docs: translate all SKILL.md files to English

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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

skills/research/SKILL.md

@@ -1,143 +1,143 @@
 ---
 allowed-tools: Read, Write, Glob, WebSearch, Task, AskUserQuestion
-description: 对目标话题进行初步调研，生成调研outline。用于学术调研、benchmark调研、技术选型等场景。
+description: Conduct preliminary research on a topic and generate research outline. For academic research, benchmark research, technology selection, etc.
 ---
 
-# Research Skill - 初步调研
+# Research Skill - Preliminary Research
 
-## 触发方式
+## Trigger
 `/research <topic>`
 
-## 执行流程
+## Workflow
 
-### Step 1: 模型内部知识生成初步框架
+### Step 1: Generate Initial Framework from Model Knowledge
-基于topic，利用模型已有知识生成：
+Based on topic, use model's existing knowledge to generate:
-- 该领域的主要研究对象/items列表
+- Main research objects/items list in this domain
-- 建议的调研字段框架
+- Suggested research field framework
 
-输出{step1_output}，使用AskUserQuestion确认：
+Output {step1_output}, use AskUserQuestion to confirm:
-- items列表是否需要增减？
+- Need to add/remove items?
-- 字段框架是否满足需求？
+- Does field framework meet requirements?
 
-### Step 2: Web Search补充
+### Step 2: Web Search Supplement
-使用AskUserQuestion询问时间范围（如：最近6个月、2024年至今、不限）。
+Use AskUserQuestion to ask for time range (e.g., last 6 months, since 2024, unlimited).
 
-**参数获取**：
+**Parameter Retrieval**:
-- `{topic}`: 用户输入的调研话题
+- `{topic}`: User input research topic
-- `{YYYY-MM-DD}`: 当前日期
+- `{YYYY-MM-DD}`: Current date
-- `{step1_output}`: Step 1生成的完整输出内容
+- `{step1_output}`: Complete output from Step 1
-- `{time_range}`: 用户指定的时间范围
+- `{time_range}`: User specified time range
 
-**硬约束**：以下prompt必须严格复述，仅替换{xxx}中的变量，禁止改写结构或措辞。
+**Hard Constraint**: The following prompt must be strictly reproduced, only replacing variables in {xxx}, do not modify structure or wording.
 
-启动1个web-search-agent（后台），**Prompt模板**：
+Launch 1 web-search-agent (background), **Prompt Template**:
 ```python
-prompt = f"""## 任务
+prompt = f"""## Task
-调研话题: {topic}
+Research topic: {topic}
-当前日期: {YYYY-MM-DD}
+Current date: {YYYY-MM-DD}
 
-基于以下初步框架，补充最新items和推荐调研字段。
+Based on the following initial framework, supplement latest items and recommended research fields.
 
-## 已有框架
+## Existing Framework
 {step1_output}
 
-## 目标
+## Goals
-1. 验证已有items是否遗漏重要对象
+1. Verify if existing items are missing important objects
-2. 根据遗漏对象进行补充items
+2. Supplement items based on missing objects
-3. 继续搜索{topic}相关且{time_range}内的items并补充
+3. Continue searching for {topic} related items within {time_range} and supplement
-4. 补充新fields
+4. Supplement new fields
 
-## 输出要求
+## Output Requirements
-直接返回结构化结果（不写文件）：
+Return structured results directly (do not write files):
 
-### 补充Items
+### Supplementary Items
-- item_name: 简要说明（为什么应该加入）
+- item_name: Brief explanation (why it should be added)
 ...
 
-### 推荐补充字段
+### Recommended Supplementary Fields
-- field_name: 字段描述（为什么需要这个维度）
+- field_name: Field description (why this dimension is needed)
 ...
 
-### 信息来源
+### Sources
-- [来源1](url1)
+- [Source1](url1)
-- [来源2](url2)
+- [Source2](url2)
 """
 ```
 
