diff --git a/README.md b/README.md
index 891bbd7..a9e02cf 100644
--- a/README.md
+++ b/README.md
@@ -1,10 +1,16 @@
+<!-- WEHUB_ZH_README -->
+> [!NOTE]
+> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。  
+> [English](./README.en.md) · [原始项目](https://github.com/microsoft/Webwright) · [上游 README](https://github.com/microsoft/Webwright/blob/HEAD/README.md)  
+> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
+
 # Webwright
 
 <p align="center">
   <img src="assets/webwright_logo.svg" alt="Webwright logo" width="320">
 </p>
 
-<p align="center"><b>Turn Your Coding Models to Be State-of-the-art Browser Agents</b></p>
+<p align="center"><b>让你的编程模型成为一流的浏览器智能体（Browser Agent）</b></p>
 
 <p align="center">
   <img src="https://img.shields.io/badge/python-%E2%89%A53.10-blue?logo=python&logoColor=white" alt="Python">
@@ -13,94 +19,94 @@
   <img src="https://img.shields.io/badge/footprint-%E2%89%A4~1.5k%20LoC-brightgreen" alt="Footprint">
 </p>
 
-- 📝 **Blog:** [Webwright: A Terminal Is All You Need For Web Agents](https://www.microsoft.com/en-us/research/articles/webwright-a-terminal-is-all-you-need-for-web-agents/)
-- 🌐 **Project Page:** [microsoft.github.io/Webwright](https://microsoft.github.io/Webwright/)
+- 📝 **Blog：** [Webwright: A Terminal Is All You Need For Web Agents](https://www.microsoft.com/en-us/research/articles/webwright-a-terminal-is-all-you-need-for-web-agents/)
+- 🌐 **项目主页：** [microsoft.github.io/Webwright](https://microsoft.github.io/Webwright/)
 
-Webwright gives LLM a terminal where it can launch multiple browser sessions to inspect the page and complete a web task. It captures and inspects page screenshots/states only when needed. It enforces each web task to be completed end-to-end within a re-runnable Python script, i.e. your web agent browsing history is a single code file. No multi-agent system, no graph engine, no plugin layer, no hidden orchestration — just a terminal, a browser, and a model.
+Webwright 为 LLM 提供一个终端，使其能够启动多个浏览器会话来检查页面并完成 Web 任务。它仅在需要时捕获并检查页面截图/状态。它要求每个 Web 任务都必须在可重复运行的 Python 脚本中端到端完成——也就是说，你的 Web 智能体浏览历史就是单个代码文件。没有多智能体系统、没有图引擎、没有插件层、没有隐藏的编排——只有终端、浏览器和模型。
 
-Already got your favorite agents, and wonder how to make Claude Code, Codex, Hermes, OpenClaw more capable in browser tasks? Consider adding [Webwright plugin/skills](#-use-as-a-claude-code-skill)!
+已有自己喜爱的智能体，想知道如何让 Claude Code、Codex、Hermes、OpenClaw 在浏览器任务中更强？可以考虑添加 [Webwright 插件/技能](#-use-as-a-claude-code-skill)！
 
 ---
 
-## 📰 News
+## 📰 新闻
 
-- **2026-05-11** — Support Task2UI mode: Webwright completes the task and renders task results into an HTML-based web app you can easily view and reuse.  
-- **2026-05-06** — Codex and Claude Code plugin manifests added; install via `/plugin install webwright@webwright`. OpenClaw and Hermes Agent integrations shipped; the same `skills/webwright/` folder now loads across Claude Code, Codex, OpenClaw, and Hermes.
-- **2026-05-04** — Initial public release: ~1.5k LoC, OpenAI / Anthropic / OpenRouter backends, Playwright environment.
+- **2026-05-11** — 支持 Task2UI 模式：Webwright 完成任务后将任务结果渲染为基于 HTML 的 Web 应用，便于查看和复用。  
+- **2026-05-06** — 新增 Codex 与 Claude Code 插件清单；可通过 `/plugin install webwright@webwright` 安装。已交付 OpenClaw 与 Hermes Agent 集成；同一 `skills/webwright/` 文件夹现可在 Claude Code、Codex、OpenClaw 和 Hermes 中加载。
+- **2026-05-04** — 首次公开发布：约 1.5k 行代码，支持 OpenAI / Anthropic / OpenRouter 后端与 Playwright 环境。
 
