Research Report

Research Report - Summary Report

Trigger

/research-report

Workflow

Step 1: Locate Results Directory

Find */outline.yaml in current working directory, read topic and output_dir config.

Step 2: Scan Optional Summary Fields

Read all JSON results, extract fields suitable for TOC display (numeric, short metrics), e.g.:

• github_stars
• google_scholar_cites
• swe_bench_score
• user_scale
• valuation
• release_date

Use AskUserQuestion to ask user:

• Which fields to display in TOC besides item name?
• Provide dynamic options list (based on actual fields in JSON)

Step 3: Generate Python Conversion Script

Generate generate_report.py in {topic}/ directory, script requirements:

• Read all JSON from output_dir
• Read fields.yaml to get field structure
• Cover all field values from each JSON
• Skip fields with values containing [uncertain]
• Skip fields listed in uncertain array
• Generate markdown report format: Table of contents (with anchor links + user-selected summary fields) + Detailed content (by field category)
• Save to {topic}/report.md

TOC Format Requirements:

• Must include every item
• Each item displays: number, name (anchor link), user-selected summary fields
• Example: 1. [GitHub Copilot](#github-copilot) - Stars: 10k | Score: 85%

Script Technical Requirements (Must Follow)

1. JSON Structure Compatibility Support two JSON structures:

• Flat structure: Fields directly at top level {"name": "xxx", "release_date": "xxx"}
• Nested structure: Fields in category sub-dict {"basic_info": {"name": "xxx"}, "technical_features": {...}}

Field lookup order: Top level -> category mapping key -> Traverse all nested dicts

2. Category Multi-language Mapping fields.yaml category names and JSON keys can be any combination (CN-CN, CN-EN, EN-CN, EN-EN). Must establish bidirectional mapping:

CATEGORY_MAPPING = {
    "Basic Info": ["basic_info", "Basic Info"],
    "Technical Features": ["technical_features", "technical_characteristics", "Technical Features"],
    "Performance Metrics": ["performance_metrics", "performance", "Performance Metrics"],
    "Milestone Significance": ["milestone_significance", "milestones", "Milestone Significance"],
    "Business Info": ["business_info", "commercial_info", "Business Info"],
    "Competition & Ecosystem": ["competition_ecosystem", "competition", "Competition & Ecosystem"],
    "History": ["history", "History"],
    "Market Positioning": ["market_positioning", "market", "Market Positioning"],
}

3. Complex Value Formatting

• list of dicts (e.g., key_events, funding_history): Format each dict as one line, separate kv with |
• Normal list: Short lists joined with comma, long lists displayed with line breaks
• Nested dict: Recursive formatting, display with semicolon or line breaks
• Long text strings (over 100 chars): Add line breaks <br> or use blockquote format for readability

4. Extra Fields Collection Collect fields that exist in JSON but not defined in fields.yaml, put in "Other Info" category. Note to filter:

• Internal fields: _source_file, uncertain
• Nested structure top-level keys: basic_info, technical_features etc.
• uncertain array: Display each field name on separate line, don't compress into one line

5. Uncertain Value Skipping Skip conditions:

• Field value contains [uncertain] string
• Field name is in uncertain array
• Field value is None or empty string

Step 4: Execute Script

Run python {topic}/generate_report.py

Output

• {topic}/generate_report.py - Conversion script
• {topic}/report.md - Summary report

触发方式

/research-report

执行流程

Step 1: 定位结果目录

在当前工作目录查找 */outline.yaml，读取topic和output_dir配置。

Step 2: 扫描可选摘要字段

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：

• github_stars
• google_scholar_cites
• swe_bench_score
• user_scale
• valuation
• release_date

使用AskUserQuestion询问用户：

• 目录中除了item名称外，还需要显示哪些字段？
• 提供动态选项列表（基于实际JSON中存在的字段）

Step 3: 生成Python转换脚本

在 {topic}/ 目录下生成 generate_report.py，脚本要求：

• 读取output_dir下所有JSON
• 读取fields.yaml获取字段结构
• 覆盖每个JSON的所有字段值
• 跳过值包含[不确定]的字段
• 跳过uncertain数组中列出的字段
• 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）
• 保存到 {topic}/report.md

目录格式要求：

• 必须包含每一个item
• 每个item显示：序号、名称（锚点链接）、用户选择的摘要字段
• 示例：1. [GitHub Copilot](#github-copilot) - Stars: 10k | Score: 85%

脚本技术要点（必须遵循）

1. JSON结构兼容 支持两种JSON结构：

• 扁平结构：字段直接在顶层 {"name": "xxx", "release_date": "xxx"}
• 嵌套结构：字段在category子dict中 {"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

2. Category多语言映射 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：

CATEGORY_MAPPING = {
    "基本信息": ["basic_info", "基本信息"],
    "技术特性": ["technical_features", "technical_characteristics", "技术特性"],
    "性能指标": ["performance_metrics", "performance", "性能指标"],
    "里程碑意义": ["milestone_significance", "milestones", "里程碑意义"],
    "商业信息": ["business_info", "commercial_info", "商业信息"],
    "竞争与生态": ["competition_ecosystem", "competition", "竞争与生态"],
    "历史沿革": ["history", "历史沿革"],
    "市场定位": ["market_positioning", "market", "市场定位"],
}

3. 复杂值格式化

• list of dicts（如key_events, funding_history）：每个dict格式化为一行，用|分隔kv
• 普通list：短列表用逗号连接，长列表换行显示
• 嵌套dict：递归格式化，用分号或换行显示
• 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

4. 额外字段收集 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：

• 内部字段：_source_file, uncertain
• 嵌套结构顶级key：basic_info, technical_features等
• uncertain数组：需要逐行显示每个字段名，不要压缩成一行

5. 不确定值跳过 跳过条件：

• 字段值包含[不确定]字符串
• 字段名在uncertain数组中
• 字段值为None或空字符串

Step 4: 执行脚本

运行 python {topic}/generate_report.py

输出

• {topic}/generate_report.py - 转换脚本
• {topic}/report.md - 汇总报告