Research Report - Summary Report

Attribution: Originally authored by Weizhena. Included with attribution for use in the Deep query workflow.

Research Report - Summary Report

Trigger Method

/research-report

Execution Flow

Step 1: Locate Results Directory

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

Step 2: Scan Optional Summary Fields

Read all JSON results and extract fields suitable for display in the table of contents (numeric, short indicators), such as:

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

Use AskUserQuestion to ask the user:

• Which fields should be displayed in the table of contents besides item names?
• Provide dynamic option list (based on fields actually present in JSONs)

Step 3: Generate Python Conversion Script

Generate generate_report.py in the {topic}/ directory with requirements:

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

Table of Contents Format Requirements:

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

Script Technical Points (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-dicts {"basic_info": {"name": "xxx"}, "technical_features": {...}}

Field lookup order: top level -> category mapping key -> traverse all nested dicts

2. Category Multilingual Mapping Category names in fields.yaml and JSON keys can be any combination (Chinese-Chinese, Chinese-English, English-Chinese, English-English). Must establish bidirectional mapping:

CATEGORY_MAPPING = {
    "Basic Info": ["basic_info", "基本信息"],
    "Technical Features": ["technical_features", "technical_characteristics", "技术特性"],
    "Performance Metrics": ["performance_metrics", "performance", "性能指标"],
    "Milestone Significance": ["milestone_significance", "milestones", "里程碑意义"],
    "Business Info": ["business_info", "commercial_info", "商业信息"],
    "Competition & Ecosystem": ["competition_ecosystem", "competition", "竞争与生态"],
    "History": ["history", "历史沿革"],
    "Market Positioning": ["market_positioning", "market", "市场定位"],
}

3. Complex Value Formatting

• list of dicts (like key_events, funding_history): format each dict as one line, separate kv with |
• Regular lists: short lists joined with commas, long lists displayed with line breaks
• Nested dicts: recursively format, display with semicolons or line breaks
• Long text strings (over 100 characters): add line breaks <br> or use blockquote format for better readability

4. Additional Field Collection Collect fields present in JSON but not defined in fields.yaml, put into "Other Information" category. Note filtering:

• Internal fields: _source_file, uncertain
• Nested structure top-level keys: basic_info, technical_features, etc.
• uncertain array: display each field name line by 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