 ---
 
 <details>
-<summary><strong>💡 Motivation: Beyond Step-by-Step Web Interaction in a Stateful Browser</strong></summary>
+<summary><strong>💡 动机：超越有状态浏览器中的逐步 Web 交互</strong></summary>
 
-Most web agents today treat the browser session itself as the workspace: at each step the model receives the current page state and predicts a single next operation — a click, a type, a DOM selector, or a short tool call. Whatever the format, the agent is locked into predicting one web action at a time inside a predefined interaction loop. That harness was useful when LLMs were weaker. As models get stronger at writing and debugging code, the same harness becomes a bottleneck.
+当今大多数 Web 智能体将浏览器会话本身视为工作区：每一步模型接收当前页面状态并预测单个下一步操作——点击、输入、DOM 选择器或简短的工具调用。无论格式如何，智能体都被锁定在预定义交互循环内一次预测一个 Web 操作。当 LLM 较弱时，这种框架很有用；随着模型在编写和调试代码方面越来越强，同样的框架反而成为瓶颈。
 
-Webwright takes a different stance: **separate the agent from the browser**, and treat the browser as something the agent can launch, inspect, and discard while developing a program. The persistent artifact is not the browser session — it's the **code and logs in the local workspace**.
+Webwright 采取不同立场：**将智能体与浏览器分离**，把浏览器视为智能体在开发程序时可以启动、检查并丢弃的对象。持久化产物不是浏览器会话——而是**本地工作区中的代码和日志**。
 
-- 🧱 **Robust, reusable interaction with web environments** — instead of fragile pixel-level actions, a coding agent with a terminal queries elements, waits for conditions, and handles dynamic behaviors like lazy loading or re-rendering. The resulting scripts can be rerun, adapted, and shared across tasks rather than rediscovered from scratch.
-- ⚡ **Efficient composition of complex workflows** — multi-step interactions like selecting a date or filling a form become a compact program. Loops, functions, and abstractions let the agent generalize across similar tasks (e.g. different dates) without re-predicting the same low-level sequences. Fewer interaction rounds, faster execution, less error accumulation on long horizons.
-- 🧪 **Workspace-as-state, not browser-as-state** — the agent can write exploratory scripts, spawn fresh browser sessions, and decide for itself when to capture screenshots and inspect failures, much like a human engineer iterating on an RPA script.
-- 🪄 **Surprisingly effective despite being minimal** — this stripped-down setup turns out to handle complex and especially long-horizon web tasks well (see [Performance](#-performance)).
+- 🧱 **与 Web 环境进行稳健、可复用的交互** — 编程智能体通过终端查询元素、等待条件，并处理懒加载或重新渲染等动态行为，而非脆弱的像素级操作。生成的脚本可重新运行、适配并在任务间共享，而不必从头摸索。
+- ⚡ **高效组合复杂工作流** — 选择日期或填写表单等多步交互可变成紧凑程序。循环、函数和抽象让智能体在相似任务（如不同日期）间泛化，而无需重复预测相同的底层序列。更少交互轮次、更快执行、长程任务中更少错误累积。
+- 🧪 **工作区即状态，而非浏览器即状态** — 智能体可编写探索性脚本、启动全新浏览器会话，并自行决定何时捕获截图和检查失败，类似人类工程师迭代 RPA 脚本。
+- 🪄 **极简却出人意料地有效** — 这种精简配置在处理复杂且尤其长程的 Web 任务时表现良好（见[性能](#-performance)）。
 
 </details>
 
 ---
 
 <details>
-<summary><strong>🌟 Why Webwright</strong></summary>
+<summary><strong>🌟 为什么选择 Webwright</strong></summary>
 
-Most web agent frameworks bury the actual agent loop under layers of abstractions. Webwright takes the opposite stance:
+大多数 Web 智能体框架将实际智能体循环埋在层层抽象之下。Webwright 采取相反立场：
 