-**One-shot示例**（假设调研AI Coding发展史）：
+**One-shot Example** (assuming researching AI Coding History):
 ```
-## 任务
+## Task
-调研话题: AI Coding 发展史
+Research topic: AI Coding History
-当前日期: 2025-12-30
+Current date: 2025-12-30
 
-基于以下初步框架，补充最新items和推荐调研字段。
+Based on the following initial framework, supplement latest items and recommended research fields.
 
-## 已有框架
+## Existing Framework
-### Items列表
+### Items List
-1. GitHub Copilot: Microsoft/GitHub开发，首个主流AI编程助手
+1. GitHub Copilot: Developed by Microsoft/GitHub, first mainstream AI coding assistant
-2. Cursor: AI-first IDE，基于VSCode
+2. Cursor: AI-first IDE, based on VSCode
 ...
 
-### 字段框架
+### Field Framework
-- 基本信息: name, release_date, company
+- Basic Info: name, release_date, company
-- 技术特性: underlying_model, context_window
+- Technical Features: underlying_model, context_window
 ...
 
-## 目标
+## Goals
-1. 验证已有items是否遗漏重要对象
+1. Verify if existing items are missing important objects
-2. 根据遗漏对象进行补充items
+2. Supplement items based on missing objects
-3. 继续搜索AI Coding 发展史相关且2024年至今内的items并补充
+3. Continue searching for AI Coding History related items within since 2024 and supplement
-4. 补充新fields
+4. Supplement new fields
 
-## 输出要求
+## Output Requirements
-直接返回结构化结果（不写文件）：
+Return structured results directly (do not write files):
 
-### 补充Items
+### Supplementary Items
-- item_name: 简要说明（为什么应该加入）
+- item_name: Brief explanation (why it should be added)
 ...
 
-### 推荐补充字段
+### Recommended Supplementary Fields
-- field_name: 字段描述（为什么需要这个维度）
+- field_name: Field description (why this dimension is needed)
 ...
 
-### 信息来源
+### Sources
-- [来源1](url1)
+- [Source1](url1)
-- [来源2](url2)
+- [Source2](url2)
 ```
 
-### Step 3: 询问用户已有字段
+### Step 3: Ask User for Existing Fields
-使用AskUserQuestion询问用户是否有已定义的字段文件，如有则读取并合并。
+Use AskUserQuestion to ask if user has existing field definition file, if so read and merge.
 
-### Step 4: 生成Outline（分离文件）
+### Step 4: Generate Outline (Separate Files)
-合并{step1_output}、{step2_output}和用户已有字段，生成两个文件：
+Merge {step1_output}, {step2_output} and user's existing fields, generate two files:
 
-**outline.yaml**（items + 配置）：
+**outline.yaml** (items + config):
-- topic: 调研主题
+- topic: Research topic
-- items: 调研对象列表
+- items: Research objects list
 - execution:
-  - batch_size: 并行agent数量（需AskUserQuestion确认）
+  - batch_size: Number of parallel agents (confirm with AskUserQuestion)
-  - items_per_agent: 每个agent调研项目数（需AskUserQuestion确认）
+  - items_per_agent: Items per agent (confirm with AskUserQuestion)
-  - output_dir: 结果输出目录（默认./results）
+  - output_dir: Results output directory (default: ./results)
 
-**fields.yaml**（字段定义）：
+**fields.yaml** (field definitions):
-- 字段分类和定义
+- Field categories and definitions
-- 每个字段的name、description、detail_level
+- Each field's name, description, detail_level
-- detail_level分层：极简 → 简要 → 详细
+- detail_level hierarchy: brief -> moderate -> detailed
-- uncertain: 不确定字段列表（保留字段，deep阶段自动填充）
+- uncertain: Uncertain fields list (reserved field, auto-filled in deep phase)
 
