semantic-article-grouping

You can group articles by semantic topic by running the skill against the 'raw/articles/' directory. It automatically analyzes and categorizes the content, recommending actions like creating new pages, updating existing ones, or skipping entries based on assessed wiki value.

The best way to assess newsletter relevance is to use automated triage to compare newsletter checkpoints against existing knowledge base coverage. This semantic analysis evaluates wiki value and recommends specific actions like creating or updating pages to maintain comprehensive documentation.

Yes, Python is required to run semantic content analysis and decision-making workflows. The process uses Python scripts along with the web_extract dependency to process raw articles, analyze semantic topics, and generate actionable triage recommendations for your knowledge base.

Yes, semantic article grouping supports PDF files through dependencies like pypdf, pdfplumber, and pdf2image. These tools extract text and images from PDF documents, enabling the workflow to process raw articles and perform topic modeling for your AI knowledge base.

Automated article triage evaluates incoming content against existing knowledge base coverage to determine wiki value. By analyzing semantic topic overlap, it recommends skipping redundant content, updating existing pages with new information, or creating entirely new pages for novel topics.

The primary limitation is that web_extract requires accessible web content structures, potentially struggling with highly dynamic or restricted pages. The workflow depends on this tool alongside Python to parse raw articles, meaning extraction success directly limits the accuracy of semantic grouping.