research-report skill

Research Report - Summary Report

Trigger

/research-report

Workflow

Step 1: Locate Results Directory

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

Step 2: Scan Optional Summary Fields

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

• github_stars
• googlescholarcites
• swebenchscore
• 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 - 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 {"basicinfo": {"name": "xxx"}, "technicalfeatures": {...}}

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., keyevents, fundinghistory): 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 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: sourcefile, uncertain
• Nested structure top-level keys: basicinfo, technicalfeatures 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