-### Step 5: 输出并确认
+### Step 5: Output and Confirm
-- 创建目录: `./{topic_slug}/`
+- Create directory: `./{topic_slug}/`
-- 保存: `outline.yaml` 和 `fields.yaml`
+- Save: `outline.yaml` and `fields.yaml`
-- 展示给用户确认
+- Show to user for confirmation
 
-## 输出路径
+## Output Path
 ```
-{当前工作目录}/{topic_slug}/
+{current_working_directory}/{topic_slug}/
-  ├── outline.yaml    # items列表 + execution配置
+  ├── outline.yaml    # items list + execution config
-  └── fields.yaml     # 字段定义
+  └── fields.yaml     # field definitions
 ```
 
-## 后续命令
+## Follow-up Commands
-- `/research-add-items` - 补充items
+- `/research-add-items` - Supplement items
-- `/research-add-fields` - 补充字段
+- `/research-add-fields` - Supplement fields
-- `/research-deep` - 开始深度调研
+- `/research-deep` - Start deep research

skills/research/deep/SKILL.md

@@ -1,98 +1,98 @@
 ---
-description: 读取调研outline，为每个item启动独立agent进行深度调研。禁用task output。
+description: Read research outline, launch independent agent for each item for deep research. Disable task output.
 allowed-tools: Bash, Read, Write, Glob, WebSearch, Task
 ---
 
-# Research Deep - 深度调研
+# Research Deep - Deep Research
 
-## 触发方式
+## Trigger
 `/research-deep`
 
-## 执行流程
+## Workflow
 
-### Step 1: 自动定位Outline
+### Step 1: Auto-locate Outline
-在当前工作目录查找 `*/outline.yaml` 文件，读取items列表、execution配置（含items_per_agent）。
+Find `*/outline.yaml` file in current working directory, read items list, execution config (including items_per_agent).
 
-### Step 2: 断点续传检查
+### Step 2: Resume Check
-- 检查output_dir下已完成的JSON文件
+- Check completed JSON files in output_dir
-- 跳过已完成的items
+- Skip completed items
 
-### Step 3: 分批执行
+### Step 3: Batch Execution
-- 按batch_size分批（完成一批需要得到用户同意才可进行下一批）
+- Batch by batch_size (need user approval before next batch)
-- 每个agent负责items_per_agent个项目
+- Each agent handles items_per_agent items
-- 启动web-search-agent（后台并行，禁用task output）
+- Launch web-search-agent (background parallel, disable task output)
 
-**参数获取**：
+**Parameter Retrieval**:
-- `{topic}`: outline.yaml中的topic字段
+- `{topic}`: topic field from outline.yaml
-- `{item_name}`: item的name字段
+- `{item_name}`: item's name field
-- `{item_related_info}`: item的完整yaml内容（name + category + description等）
+- `{item_related_info}`: item's complete yaml content (name + category + description etc.)
-- `{output_dir}`: outline.yaml中execution.output_dir（默认./results）
+- `{output_dir}`: execution.output_dir from outline.yaml (default: ./results)
-- `{fields_path}`: {topic}/fields.yaml的绝对路径
+- `{fields_path}`: absolute path to {topic}/fields.yaml
-- `{output_path}`: {output_dir}/{item_name}.json的绝对路径
+- `{output_path}`: absolute path to {output_dir}/{item_name}.json
 
-**硬约束**：以下prompt必须严格复述，仅替换{xxx}中的变量，禁止改写结构或措辞。
+**Hard Constraint**: The following prompt must be strictly reproduced, only replacing variables in {xxx}, do not modify structure or wording.
 