-- 🪶 **Lightweight by design** — core agent loop in a single ~450-line file, Playwright environment in ~570 lines, CLI in ~150 lines.
-- 🧩 **Pluggable model backends** — OpenAI, Anthropic, and OpenRouter, each ~150–200 lines.
-- 🔍 **Zero hidden frameworks** — just `httpx`, `pydantic`, `playwright`, and `typer`.
-- 🔁 **Flat prompt → observe → execute script loop** — readable end-to-end, easy to debug, easy to fork.
-- 🧪 **Run-artifact first** — every run writes trajectories and screenshots to disk for inspection.
+- 🪶 **设计上轻量** — 核心智能体循环在单个约 450 行的文件中，Playwright 环境约 570 行，CLI 约 150 行。
+- 🧩 **可插拔的模型后端** — OpenAI、Anthropic 和 OpenRouter，各约 150–200 行。
+- 🔍 **零隐藏框架** — 只有 `httpx`、`pydantic`、`playwright` 和 `typer`。
+- 🔁 **扁平的 prompt → observe → execute script 循环** — 端到端可读、易于调试、易于 fork。
+- 🧪 **运行产物优先** — 每次运行都将轨迹和截图写入磁盘以供检查。
 
-If you want a minimal, easy-to-debug starting point for browser-using agents instead of another heavyweight platform, this is it.
+如果你想要一个极简、易于调试的浏览器智能体起点，而不是又一个重量级平台，这就是它。
 
 </details>
 
 ---
 
 <details>
-<summary><strong>🆚 How Webwright Differs From Other Browser-Agent Repos</strong></summary>
+<summary><strong>🆚 Webwright 与其他浏览器智能体仓库的区别</strong></summary>
 
-How they differ at the architectural level:
+架构层面的差异：
 
 |                     | **Stagehand (Browserbase)**                                  | **agent-browser (Vercel)**                                                | **browser-use**                                       | **Webwright**                                                       |
 | ------------------- | ------------------------------------------------------------ | ------------------------------------------------------------------------- | ----------------------------------------------------- | ------------------------------------------------------------------------- |
-| **Paradigm**        | Hybrid: code + NL primitives (`act` / `extract` / `agent`)   | CLI tool that *another* agent (Claude Code, Codex, etc.) calls            | Autonomous LLM agent loop over DOM/AX snapshots       | **Coding agent with a terminal**; browser is just an environment it spawns |
-| **Action space**    | Playwright code, or NL → LLM-translated Playwright           | Discrete subcommands (`open`, `click @e2`, `snapshot`, `eval`)            | Indexed click/type actions selected by the LLM        | **Free-form Python (writes Playwright scripts itself)**                       |
-| **What is "state"?**| The browser session                                          | The browser session (held by daemon across CLI calls)                     | The browser session                                   | **The local workspace — code, screenshots, logs.** Browser is disposable. |
-| **Loop shape**      | Imperative; `agent()` does multi-step when needed            | One CLI invocation per micro-step                                         | observe → predict next action → execute → repeat      | write code → execute → inspect screenshots → repair (code-as-action)      |
+| **范式**        | 混合：代码 + 自然语言原语（`act` / `extract` / `agent`）   | 供*其他*智能体（Claude Code、Codex 等）调用的 CLI 工具            | 基于 DOM/AX 快照的自主 LLM 智能体循环       | **带终端的编程智能体**；浏览器只是其启动的环境 |
+| **动作空间**    | Playwright 代码，或自然语言 → LLM 翻译的 Playwright           | 离散子命令（`open`、`click @e2`、`snapshot`、`eval`）            | 由 LLM 选择的索引化点击/输入操作        | **自由形式 Python（自行编写 Playwright 脚本）**                       |
+| **何为「状态」？**| 浏览器会话                                          | 浏览器会话（由守护进程在多次 CLI 调用间保持）                     | 浏览器会话                                   | **本地工作区——代码、截图、日志。** 浏览器可丢弃。 |
+| **循环形态**      | 命令式；`agent()` 在需要时执行多步            | 每个微步骤一次 CLI 调用                                         | observe → predict next action → execute → repeat      | 编写代码 → 执行 → 检查截图 → 修复（code-as-action）      |
 </details>
 
 
 ---
 
-## 🎥 Demo
+## 🎥 演示
 https://github.com/user-attachments/assets/4ed94cd5-11be-4daa-b2d7-1260a803baca
 
