cjournal-analyzer

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

265|23|Updated Feb 7, 2026
One-click install
npx skills add https://github.com/yipng05-max/-skills --skill cjournal-analyzer
Or copy as Structured Prompt for Agent
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.