docs: make Chinese README the default

README.md

@@ -1,6 +1,12 @@
-# SkillOpt: Executive Strategy for Self-Evolving Agent Skills
+<!-- 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/SkillOpt) · [上游 README](https://github.com/microsoft/SkillOpt/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。
 
-*Train agent skills like you train neural networks — with epochs, (mini-)batchsize, learning rates, and validation gates — but without touching model weights.*
+# SkillOpt：自进化 Agent 技能（Agent Skills）的执行策略
+
+*像训练神经网络一样训练 agent 技能——使用 epoch、（mini-）batch size、学习率（learning rate）和验证门控（validation gates）——但无需改动模型权重。*
 
 [![Project Page](https://img.shields.io/badge/Project%20Page-SkillOpt-8dbb3c)](https://microsoft.github.io/SkillOpt/) [![Paper](https://img.shields.io/badge/Paper-arXiv-b31b1b)](https://arxiv.org/abs/2605.23904) [![Project Video](https://img.shields.io/badge/Project%20Video-Watch%20Demo-ff0000)](https://youtu.be/JUBMDTCiM0M) [![PyPI](https://img.shields.io/badge/PyPI-skillopt-green.svg)](https://pypi.org/project/skillopt/) [![Python 3.10+](https://img.shields.io/badge/Python-3.10%2B-blue.svg)](https://www.python.org/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
 
@@ -9,52 +15,34 @@
   <a href="https://trendshift.io/repositories/38498?utm_source=trendshift-badge&utm_medium=badge&utm_campaign=badge-trendshift-38498" target="_blank" rel="noopener noreferrer"><img src="https://trendshift.io/api/badge/trendshift/repositories/38498/weekly?language=Python" alt="microsoft%2FSkillOpt | Trendshift" width="250" height="55"/></a>
 </p>
 
-> 📖 **For installation, data preparation, training/eval commands, the full configuration reference, and framework internals, see the [Documentation & Reproduction Guide](https://microsoft.github.io/SkillOpt/docs/guideline.html)** (rendered on GitHub Pages).
+> 📖 **有关安装、数据准备、训练/评估命令、完整配置参考及框架内部机制，请参阅 [Documentation & Reproduction Guide](https://microsoft.github.io/SkillOpt/docs/guideline.html)**（在 GitHub Pages 上渲染）。
 
