deep-research-report

<!-- ===== Layer 1: 永久缓存 ===== --> # 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: ```python 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