 ---
 
-## 📊 Performance
+## 📊 性能
 
-State-of-the-art on two real-website benchmarks with a 100-step budget — see the [blog post](https://www.microsoft.com/en-us/research/articles/webwright-a-terminal-is-all-you-need-for-web-agents/) for full details.
+在两个真实网站基准测试上以 100 步预算达到一流水平——详见[博客文章](https://www.microsoft.com/en-us/research/articles/webwright-a-terminal-is-all-you-need-for-web-agents/)。
 
-- 🏆 **Online-Mind2Web (300 tasks):** **86.7%** with GPT-5.4 — highest among open-sourced harnesses in the AutoEval category. Claude Opus 4.7 reaches **84.7%**, and is stronger on the hard split (**80.5%** vs. 76.6% for GPT-5.4 at N=100).
-- 🚀 **Odysseys (200 long-horizon tasks):** **60.1%** with GPT-5.4 (avg. 76.1 steps) — **+15.6 points** over the prior SOTA (Opus 4.6 at 44.5%, using vision based approach and persistent browser) and **+26.6 points** over base GPT-5.4 (33.5% using xy-coordinate prediction and persistent browser).
-- 🧠 **Code-as-action beats coordinate prediction:** Webwright substantially outperforms a reproduced GPT-5.4 screenshot+xy-coordinate baseline across all difficulty splits.
-- 🧰 **Small models + reusable tools:** generated scripts can be packaged as parameterized CLI tools — even **Qwen-3.5-9B** completes tasks well on Online-Mind2Web sites with 5+ tools available.
+- 🏆 **Online-Mind2Web（300 项任务）：** 使用 GPT-5.4 达到 **86.7%** — AutoEval 类别中开源框架最高。Claude Opus 4.7 达到 **84.7%**，在困难子集上更强（**80.5%** vs. GPT-5.4 在 N=100 时的 76.6%）。
+- 🚀 **Odysseys（200 项长程任务）：** 使用 GPT-5.4（平均 76.1 步）达到 **60.1%** — 较先前 SOTA（Opus 4.6 为 44.5%，采用基于视觉的方法与持久化浏览器）**+15.6 个百分点**，较基础 GPT-5.4（33.5%，采用 xy 坐标预测与持久化浏览器）**+26.6 个百分点**。
+- 🧠 **Code-as-action 优于坐标预测：** Webwright 在所有难度子集上均大幅优于复现的 GPT-5.4 截图+xy 坐标基线。
+- 🧰 **小模型 + 可复用工具：** 生成的脚本可打包为参数化 CLI 工具 — 即便 **Qwen-3.5-9B** 在 Online-Mind2Web 站点上、有 5+ 个可用工具时也能很好地完成任务。
 
 <p align="center">
-  <img src="assets/odysseys_eval_step100.png" alt="Odysseys long-horizon eval @ 100 steps" width="49%">
+  <img src="assets/odysseys_eval_step100.png" alt="Odysseys 长视野评测 @ 100 steps" width="49%">
   <img src="assets/om2w_autoeval_step100.png" alt="Online-Mind2Web AutoEval @ 100 steps" width="49%">
 </p>
 
 ---
 
-## 🗺️ Project Map
+## 🗺️ 项目地图
 
 ```
 webwright/
@@ -124,23 +130,24 @@ webwright/
 
 ---
 
-## 📰 Task Showcase (repeatable runs as a dashboard)
+## 📰 任务展示（可重复运行，仪表板形式）
 
-A tiny Flask app under [`assets/task_showcase/`](assets/task_showcase/README.md) consolidates
-Webwright runs for **repeatable** odyssey tasks (deals, inventory, listings,
-job boards, weather, etc.) into a single dashboard. Each task ships only two
-files — `task.json` (metadata) and `report.json` (curated, structured output:
-sources + result sections like tables, lists, summaries) — and the templates
-render them generically, so adding a new task is just dropping a new folder
-in `assets/task_showcase/tasks/`.
+位于 [`assets/task_showcase/`](assets/task_showcase/README.md) 下的小型 Flask 应用将
+针对**可重复** odyssey 任务（优惠、库存、列表、
+招聘板、天气等）的 Webwright 运行结果汇总到
+单一仪表板中。每个任务仅包含两个
+文件 — `task.json`（元数据）和 `report.json`（精心整理的结构化输出：
+来源 + 表格、列表、摘要等结果区块）— 模板
+以通用方式渲染它们，因此添加新任务只需在
+`assets/task_showcase/tasks/` 中放入一个新文件夹即可。
 
