research-report

Featured

Summarize deep research results into markdown report, cover all fields, skip uncertain values.

Data & Documents 853 stars 79 forks Updated 2 weeks ago MIT

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Quality Score: 90/100

Stars 20%
98
Recency 20%
90
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
80
License 10%
100
Description 5%
100

Skill Content

# 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"...

Details

Author
Weizhena
Repository
Weizhena/Deep-Research-skills
Created
4 months ago
Last Updated
2 weeks ago
Language
Python
License
MIT

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