cjournal-analyzer

Name: cjournal-analyzer
Availability: InStock
Author: yipng05-max

Analyze CSSCI/C journal catalogs from CNKI and generate Word reports.

265|23|Updated Feb 7, 2026

Search Tags:#topic modeling #research trends #cnki #word report #cssci #c journal analysis #method analysis

Ready to run

npx skills add https://github.com/yipng05-max/-skills --skill cjournal-analyzer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: cjournal-analyzer
Source: https://github.com/yipng05-max/-skills/tree/main/cjournal-analyzer
Command: npx skills add https://github.com/yipng05-max/-skills --skill cjournal-analyzer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires jieba, matplotlib, python-docx, numpy, wordcloud, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps you quickly understand what recently published papers in a specific CSSCI/C journal focus on, including research hotspots, methods, authors, and potential research gaps.

Core Features & Use Cases

CNKI-based journal mapping: Resolve the journal’s CNKI code (from a local reference table or via web lookup) and confirm with the user.
Multi-issue data collection: Collect recent five years of issue/article lists, then extract abstracts and keywords using a sampling strategy.
Automated quantitative insight + Word report: Run analysis (trends, top keywords/wordcloud, method preference, core authors, section changes, emerging/declining topics) and generate a formatted .docx report.

Quick Start

Ask the skill to analyze a CSSCI journal by name (e.g., “帮我分析《管理世界》近五年发文趋势，并生成Word报告”).

Frequently Asked Questions about cjournal-analyzer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I analyze CSSCI journal research trends and generate a Word report?▼

To analyze CSSCI journal research trends and generate a Word report, you provide the journal name and the tool collects recent CNKI article catalogs, extracts sampled abstracts, and outputs a formatted .docx file with topic distribution and core author clusters.

Can I extract keywords and abstracts from CNKI for topic modeling automatically?▼

Yes, you can extract keywords and abstracts from CNKI for topic modeling automatically by using a sampling strategy on recent five-year issue lists, which then feeds the data into downstream JSON-based quantitative analysis.

What's the best way to identify research gaps and method preferences in C journals?▼

The best way to identify research gaps and method preferences in C journals is by running automated quantitative analysis on sampled CNKI abstracts, which detects emerging and declining topics while highlighting method preferences in the generated Word report.

Do I need python-docx and wordcloud dependencies to generate CNKI journal analysis reports?▼

Yes, you need python-docx and wordcloud dependencies to generate CNKI journal analysis reports, as python-docx structures the final Word document and wordcloud visualizes the top keywords extracted from the sampled article data.

How does CNKI code resolution work for CSSCI journal mapping?▼

CNKI code resolution for CSSCI journal mapping works by looking up the journal's CNKI code from a local reference table or via web lookup, then confirming the resolved code with the user before proceeding with multi-issue data collection.

Why does CNKI data collection require anti-bot workflow guidance?▼

CNKI data collection requires anti-bot workflow guidance because automated scraping of issue and article lists often triggers access restrictions, so specific workflow steps are needed to successfully retrieve the catalog data for analysis.