 ```bash
 pip install flask
 python assets/task_showcase/app.py    # http://127.0.0.1:5005
 ```
 
-To have Webwright produce a renderer-ready task folder at runtime, stack the
-Task Showcase overlay:
+若要在运行时让 Webwright 生成可供渲染器使用的任务文件夹，请叠加
+Task Showcase 覆盖层：
 
 ```bash
 python -m webwright.run.cli \
@@ -150,13 +157,12 @@ python -m webwright.run.cli \
     -o outputs/default
 ```
 
-> **Note:** `report.json` is only generated when `-c task_showcase.yaml` is
-> included. A plain `base.yaml` run produces `trajectory.json` and debug
-> artifacts but no `report.json`.
+> **注意：** 仅当包含 `-c task_showcase.yaml` 时才会生成 `report.json`。
+> 普通的 `base.yaml` 运行会产生 `trajectory.json` 和调试
+> 产物，但不会生成 `report.json`。
 
-The run writes `task_showcase/tasks/<short_id>/task.json` and `report.json`
-inside the output workspace. Render those generated files without copying them
-back into the repo:
+运行会在输出工作区内写入 `task_showcase/tasks/<short_id>/task.json` 和 `report.json`。
+直接渲染这些生成的文件，无需将它们复制回仓库：
 
 ```bash
 python assets/task_showcase/app.py \
@@ -165,27 +171,27 @@ python assets/task_showcase/app.py \
 
 ---
 
-## 🚀 Quick Start
+## 🚀 快速开始
 
-### Prerequisites
+### 前置条件
 
 - Python 3.10+
-- Chromium installed through Playwright
-- An API key for your chosen backend (OpenAI, Anthropic, or OpenRouter)
+- 通过 Playwright 安装的 Chromium
+- 所选后端（OpenAI、Anthropic 或 OpenRouter）的 API 密钥
 
-### Install
+### 安装
 
 ```bash
 pip install -e .
 playwright install chromium
 ```
 
-### Run
+### 运行
 
-Export credentials for the configured backend (for example, `OPENAI_API_KEY`
-with `model_openai.yaml` or `ANTHROPIC_API_KEY` with `model_claude.yaml`). The
-`image_qa` and `self_reflection` tools use the same configured model by default,
-so an Anthropic run does not require an OpenAI key. Then:
+为已配置的后端导出凭据（例如，`OPENAI_API_KEY`
+配合 `model_openai.yaml`，或 `ANTHROPIC_API_KEY` 配合 `model_claude.yaml`）。默认情况下，
+`image_qa` 和 `self_reflection` 工具使用同一已配置模型，
+因此使用 Anthropic 运行无需 OpenAI 密钥。然后：
 
 ```bash
 python -m webwright.run.cli \
@@ -196,23 +202,23 @@ python -m webwright.run.cli \
     -o outputs/default
 ```
 
-### 🚩 Flags
+### 🚩 标志
 
-| Flag | Description |
-|------|-------------|
-| `-c` | Config file(s) from `src/webwright/config/` (stackable). |
-| `-t` | Task instruction. |
-| `--start-url` | Initial page. |
-| `--task-id` | Output subfolder name. |
-| `-o` | Output directory. |
+| Flag | 说明 |
+|------|------|
+| `-c` | 来自 `src/webwright/config/` 的配置文件（可叠加）。 |
+| `-t` | 任务指令。 |
+| `--start-url` | 初始页面。 |
+| `--task-id` | 输出子文件夹名称。 |
+| `-o` | 输出目录。 |
 