 ---
 
 ## News 🔥🔥🔥
-- **[2026-07-02]** 🚀 **SkillOpt [v0.2.0](https://github.com/microsoft/SkillOpt/releases/tag/v0.2.0) is out on [PyPI](https://pypi.org/project/skillopt/)!** Headline feature: **SkillOpt-Sleep**, a nightly offline self-evolution engine (harvest → mine → replay → consolidate, all behind a held-out validation gate) with multi-objective reward, experience replay + dream rollouts, and long-term memory — now shipped as the `skillopt-sleep` CLI. This release also adds cross-tool backends and plugin shells for **Claude, Codex, Copilot, Devin, and OpenClaw**, SearchQA split materialization, Windows robustness, and hardened JSON parsing. See the [release notes](https://github.com/microsoft/SkillOpt/releases/tag/v0.2.0) for the full changelog and contributor acknowledgements.
-- **[2026-06-15]** 😴 **SkillOpt-Sleep (preview)** — a nightly offline self-evolution companion for local coding agents (Claude Code / Codex / Copilot): review past sessions, replay recurring tasks, and consolidate validated skills behind a held-out gate. See **[`docs/sleep/README.md`](docs/sleep/README.md)** for what it is, how to use it, and results.
-- **[2026-06-03]** 🎉 **[gbrain](https://github.com/garrytan/gbrain), [gbrain-evals](https://github.com/garrytan/gbrain-evals/blob/main/docs/benchmarks/2026-06-03-skillopt.md), and [darwin-skill](https://github.com/alchaincyf/darwin-skill) have all integrated SkillOpt.**
-- **[2026-06-02]** 🎉 **SkillOpt [v0.1.0](https://github.com/microsoft/SkillOpt/releases/tag/v0.1.0) is now available on [PyPI](https://pypi.org/project/skillopt/)!** Install with `pip install skillopt`. This initial release includes the full training loop (rollout → reflect → aggregate → select → update → evaluate), multi-backend support (OpenAI / Azure / Claude / Qwen / MiniMax), six built-in benchmarks, and WebUI dashboard.
+- **[2026-07-02]** 🚀 **SkillOpt [v0.2.0](https://github.com/microsoft/SkillOpt/releases/tag/v0.2.0) 已发布至 [PyPI](https://pypi.org/project/skillopt/)!**  headline 功能：**SkillOpt-Sleep**，一款夜间离线自进化引擎（harvest → mine → replay → consolidate，全程受留出验证门控保护），支持多目标奖励、经验回放（experience replay）+ 梦境 rollout（dream rollouts）以及长期记忆——现已作为 `skillopt-sleep` CLI 交付。本版本还新增跨工具后端及面向 **Claude、Codex、Copilot、Devin 和 OpenClaw** 的插件外壳、SearchQA 划分物化、Windows 稳健性增强，以及加固的 JSON 解析。完整变更日志与贡献者致谢见 [release notes](https://github.com/microsoft/SkillOpt/releases/tag/v0.2.0)。
+- **[2026-06-15]** 😴 **SkillOpt-Sleep（预览版）** —— 面向本地编码 agent（Claude Code / Codex / Copilot）的夜间离线自进化伴侣：回顾过往会话、重放重复任务，并在留出门控后巩固已验证技能。其定义、用法与结果见 **[`docs/sleep/README.md`](docs/sleep/README.md)**。
+- **[2026-06-03]** 🎉 **[gbrain](https://github.com/garrytan/gbrain), [gbrain-evals](https://github.com/garrytan/gbrain-evals/blob/main/docs/benchmarks/2026-06-03-skillopt.md), 与 [darwin-skill](https://github.com/alchaincyf/darwin-skill) 均已集成 SkillOpt。**
+- **[2026-06-02]** 🎉 **SkillOpt [v0.1.0](https://github.com/microsoft/SkillOpt/releases/tag/v0.1.0) 现已在 [PyPI](https://pypi.org/project/skillopt/)!** 发布。使用 `pip install skillopt` 安装。首发版本包含完整训练循环（rollout → reflect → aggregate → select → update → evaluate）、多后端支持（OpenAI / Azure / Claude / Qwen / MiniMax）、六个内置基准测试，以及 WebUI 仪表盘。
 
 ---
 
 ## Overview
 
-Modern agent skills are usually hand-crafted, generated one-shot by a strong
-LLM, or evolved through loosely controlled self-revision — none of which
-behaves like a deep-learning optimizer for the skill itself, and none of
-which reliably improves over its starting point under feedback.
+现代 agent 技能通常靠手工编写、由强 LLM 一次性生成，或通过松散控制的自我修订来演化——这些方式都不像针对技能本身的深度学习优化器，也都不能在反馈下可靠地超越起点。
 
-**SkillOpt treats the skill document as the trainable state of a frozen
-agent**, and trains it with the discipline that makes weight-space
-optimization reproducible. A separate optimizer model turns scored rollouts
-into bounded add / delete / replace edits on a single skill document; a
-candidate edit is accepted only when it strictly improves a held-out
-validation score. A textual learning-rate budget, a rejected-edit buffer,
-and an epoch-wise slow / meta update make skill training stable while
-adding **zero inference-time model calls** at deployment.
+**SkillOpt 将技能文档视为冻结 agent 的可训练状态**，并以使权重空间优化可复现的纪律来训练它。独立的优化器模型将带评分的 rollout 转化为对单一技能文档的有界 add / delete / replace 编辑；候选编辑仅在其严格提升留出验证分数时才被接受。文本学习率预算、被拒编辑缓冲区，以及按 epoch 的慢速 / 元更新，使技能训练保持稳定，同时在部署时**零额外推理时模型调用**。
 
