research-report

name	research-report
user-invocable	true
description	Summarize deep research results into markdown report, cover all fields, skip uncertain values.
allowed-tools	Read, Write, Glob, Bash, AskUserQuestion

Research Report - Summary Report

Trigger

/research-report

Workflow

Step 1: Locate Results Directory

Find outline.yaml in content/_raw/research-workspaces/*/ (preferred) or anywhere via Glob fallback. 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 content/_raw/research-workspaces/{topic_slug}/generate_report.py

Step 5: Promote Report to Brain Inbox

After report.md is generated in the workspace, create a brain-ready copy in content/_raw/inbox/ so /ingest can classify it into the proper topic folder.

Filename: content/_raw/inbox/{YYYY-MM-DD}-research-{topic_slug}.md (current date in ISO format).

Prepend Obsidian frontmatter to the report contents:

---
title: "{topic} — Research Report"
date: {YYYY-MM-DD}
enableToc: true
openToc: true
tags: ["research", "compiled"]   # add topic-derived tags if obvious from items/categories
type: compiled-note
source: "research-en deep research — content/_raw/research-workspaces/{topic_slug}/"
agent-created: true
summary: "{one-line description of what was researched, ~15 words}"
---

Then append the full markdown body from report.md.

Note to user after writing:

• Print the inbox path
• Suggest: "Run /ingest to classify this into the proper topic folder (likely AI/, BUSINESS/, etc.) and update indexes."
• Workspace files (outline.yaml, fields.yaml, results/*.json, generate_report.py, original report.md) stay in content/_raw/research-workspaces/{topic_slug}/ for future /research-add-items or /research-add-fields runs.

Output

• content/_raw/research-workspaces/{topic_slug}/generate_report.py — Conversion script
• content/_raw/research-workspaces/{topic_slug}/report.md — Raw summary report (workspace copy)
• content/_raw/inbox/{YYYY-MM-DD}-research-{topic_slug}.md — Brain-ingestible report with Obsidian frontmatter