 ---
 
-## 🔌 Use as a Plugin
+## 🔌 作为插件使用
 
-Webwright ships plugin manifests for both [Claude Code](https://docs.claude.com/en/docs/claude-code/plugins) ([`.claude-plugin/plugin.json`](.claude-plugin/plugin.json)) and [OpenAI Codex](https://developers.openai.com/codex/plugins) ([`.codex-plugin/plugin.json`](.codex-plugin/plugin.json)), with the shared skill at [`skills/webwright/`](skills/webwright/) and slash commands at [`skills/webwright/commands/`](skills/webwright/commands/). The host agent drives the Webwright loop natively — no extra LLM API key or cost beyond your host subscription. Hosts that read PNG screenshots natively skip the `image_qa` / `self_reflection` tools.
+Webwright 为 [Claude Code](https://docs.claude.com/en/docs/claude-code/plugins) ([`.claude-plugin/plugin.json`](.claude-plugin/plugin.json)) 和 [OpenAI Codex](https://developers.openai.com/codex/plugins) ([`.codex-plugin/plugin.json`](.codex-plugin/plugin.json)) 均提供了插件清单，共享技能位于 [`skills/webwright/`](skills/webwright/)，斜杠命令位于 [`skills/webwright/commands/`](skills/webwright/commands/)。宿主智能体以原生方式驱动 Webwright 循环 — 除宿主订阅费用外，无需额外的 LLM API 密钥或成本。原生支持读取 PNG 截图的宿主会跳过 `image_qa` / `self_reflection` 工具。
 
-Common runtime deps (install once after either path):
+通用运行时依赖（任选一种安装路径后安装一次即可）：
 
 ```bash
 pip install -e .
@@ -222,9 +228,9 @@ playwright install chromium
 <details>
 <summary><b>Claude Code</b></summary>
 
-### Install
+### 安装
 
-Install through the bundled marketplace inside Claude Code:
+通过 Claude Code 内置的市场安装：
 
 ```text
 # 1. Add this repo as a Claude Code plugin marketplace
@@ -234,37 +240,37 @@ Install through the bundled marketplace inside Claude Code:
 /plugin install webwright@webwright
 ```
 
-Prefer a local checkout? Point the marketplace command at the cloned repo instead:
+更倾向于使用本地检出？将市场命令指向克隆的仓库即可：
 
 ```text
 /plugin marketplace add /absolute/path/to/Webwright
 /plugin install webwright@webwright
 ```
 
-### Use
+### 使用
 
-**Start a new Claude Code session** after installing — plugins are loaded at session start and won't appear until you restart.
+安装后**启动新的 Claude Code 会话** — 插件在会话开始时加载，重启前不会出现。
 
-You can either ask Claude Code in plain English (the skill auto-activates from its description), or use one of the slash commands:
+你可以用自然语言向 Claude Code 提问（技能会根据其描述自动激活），或使用其中一个斜杠命令：
 
 ```
 /webwright:run search Google Flights for flights from SEA to JFK on 2026-08-15 to 2026-08-20
 /webwright:craft search a ticket on Google Flights from LAX to SFO depart June 7 return June 14
 ```
 
-- `/webwright:run` (or any plain prompt) produces a **one-shot** `final_script.py` for the literal task values.
-- `/webwright:craft` produces a **reusable CLI tool**: `final_script.py` becomes one parameterized function with a Google-style `Args:` docstring and an `argparse` wrapper whose flags default to the concrete task values, so you can rerun it later with different arguments — e.g. `python final_script.py --origin JFK --destination LAX --depart-date 2026-07-01`.
+- `/webwright:run`（或任意普通提示）会为字面任务值生成**一次性** `final_script.py`。
+- `/webwright:craft` 会生成**可复用的 CLI 工具**：`final_script.py` 变成一个参数化函数，带有 Google 风格的 `Args:` docstring 和 `argparse` 包装器，其标志默认为具体任务值，因此你可以稍后使用不同参数重新运行 — 例如 `python final_script.py --origin JFK --destination LAX --depart-date 2026-07-01`。
 
-In both modes Claude Code scaffolds a workspace with `plan.md`, runs instrumented Playwright scripts under `final_runs/run_<id>/`, and visually self-verifies each critical point against the saved screenshots.
+在两种模式下，Claude Code 都会使用 `plan.md` 搭建工作区，在 `final_runs/run_<id>/` 下运行插桩的 Playwright 脚本，并针对保存的截图对每个关键点进行可视化自检。
 
 </details>
 
 <details>
 <summary><b>OpenAI Codex</b></summary>
 
-### Install
+### 安装
 
-Codex reads Claude-style marketplaces, so the same repo works as a Codex plugin marketplace. From the Codex CLI:
+Codex 可读取 Claude 风格的市场，因此同一仓库可作为 Codex 插件市场使用。在 Codex CLI 中：
 