-**Prompt模板**：
+**Prompt Template**:
 ```python
-prompt = f"""## 任务
+prompt = f"""## Task
-调研 {item_related_info}，输出结构化JSON到 {output_path}
+Research {item_related_info}, output structured JSON to {output_path}
 
-## 字段定义
+## Field Definitions
-读取 {fields_path} 获取所有字段定义
+Read {fields_path} to get all field definitions
 
-## 输出要求
+## Output Requirements
-1. 按fields.yaml定义的字段输出JSON
+1. Output JSON according to fields defined in fields.yaml
-2. 不确定的字段值标注[不确定]
+2. Mark uncertain field values with [uncertain]
-3. JSON末尾添加uncertain数组，列出所有不确定的字段名
+3. Add uncertain array at the end of JSON, listing all uncertain field names
-4. 所有字段值必须使用中文输出（调研过程可用英文，但最终JSON值为中文）
+4. All field values must be in Chinese (research can be in English, but final JSON values in Chinese)
 
-## 输出路径
+## Output Path
 {output_path}
 
-## 验证
+## Validation
-完成JSON输出后，运行验证脚本确保字段完整覆盖：
+After completing JSON output, run validation script to ensure complete field coverage:
 python ~/.claude/commands/research/validate_json.py -f {fields_path} -j {output_path}
-验证通过后才算完成任务。
+Task is complete only after validation passes.
 """
 ```
 
-**One-shot示例**（假设调研GitHub Copilot）：
+**One-shot Example** (assuming researching GitHub Copilot):
 ```
-## 任务
+## Task
-调研 name: GitHub Copilot
+Research name: GitHub Copilot
-category: 国际产品
+category: International Product
-description: Microsoft/GitHub开发，首个主流AI编程助手，市场份额约40%，输出结构化JSON到 /home/weizhena/AIcoding/aicoding-history/results/GitHub_Copilot.json
+description: Developed by Microsoft/GitHub, first mainstream AI coding assistant, ~40% market share, output structured JSON to /home/weizhena/AIcoding/aicoding-history/results/GitHub_Copilot.json
 
-## 字段定义
+## Field Definitions
-读取 /home/weizhena/AIcoding/aicoding-history/fields.yaml 获取所有字段定义
+Read /home/weizhena/AIcoding/aicoding-history/fields.yaml to get all field definitions
 
-## 输出要求
+## Output Requirements
-1. 按fields.yaml定义的字段输出JSON
+1. Output JSON according to fields defined in fields.yaml
-2. 不确定的字段值标注[不确定]
+2. Mark uncertain field values with [uncertain]
-3. JSON末尾添加uncertain数组，列出所有不确定的字段名
+3. Add uncertain array at the end of JSON, listing all uncertain field names
-4. 所有字段值必须使用中文输出（调研过程可用英文，但最终JSON值为中文）
+4. All field values must be in Chinese (research can be in English, but final JSON values in Chinese)
 
-## 输出路径
+## Output Path
 /home/weizhena/AIcoding/aicoding-history/results/GitHub_Copilot.json
 
-## 验证
+## Validation
-完成JSON输出后，运行验证脚本确保字段完整覆盖：
+After completing JSON output, run validation script to ensure complete field coverage:
 python ~/.claude/commands/research/validate_json.py -f /home/weizhena/AIcoding/aicoding-history/fields.yaml -j /home/weizhena/AIcoding/aicoding-history/results/GitHub_Copilot.json
-验证通过后才算完成任务。
+Task is complete only after validation passes.
 ```
 
-### Step 4: 等待与监控
+### Step 4: Wait and Monitor
-- 等待当前批次完成
+- Wait for current batch to complete
-- 启动下一批
+- Launch next batch
-- 显示进度
+- Display progress
 
-### Step 5: 汇总报告
+### Step 5: Summary Report
-全部完成后输出：
+After all complete, output:
-- 完成数量
+- Completion count
-- 失败/不确定标记的items
+- Failed/uncertain marked items
-- 输出目录
+- Output directory
 
