docs: make English the default docs language and fix link paths

docs/intro/architecture.mdx

@@ -1,41 +1,41 @@
 ---
-title: 项目架构
-description: CowAgent 2.0 的系统架构和核心设计
+title: Architecture
+description: CowAgent 2.0 system architecture and core design
 ---
 
-CowAgent 2.0 从简单的聊天机器人全面升级为超级智能助理，采用 Agent 架构设计，具备自主思考、规划任务、长期记忆和技能扩展等能力。
+CowAgent 2.0 has evolved from a simple chatbot into a super intelligent assistant with Agent architecture, featuring autonomous thinking, task planning, long-term memory, and skill extensibility.
 
-## 系统架构
+## System Architecture
 
-CowAgent 的整体架构由以下核心模块组成：
+CowAgent's architecture consists of the following core modules:
 
-<img src="https://cdn.jsdelivr.net/gh/zhayujie/cowagent-assets@main/architecture/zh/architecture.jpg" alt="CowAgent Architecture" />
+<img src="https://cdn.jsdelivr.net/gh/zhayujie/cowagent-assets@main/architecture/en/architecture.jpg" alt="CowAgent Architecture" />
 
-| 模块 | 说明 |
+| Module | Description |
 | --- | --- |
-| **Plan** | 理解用户意图，将复杂任务分解为多步骤计划，循环调用工具直到完成目标 |
-| **Memory** | 自动将重要信息持久化为核心记忆和日级记忆，支持关键词和向量混合检索，跨会话保持上下文连续性 |
-| **Knowledge** | 以主题维度组织结构化知识，Agent 自主整理有价值信息为 Markdown 页面，维护索引和交叉引用，构建持续增长的知识网络 |
-| **Tools** | Agent 访问操作系统资源的核心能力，内置文件读写、终端执行、浏览器操作、定时调度、记忆检索、联网搜索等 10+ 种工具 |
-| **Skills** | 加载和管理 Skills，支持从 Skill Hub、GitHub 等一键安装，或通过对话创建自定义技能 |
-| **Models** | 模型层，统一接入 OpenAI、Claude、Gemini、DeepSeek、MiniMax、GLM、Qwen 等国内外主流大语言模型 |
-| **Channels** | 消息通道层，负责接收和发送消息，支持 Web 控制台、微信、飞书、钉钉、企微、公众号等，统一消息协议 |
-| **CLI** | 命令行系统，提供终端命令（`cow`）和对话命令（`/`），支持进程管理、技能安装、配置修改、知识库管理等操作 |
+| **Plan** | Understands user intent, decomposes complex tasks into multi-step plans, and iteratively invokes tools until the goal is achieved |
+| **Memory** | Automatically persists important information as core memory and daily memory, with hybrid keyword and vector retrieval for cross-session context continuity |
+| **Knowledge** | Organizes structured knowledge by topic. The Agent autonomously distills valuable information into Markdown pages, maintaining indexes and cross-references to build a growing knowledge network |
+| **Tools** | Core capability for Agent to access OS resources. 10+ built-in tools including file read/write, terminal, browser, scheduler, memory search, web search, and more |
+| **Skills** | Loads and manages Skills. Supports one-click installation from Skill Hub, GitHub, and more, or custom skill creation through conversation |
+| **Models** | Model layer with unified access to OpenAI, Claude, Gemini, DeepSeek, MiniMax, GLM, Qwen, and other mainstream LLMs |
+| **Channels** | Message channel layer for receiving and sending messages. Supports Web console, WeChat, Feishu, DingTalk, WeCom, WeChat Official Account, and more with a unified protocol |
+| **CLI** | Command-line system providing terminal commands (`cow`) and chat commands (`/`) for process management, skill installation, configuration, knowledge base management, and more |
 
-## Agent 模式
+## Agent Mode Workflow
 
-启用 Agent 模式后，CowAgent 会以自主智能体的方式运行，核心工作流如下：
+When Agent mode is enabled, CowAgent runs as an autonomous agent with the following workflow:
 
-1. **接收消息** — 通过通道接收用户输入
-2. **理解意图** — 分析任务需求和上下文
-3. **规划任务** — 将复杂任务分解为多个步骤
-4. **调用工具** — 选择合适的工具执行每个步骤
-5. **记忆与知识更新** — 将重要信息存入长期记忆，将结构化知识整理至知识库
-6. **返回结果** — 将执行结果发送回用户
+1. **Receive Message** — Receive user input through channels
+2. **Understand Intent** — Analyze task requirements and context
+3. **Plan Task** — Break complex tasks into multiple steps
+4. **Invoke Tools** — Select and execute appropriate tools for each step
+5. **Update Memory & Knowledge** — Store important information in long-term memory and organize structured knowledge into the knowledge base
+6. **Return Result** — Send execution results back to the user
 
-## 工作空间
+## Workspace Directory Structure
 
-Agent 的工作空间默认位于 `~/cow` 目录，用于存储系统提示词、记忆文件、技能文件等：
+The Agent workspace is located at `~/cow` by default and stores system prompts, memory files, and skill files:
 
 ```
 ~/cow/
@@ -52,36 +52,36 @@ Agent 的工作空间默认位于 `~/cow` 目录，用于存储系统提示词
     └── skill-2/
 ```
 
-秘钥文件单独存储在 `~/.cow` 目录（出于安全考虑）：
+Secret keys are stored separately in `~/.cow` directory for security:
 
 ```
 ~/.cow/
 └── .env               # Secret keys for skills
 ```
 
-## 核心配置
+## Core Configuration
 
-在 `config.json` 中配置 Agent 模式的核心参数：
+Configure Agent mode parameters in `config.json`:
 
 ```json
 {
   "agent": true,
   "agent_workspace": "~/cow",
-  "agent_max_context_tokens": 40000,
-  "agent_max_context_turns": 30,
-  "agent_max_steps": 15,
+  "agent_max_context_tokens": 50000,
+  "agent_max_context_turns": 20,
+  "agent_max_steps": 20,
   "enable_thinking": false,
   "cow_lang": "auto"
 }
 ```
 
-| 参数 | 说明 | 默认值 |
+| Parameter | Description | Default |
 | --- | --- | --- |
-| `agent` | 是否启用 Agent 模式 | `true` |
-| `agent_workspace` | 工作空间路径 | `~/cow` |
-| `agent_max_context_tokens` | 最大上下文 token 数 | `50000` |
-| `agent_max_context_turns` | 最大上下文记忆轮次 | `20` |
-| `agent_max_steps` | 单次任务最大决策步数 | `20` |
-| `enable_thinking` | 是否启用深度思考模式 | `false` |
-| `knowledge` | 是否启用个人知识库 | `true` |
-| `cow_lang` | 界面、命令文案、系统提示词等的语言，`auto` 自动检测，可设为 `zh` / `en` | `auto` |
+| `agent` | Enable Agent mode | `true` |
+| `agent_workspace` | Workspace path | `~/cow` |
+| `agent_max_context_tokens` | Max context tokens | `50000` |
+| `agent_max_context_turns` | Max context turns | `20` |
+| `agent_max_steps` | Max decision steps per task | `20` |
+| `enable_thinking` | Enable deep-thinking mode | `false` |
+| `knowledge` | Enable personal knowledge base | `true` |
+| `cow_lang` | Language for the UI, command text and system prompts; `auto` to detect, or set `zh` / `en` | `auto` |