 ```bash
 # 1. Add this repo as a Codex plugin marketplace
@@ -275,129 +281,129 @@ codex
 /plugins
 ```
 
-Prefer a local checkout?
+更倾向于使用本地检出？
 
 ```bash
 codex plugin marketplace add /absolute/path/to/Webwright
 ```
 
-Then restart Codex so the new marketplace and plugin are picked up.
+然后重启 Codex，以加载新的市场和插件。
 
-### Use
+### 使用
 
-In a new Codex thread, either ask in plain English (the skill auto-activates from its description) or invoke the bundled skill explicitly with `@webwright`:
+在新的 Codex 线程中，可以用自然语言提问（技能会根据其描述自动激活），或使用 `@webwright` 显式调用内置技能：
 
 ```
 @webwright search Google Flights for flights from SEA to JFK on 2026-08-15 to 2026-08-20
 ```
 
-Codex scaffolds a workspace with `plan.md`, runs instrumented Playwright scripts under `final_runs/run_<id>/`, and visually self-verifies each critical point against the saved screenshots.
+Codex 会使用 `plan.md` 搭建工作区，在 `final_runs/run_<id>/` 下运行插桩的 Playwright 脚本，并针对保存的截图对每个关键点进行可视化自检。
 
-To turn the plugin off without uninstalling, set its entry in `~/.codex/config.toml` to `enabled = false` and restart Codex.
+若要在不卸载的情况下关闭插件，请将其在 `~/.codex/config.toml` 中的条目设为 `enabled = false` 并重启 Codex。
 
 </details>
 
 <details>
 <summary><b>🦞 OpenClaw</b></summary>
 
-### Install
+### 安装
 
-Install directly from a local checkout (path, archive, npm spec, git repo, or `clawhub:` spec all work):
+直接从本地检出安装（路径、压缩包、npm spec、git 仓库或 `clawhub:` spec 均可）：
 
 ```bash
 openclaw plugins install /absolute/path/to/Webwright
 openclaw gateway restart   # reload so the plugin and skill are picked up
 ```
 
-Verify:
+验证：
 
 ```bash
 openclaw plugins list | grep webwright
 openclaw skills  list | grep webwright   # should show "✓ ready"
 ```
 
-### Use
+### 使用
 
-The `webwright` skill is now available to any OpenClaw agent surface (CLI, Telegram, etc.) — invoke it by asking the agent in natural language, or via the slash commands shipped under [`skills/webwright/commands/`](skills/webwright/commands/), e.g. `/webwright run <task>`.
+`webwright` 技能现可在任意 OpenClaw 智能体界面（CLI、Telegram 等）上使用 — 可通过自然语言向智能体提问，或通过 [`skills/webwright/commands/`](skills/webwright/commands/) 下附带的斜杠命令调用，例如 `/webwright run <task>`。
 
-To uninstall: `openclaw plugins uninstall webwright`.
+卸载：`openclaw plugins uninstall webwright`。
 
 </details>
 
 <details>
 <summary><b>Hermes Agent</b></summary>
 
-### Install
+### 安装
 
-[Hermes Agent](https://github.com/NousResearch/hermes-agent) is a [skills-compatible client](https://agentskills.io), so the same `skills/webwright/` folder loads as a Hermes skill. Symlink it into your Hermes user-skills directory:
+[Hermes Agent](https://github.com/NousResearch/hermes-agent) 是一款 [skills-compatible client](https://agentskills.io),，因此同一 `skills/webwright/` 文件夹可作为 Hermes 技能加载。请将其符号链接到你的 Hermes 用户技能目录：
 