-## Agent配置
+## Agent Config
-- 后台执行: 是
+- Background execution: Yes
-- Task Output: 禁用（agent完成时有明确输出文件）
+- Task Output: Disabled (agent has explicit output file when complete)
-- 断点续传: 是
+- Resume support: Yes

skills/research/report/SKILL.md

@@ -1,20 +1,20 @@
 ---
-description: 将deep调研结果汇总为markdown报告，覆盖所有字段，跳过不确定值。
+description: Summarize deep research results into markdown report, cover all fields, skip uncertain values.
 allowed-tools: Read, Write, Glob, Bash
 ---
 
-# Research Report - 汇总报告
+# Research Report - Summary Report
 
-## 触发方式
+## Trigger
 `/research-report`
 
-## 执行流程
+## Workflow
 
-### Step 1: 定位结果目录
+### Step 1: Locate Results Directory
-在当前工作目录查找 `*/outline.yaml`，读取topic和output_dir配置。
+Find `*/outline.yaml` in current working directory, read topic and output_dir config.
 
-### Step 2: 扫描可选摘要字段
+### Step 2: Scan Optional Summary Fields
-读取所有JSON结果，提取适合在目录中显示的字段（数值型、简短指标），例如：
+Read all JSON results, extract fields suitable for TOC display (numeric, short metrics), e.g.:
 - github_stars
 - google_scholar_cites
 - swe_bench_score
@@ -22,70 +22,70 @@ allowed-tools: Read, Write, Glob, Bash
 - valuation
 - release_date
 
-使用AskUserQuestion询问用户：
+Use AskUserQuestion to ask user:
-- 目录中除了item名称外，还需要显示哪些字段？
+- Which fields to display in TOC besides item name?
-- 提供动态选项列表（基于实际JSON中存在的字段）
+- Provide dynamic options list (based on actual fields in JSON)
 
-### Step 3: 生成Python转换脚本
+### Step 3: Generate Python Conversion Script
-在 `{topic}/` 目录下生成 `generate_report.py`，脚本要求：
+Generate `generate_report.py` in `{topic}/` directory, script requirements:
-- 读取output_dir下所有JSON
+- Read all JSON from output_dir
-- 读取fields.yaml获取字段结构
+- Read fields.yaml to get field structure
-- 覆盖每个JSON的所有字段值
+- Cover all field values from each JSON
-- 跳过值包含[不确定]的字段
+- Skip fields with values containing [uncertain]
-- 跳过uncertain数组中列出的字段
+- Skip fields listed in uncertain array
-- 生成markdown报告格式：目录（带锚点跳转+用户选择的摘要字段）+ 详细内容（按字段分类）
+- Generate markdown report format: Table of contents (with anchor links + user-selected summary fields) + Detailed content (by field category)
-- 保存到 `{topic}/report.md`
+- Save to `{topic}/report.md`
 
-**目录格式要求**：
+**TOC Format Requirements**:
-- 必须包含每一个item
+- Must include every item
-- 每个item显示：序号、名称（锚点链接）、用户选择的摘要字段
+- Each item displays: number, name (anchor link), user-selected summary fields
-- 示例：`1. [GitHub Copilot](#github-copilot) - Stars: 10k | Score: 85%`
+- Example: `1. [GitHub Copilot](#github-copilot) - Stars: 10k | Score: 85%`
 
-#### 脚本技术要点（必须遵循）
+#### Script Technical Requirements (Must Follow)
 
-**1. JSON结构兼容**
+**1. JSON Structure Compatibility**
-支持两种JSON结构：
+Support two JSON structures:
-- 扁平结构：字段直接在顶层 `{"name": "xxx", "release_date": "xxx"}`
+- Flat structure: Fields directly at top level `{"name": "xxx", "release_date": "xxx"}`
-- 嵌套结构：字段在category子dict中 `{"basic_info": {"name": "xxx"}, "technical_features": {...}}`
+- Nested structure: Fields in category sub-dict `{"basic_info": {"name": "xxx"}, "technical_features": {...}}`
 
-字段查找顺序：顶层 -> category映射key -> 遍历所有嵌套dict
+Field lookup order: Top level -> category mapping key -> Traverse all nested dicts
 
