article-enrichment

To convert raw article text dumps into structured markdown insights, the Skill parses unformatted content to automatically generate executive summaries, verbatim quotes, and key insights for actionable brain pages.

Summarizing articles for knowledge management involves transforming raw text into structured brain pages that enhance graph connectivity, adding back-links to relevant entities, and enforcing citation conventions for future retrieval.

To enrich article pages in the media/articles directory, you ask the agent to process the specific page path, which triggers LLM-based parsing to extract entities and synthesize the unformatted text.

Yes, you can connect ingested article content to your existing brain context by grounding the synthesized information, which explains why specific details matter to your projects and adds relevant back-links.

Yes, article enrichment requires LLM-based parsing because the system relies on language models to extract entities, generate contextual summaries, and enforce strict citation conventions from unformatted text dumps.

A limitation when transforming unformatted article dumps is that the Skill specifically operates on article pages within the media/articles directory, requiring raw text to be ingested into this path before structured synthesis can occur.