feat: enhance research skills with validation and English translation
- Add validate_json.py for field coverage validation - Add hard constraints and one-shot examples to prompt templates - Add parameter retrieval sections before prompts - Translate all SKILL.md files to English - Update report.md with technical requirements 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Claude Opus 4.5
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---
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allowed-tools: Read, Write, Glob, WebSearch, Task, AskUserQuestion
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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。
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description: Conduct preliminary research on a topic and generate a research outline. Use for academic research, benchmark research, technology selection, etc.
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---
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# Research Skill - 初步调研
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# Research Skill - Preliminary Research
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## 触发方式
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## Trigger
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`/research <topic>`
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## 执行流程
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## Workflow
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### Step 1: 模型内部知识生成初步框架
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基于topic，利用模型已有知识生成：
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- 该领域的主要研究对象/items列表
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- 建议的调研字段框架
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### Step 1: Generate Initial Framework from Model Knowledge
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Based on the topic, use model's existing knowledge to generate:
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- Main research objects/items list in this domain
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- Suggested research field framework
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### Step 2: Web Search补充
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启动1个web-search-agent（后台），传入topic和当前日期备注，agent自行设计搜索策略补充最新items和字段建议。等待完成后获取搜集知识。
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Output {step1_output}.
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### Step 3: 询问用户已有字段
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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。
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### Step 2: Web Search Supplement
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Use AskUserQuestion to ask for time range (e.g., last 6 months, since 2024, unlimited).
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### Step 4: 生成Outline（分离文件）
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合并所有信息，生成两个文件：
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**Parameter Retrieval**:
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- `{topic}`: User input research topic
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- `{YYYY-MM-DD}`: Current date
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- `{step1_output}`: Complete output from Step 1
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- `{time_range}`: User specified time range
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**outline.yaml**（items + 配置）：
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- topic: 调研主题
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- items: 调研对象列表
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**Hard Constraint**: The following prompt must be strictly reproduced, only replacing variables in {xxx}, do not modify structure or wording.
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Launch 1 web-search-agent (background), **Prompt Template**:
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```python
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prompt = f"""## Task
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Research topic: {topic}
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Current date: {YYYY-MM-DD}
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Based on the following initial framework, supplement latest items and recommended research fields.
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## Existing Framework
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{step1_output}
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## Goals
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1. Verify if existing items are missing important objects
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2. Supplement items based on missing objects
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3. Continue searching for {topic} related items within {time_range} and supplement
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4. Supplement new fields
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## Output Requirements
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Return structured results directly (do not write files):
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### Supplementary Items
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- item_name: Brief explanation (why it should be added)
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...
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### Recommended Supplementary Fields
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- field_name: Field description (why this dimension is needed)
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...
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### Sources
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- [Source1](url1)
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- [Source2](url2)
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"""
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```
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**One-shot Example** (assuming researching AI Coding History):
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```
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## Task
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Research topic: AI Coding History
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Current date: 2025-12-30
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Based on the following initial framework, supplement latest items and recommended research fields.
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## Existing Framework
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### Items List
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1. GitHub Copilot: Developed by Microsoft/GitHub, first mainstream AI coding assistant
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2. Cursor: AI-first IDE, based on VSCode
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...
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### Field Framework
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- Basic Info: name, release_date, company
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- Technical Features: underlying_model, context_window
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...
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## Goals
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1. Verify if existing items are missing important objects
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2. Supplement items based on missing objects
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3. Continue searching for AI Coding History related items within since 2024 and supplement
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4. Supplement new fields
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## Output Requirements
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Return structured results directly (do not write files):
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### Supplementary Items
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- item_name: Brief explanation (why it should be added)
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...
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### Recommended Supplementary Fields
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- field_name: Field description (why this dimension is needed)
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...
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### Sources
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- [Source1](url1)
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- [Source2](url2)
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```
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### Step 3: Ask User for Existing Fields
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Use AskUserQuestion to ask if user has existing field definition file, if so read and merge.
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### Step 4: Generate Outline (Separate Files)
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Merge {step1_output}, {step2_output} and user's existing fields, generate two files:
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**outline.yaml** (items + config):
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- topic: Research topic
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- items: Research objects list
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- execution:
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- batch_size: 并行agent数量（默认5）
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- items_per_agent: 每个agent调研项目数（默认1，需AskUserQuestion确认）
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- output_dir: 结果输出目录（默认./results）
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- batch_size: Number of parallel agents (confirm with AskUserQuestion)
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- items_per_agent: Items per agent (confirm with AskUserQuestion)
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- output_dir: Results output directory (default: ./results)
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**fields.yaml**（字段定义）：
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- 字段分类和定义
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- 每个字段的name、description、detail_level
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- uncertain: 不确定字段列表（保留字段，deep阶段自动填充）
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**fields.yaml** (field definitions):
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- Field categories and definitions
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- Each field's name, description, detail_level
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- uncertain: Uncertain fields list (reserved field, auto-filled in deep phase)
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### Step 5: 输出并确认
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- 创建目录: `./{topic_slug}/`
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- 保存: `outline.yaml` 和 `fields.yaml`
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- 展示给用户确认
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### Step 5: Output and Confirm
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- Create directory: `./{topic_slug}/`
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- Save: `outline.yaml` and `fields.yaml`
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- Show to user for confirmation
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## 输出路径
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## Output Path
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```
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{当前工作目录}/{topic_slug}/
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├── outline.yaml # items列表 + execution配置
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└── fields.yaml # 字段定义
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{current_working_directory}/{topic_slug}/
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├── outline.yaml # items list + execution config
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└── fields.yaml # field definitions
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```
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## 后续命令
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- `/research-add-items` - 补充items
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- `/research-add-fields` - 补充字段
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- `/research-deep` - 开始深度调研
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## Follow-up Commands
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- `/research-add-items` - Supplement items
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- `/research-add-fields` - Supplement fields
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- `/research-deep` - Start deep research
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