A specialized tool for automating the extraction and multidimensional analysis of CSSCI journal publication data from CNKI over a five-year period.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install cjournal-analyzer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install cjournal-analyzer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is C-Journal Analyzer?
C-Journal Analyzer is a powerful academic research tool designed to provide a macro-level analysis of high-impact Chinese journals (CSSCI). By automating the retrieval of data from the China National Knowledge Infrastructure (CNKI), this tool allows researchers to move beyond single-paper reading and understand the broader shifts in disciplinary priorities. It specifically targets the competitive landscape of C-journals, helping users identify which topics are currently being prioritized by top-tier editorial boards.
Built to integrate seamlessly with Openclaw Skills, this agent handles the complex workflow of navigating academic databases, managing anti-scraping measures, and processing large datasets into visual reports. Whether you are a PhD student looking for a research gap or a professor evaluating the influence of a specific journal, this tool provides the data-driven insights necessary for academic success.
C-Journal Analyzer Use Cases
- Identifying research hotspots and emerging trends in specific academic disciplines over the last five years.
- Analyzing the core author networks and institutional contributions of a target CSSCI journal.
- Discovering research gaps and underserved topics to inform high-quality paper submissions.
- Generating comprehensive visual reports on publication frequency and methodology preferences for academic evaluation.
How C-Journal Analyzer Works
- The agent extracts the journal name from the user's request and identifies its unique CNKI code using local reference files or web search.
- It initiates a browser session to navigate the CNKI journal detail pages, monitoring for security captchas and alerting the user if manual verification is needed.
- The skill extracts journal metadata, including impact factors, and compiles a comprehensive list of all articles published in the last five years.
- A sampling strategy is employed to collect abstracts and keywords while respecting server rate limits and avoiding anti-scraping blocks.
- Collected data is processed through a Python-based analysis engine that performs Chinese word segmentation, frequency counting, and clustering.
- The tool generates a structured Word document containing trend charts, word clouds, author rankings, and submission recommendations.
C-Journal Analyzer Setup
To utilize this skill within the Openclaw Skills framework, ensure your environment is prepared with the necessary analysis libraries:
# Install the Python analysis dependencies
pip3 install jieba wordcloud python-docx matplotlib numpy
Ensure that the Chrome DevTools MCP is active for browser automation tasks and that the references/journal_codes.md file is present in your workspace for optimized journal lookups.
C-Journal Analyzer Data Schema & Taxonomy
The skill organizes its findings into a structured taxonomy to ensure clarity in the final report:
| Component | Data Points Captured |
|---|---|
| Journal Metadata | Title, Sponsor, ISSN/CN, Publication Frequency, CIF, and AIF. |
| Article Data | Title, Source URL, Full Author List, Page Ranges, and Column Sections. |
| Linguistic Data | Segmented keywords, sampled abstracts, and research methodology indicators. |
| Visual Assets | Yearly publication trend lines, Keyword Word-clouds, and Author frequency bar charts. |
C-Journal Analyzer Advanced Features
- Intelligent CAPTCHA detection that pauses automation for user input to maintain session integrity.
- Smart sampling strategy for abstract collection to maximize data richness without triggering IP bans.
- Advanced methodology identification capable of recognizing empirical, case study, or machine learning approaches from titles.
- Seamless document generation using the Openclaw Skills ecosystem to output professional-grade .docx reports directly to the user's downloads folder.
SKILL.md
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