-**2. Category多语言映射**
+**2. Category Multi-language Mapping**
-fields.yaml的category名与JSON的key可能是任意组合（中中、中英、英中、英英）。必须建立双向映射：
+fields.yaml category names and JSON keys can be any combination (CN-CN, CN-EN, EN-CN, EN-EN). Must establish bidirectional mapping:
 ```python
 CATEGORY_MAPPING = {
-    "基本信息": ["basic_info", "基本信息"],
+    "Basic Info": ["basic_info", "Basic Info"],
-    "技术特性": ["technical_features", "technical_characteristics", "技术特性"],
+    "Technical Features": ["technical_features", "technical_characteristics", "Technical Features"],
-    "性能指标": ["performance_metrics", "performance", "性能指标"],
+    "Performance Metrics": ["performance_metrics", "performance", "Performance Metrics"],
-    "里程碑意义": ["milestone_significance", "milestones", "里程碑意义"],
+    "Milestone Significance": ["milestone_significance", "milestones", "Milestone Significance"],
-    "商业信息": ["business_info", "commercial_info", "商业信息"],
+    "Business Info": ["business_info", "commercial_info", "Business Info"],
-    "竞争与生态": ["competition_ecosystem", "competition", "竞争与生态"],
+    "Competition & Ecosystem": ["competition_ecosystem", "competition", "Competition & Ecosystem"],
-    "历史沿革": ["history", "历史沿革"],
+    "History": ["history", "History"],
-    "市场定位": ["market_positioning", "market", "市场定位"],
+    "Market Positioning": ["market_positioning", "market", "Market Positioning"],
 }
 ```
 
-**3. 复杂值格式化**
+**3. Complex Value Formatting**
-- list of dicts（如key_events, funding_history）：每个dict格式化为一行，用` | `分隔kv
+- list of dicts (e.g., key_events, funding_history): Format each dict as one line, separate kv with ` | `
-- 普通list：短列表用逗号连接，长列表换行显示
+- Normal list: Short lists joined with comma, long lists displayed with line breaks
-- 嵌套dict：递归格式化，用分号或换行显示
+- Nested dict: Recursive formatting, display with semicolon or line breaks
-- 长文本字符串（超过100字符）：添加换行符`<br>`或使用blockquote格式，提高可读性
+- Long text strings (over 100 chars): Add line breaks `<br>` or use blockquote format for readability
 
-**4. 额外字段收集**
+**4. Extra Fields Collection**
-收集JSON中有但fields.yaml中没定义的字段，放入"其他信息"分类。注意过滤：
+Collect fields that exist in JSON but not defined in fields.yaml, put in "Other Info" category. Note to filter:
-- 内部字段：`_source_file`, `uncertain`
+- Internal fields: `_source_file`, `uncertain`
-- 嵌套结构顶级key：`basic_info`, `technical_features`等
+- Nested structure top-level keys: `basic_info`, `technical_features` etc.
-- `uncertain_fields`列表：需要逐行显示每个字段名，不要压缩成一行
+- `uncertain_fields` list: Display each field name on separate line, don't compress into one line
 
-**5. 不确定值跳过**
+**5. Uncertain Value Skipping**
-跳过条件：
+Skip conditions:
-- 字段值包含`[不确定]`字符串
+- Field value contains `[uncertain]` string
-- 字段名在`uncertain`数组中
+- Field name is in `uncertain` array
-- 字段值为None或空字符串
+- Field value is None or empty string
 
-### Step 4: 执行脚本
+### Step 4: Execute Script
-运行 `python {topic}/generate_report.py`
+Run `python {topic}/generate_report.py`
 
-## 输出
+## Output
-- `{topic}/generate_report.py` - 转换脚本
+- `{topic}/generate_report.py` - Conversion script
-- `{topic}/report.md` - 汇总报告
+- `{topic}/report.md` - Summary report