Analyze Markdown knowledge graph structure and health metrics.
npx skills add https://github.com/agenticnotetaking/arscontexta --skill graph-agenticnotetaking
Please help me install this Agent Skill. Skill: graph Source: https://github.com/agenticnotetaking/arscontexta/tree/main/skill-sources/graph Command: npx skills add https://github.com/agenticnotetaking/arscontexta --skill graph-agenticnotetaking
This Skill helps you understand the structure and health of your knowledge graph, identifying areas for improvement and potential synthesis opportunities.
/graph health to get an overview, then /graph triangles to find potential new connections.Use the graph skill to analyze the health of your knowledge graph.
High-intent search queries and answers about installing and using this skill.
To find orphan notes and dangling links in your Markdown knowledge base, you can analyze your knowledge graph structure to identify isolated files and broken wiki links, generating a comprehensive health report.
Knowledge graph density measures the interconnectedness of notes via wiki links. Low density indicates sparse connections, meaning many notes are isolated, which limits synthesis opportunities and reduces the discoverability of related information in your knowledge base.
To identify hub notes and bridges in a network of Markdown files, run a graph analysis to detect highly connected nodes and critical links that connect separate components, ensuring structural integrity across your knowledge graph.
Yes, you can query note YAML frontmatter using natural language. The analysis supports interactive queries on your schema-based frontmatter, allowing you to filter and extract specific information from your knowledge graph structure.
The best way to discover synthesis opportunities in a knowledge graph is to analyze connected components and unconnected notes, identifying potential new wiki links that bridge isolated topics and enhance overall network density.
No, this graph analysis does not require any external dependencies or plugins. It operates directly on your Markdown files and wiki links using internal scripts and references, requiring no additional environment setup.
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