 ```bash
 mkdir -p ~/.hermes/skills
 ln -sfn /absolute/path/to/Webwright/skills/webwright ~/.hermes/skills/webwright
 ```
 
-No Hermes-specific manifest is needed; only `SKILL.md` is loaded.
+无需 Hermes 专用 manifest；仅加载 `SKILL.md`。
 
-### Use
+### 用法
 
-Start Hermes (`hermes`) and ask it to drive a web task in natural language — the skill auto-activates from its description. You can also invoke it explicitly with `/webwright`.
+启动 Hermes（`hermes`），用自然语言让它驱动一项 Web 任务——技能会根据其描述自动激活。你也可以通过 `/webwright` 显式调用。
 
-Note: the named subcommands shipped under [`skills/webwright/commands/`](skills/webwright/commands/) (`/webwright:run`, `/webwright:craft`) are a Claude Code / Codex convention and are inert in Hermes; the skill itself still works end-to-end.
+注意：[`skills/webwright/commands/`](skills/webwright/commands/) 下提供的命名子命令（`/webwright:run`、`/webwright:craft`）属于 Claude Code / Codex 约定，在 Hermes 中不起作用；技能本身仍可端到端运行。
 
 </details>
 
-## 📃 Trajectory Comparison & Viewer
+## 📃 轨迹对比与查看器
 
-You can run the same tasks using the Webwright harness and its Codex / GitHub Copilot skill variant, and see how token usage and trajectories stack up between different harnesses. The trajectory viewer supports Codex, GitHub Copilot and Webwright harness traces.
+你可以使用 Webwright harness 及其 Codex / GitHub Copilot 技能变体运行相同任务，并比较不同 harness 之间的 token 用量与轨迹。轨迹查看器支持 Codex、GitHub Copilot 和 Webwright harness 的轨迹。
 
-![Trajectory comparison](assets/trajectory-compare.png)
+![轨迹对比](assets/trajectory-compare.png)
 
-### How to use
+### 使用方法
 
 ```bash
 cd assets/compare_trajectory/
 python3 -m http.server
 ```
 
-Open the webpage in your browser and upload the Webwright `raw_responses.jsonl` and attach `trajectory.json` to view. Then on the other side you can upload your Codex or GitHub Copilot trace.
+在浏览器中打开该网页，上传 Webwright 的 `raw_responses.jsonl`，并附加 `trajectory.json` 以查看。随后在另一侧上传你的 Codex 或 GitHub Copilot 轨迹。
 
-### Obtaining Codex traces:
+### 获取 Codex 轨迹：
 
 ```
 ls ~/.codex/sessions/2026/MONTH/DAY/SESSION_ID.jsonl
 ```
 
-### Obtaining GitHub Copilot traces:
+### 获取 GitHub Copilot 轨迹：
 
 ```
 /export file session
 -> session.md is the uploadable trace
 ```
 
-### Quick Comparison
+### 快速对比
 
-#### "Find the cheapest used 8-cylinder bmw made between 2005-2015 and priced from 25,000 to  50,000 dollars with mileage less than 50,000 miles or less."
+#### "查找 2005–2015 年间制造、售价 25,000 至 50,000 美元、里程不超过 50,000 英里的最便宜二手 8 缸 BMW。"
 
-| Tokens | Webwright Harness (Local Browser Mode) | Codex Webwright Skill |
+| Tokens | Webwright Harness（本地浏览器模式） | Codex Webwright Skill |
 | --- | ---: | ---: |
-| Input | 420,433 | 3,271,143 |
-| Output | 3,593 | 20,040 |
-| Reasoning | 0 | 4,410 |
-| Cached | 217,216 | 3,081,3440 |
-| Total | 424,026 | 3,291,183 |
+| 输入 | 420,433 | 3,271,143 |
+| 输出 | 3,593 | 20,040 |
+| 推理 | 0 | 4,410 |
+| 缓存 | 217,216 | 3,081,3440 |
+| 总计 | 424,026 | 3,291,183 |
 
-Individual runs and results may vary.
+单次运行及结果可能有所不同。
 
 ---
 
-## Credits
+## 致谢
 
-- [SWE-agent/mini-swe-agent](https://github.com/SWE-agent/mini-swe-agent/tree/main) — design inspiration for the minimal agent loop.
-- [Playwright](https://playwright.dev/) — browser automation.
+- [SWE-agent/mini-swe-agent](https://github.com/SWE-agent/mini-swe-agent/tree/main) — 极简 agent 循环的设计灵感。
+- [Playwright](https://playwright.dev/) — 浏览器自动化。
 
-## Citation
+## 引用
 
-If you use Webwright in your research or build on it, please cite this repository:
+若你在研究中使用 Webwright 或在其基础上进行开发，请引用本仓库：
 
 ```bibtex
 @misc{webwright2026,