-The deployed artifact is a compact `best_skill.md` (typically 300–2,000
-tokens) that runs against the unchanged target model. Across **six
-benchmarks, seven target models, and three execution harnesses** (direct
-chat, Codex CLI, Claude Code CLI), SkillOpt is best or tied-best on **all
-52 evaluated (model, benchmark, harness) cells** and on GPT-5.5 lifts the
-average no-skill accuracy by **+23.5 points in direct chat, +24.8 inside
-the Codex agentic loop, and +19.1 inside Claude Code**. Optimized skill
-artifacts transfer across model scales, between Codex and Claude Code
-harnesses, and to nearby benchmarks without further optimization.
+部署产物是一个紧凑的 `best_skill.md`（通常 300–2,000 tokens），在不变的目标模型上运行。在**六个基准测试、七个目标模型和三种执行框架**（direct chat、Codex CLI、Claude Code CLI）上，SkillOpt 在**全部 52 个已评估（模型、基准测试、框架）单元格**上均为最佳或并列最佳；在 GPT-5.5 上，平均无技能准确率提升 **+23.5 点（direct chat）、+24.8 点（Codex agentic loop 内）、+19.1 点（Claude Code 内）**。优化后的技能产物可跨模型规模、在 Codex 与 Claude Code 框架之间，以及迁移至相近基准测试，而无需进一步优化。
 
-For the full method, ablations, and per-cell results see the [paper](https://arxiv.org/abs/2605.23904); for a visual walkthrough of the loop see the [project page](https://microsoft.github.io/SkillOpt/); for deeper API / backend / benchmark docs see [`docs/`](docs/).
+完整方法、消融实验与各单元格结果见 [paper](https://arxiv.org/abs/2605.23904);；循环的可视化导览见 [project page](https://microsoft.github.io/SkillOpt/);；更深入的 API / 后端 / 基准测试文档见 [`docs/`](docs/)。
 
 ## 🎬 Demo Video
 
 https://github.com/user-attachments/assets/eb12d3bc-371c-467f-904d-91b61f339ed7
 
 <p align="center">
-  <a href="https://youtu.be/JUBMDTCiM0M"><b>▶ Watch the full demo on YouTube</b></a>
+  <a href="https://youtu.be/JUBMDTCiM0M"><b>▶ 在 YouTube 观看完整演示</b></a>
 </p>
 
 ---
@@ -63,24 +51,21 @@ https://github.com/user-attachments/assets/eb12d3bc-371c-467f-904d-91b61f339ed7
 
 ### Adding a new backend
 
-A backend = a chat / exec target (e.g. `openai_chat`, `claude_chat`,
-`qwen_chat`, `minimax_chat`, `codex_exec`, `claude_code_exec`). See
-[`docs/guide/new-backend.md`](docs/guide/new-backend.md) for the full
-contract; in short you add a `skillopt/model/<name>_backend.py` module,
-register it in `skillopt/model/common.py` + `backend_config.py`, and wire
-it through the router in `skillopt/model/__init__.py`. `qwen_backend.py`
-and `minimax_backend.py` are good templates.
+后端 = 聊天 / 执行目标（例如 `openai_chat`、`claude_chat`、
+`qwen_chat`、`minimax_chat`、`codex_exec`、`claude_code_exec`）。完整约定见
+[`docs/guide/new-backend.md`](docs/guide/new-backend.md)；简而言之，你需要添加 `skillopt/model/<name>_backend.py` 模块，
+在 `skillopt/model/common.py` + `backend_config.py` 中注册，并在 `skillopt/model/__init__.py` 的路由器中接入。`qwen_backend.py`
+与 `minimax_backend.py` 是不错的模板。
 
 ### Adding a new benchmark
 
-A benchmark = a `skillopt/envs/<name>/` package with a `dataloader.py`, a
-`rollout.py`, and an `initial.md` seed skill. See
-[`docs/guide/new-benchmark.md`](docs/guide/new-benchmark.md) for the full
-contract; the simplest reference is `skillopt/envs/searchqa/`.
+基准测试 = 带有 `dataloader.py`、
+`rollout.py` 和 `initial.md` 种子技能的 `skillopt/envs/<name>/` 包。完整约定见
+[`docs/guide/new-benchmark.md`](docs/guide/new-benchmark.md)；最简单的参考是 `skillopt/envs/searchqa/`。
 
 ### WebUI
 
-Launch the monitoring dashboard (optional):
+启动监控仪表盘（可选）：
 
 ```bash
 pip install -e ".[webui]"