/research-report

name: research-report
description: Summarize deep research results into markdown report, cover all fields, skip uncertain values.

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 request_user_input 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