly used MCP servers here: [awesome-mcp-servers](https://github.com/punkpeye/awesome-mcp-servers/blob/main/README-zh.md#what-is-mcp), [Model Context Protocol servers](https://github.com/modelcontextprotocol/servers), [MCP.so](https://mcp.so)
|
||||
2. Get commonly used MCP servers here: [awesome-mcp-servers](https://github.com/punkpeye/awesome-mcp-servers/blob/main/README.md#what-is-mcp), [Model Context Protocol servers](https://github.com/modelcontextprotocol/servers), [MCP.so](https://mcp.so)
|
||||
|
||||
@@ -20,7 +20,7 @@ You can upload Skills with the following requirements:
|
||||
|
||||
1. The upload must be a `.zip` archive.
|
||||
2. **After extraction, it must contain a single Skill folder. The folder name will be used as the identifier for the Skill in AstrBot—please name it using English characters.**
|
||||
3. The Skill folder must include a file named `SKILL.md`, and its contents should preferably follow the Anthropic Skills specification. You can refer to Anthropic's documentation: https://code.claude.com/docs/zh-CN/skills
|
||||
3. The Skill folder must include a file named `SKILL.md`, and its contents should preferably follow the Anthropic Skills specification. You can refer to Anthropic's documentation: https://code.claude.com/docs/en/skills
|
||||
|
||||
## Using Skills in AstrBot
|
||||
|
||||
|
||||
Reference in New Issue
Block a user