learning-graph-generator

A learning graph generator builds a DAG (directed acyclic graph) that maps concepts and their prerequisite dependencies from your course description. It produces structured outputs including taxonomy categorization, dependency edges, and quality validation reports to ensure the graph is acyclic and complete.

Learning graph validation checks for cycles, orphan nodes, and analyzes indegree/outdegree metrics to ensure the DAG is valid. It detects broken prerequisite chains and provides actionable reports to fix structural issues before exporting.

Yes. The skill exports concept graphs in CSV and JSON formats compatible with visualization tools and Markdown documentation. Outputs include MkDocs-compatible Markdown, metadata, and formats suitable for editing and third-party rendering.

Course designers, educators, and AI textbook authors use learning graphs to sequence hundreds of concepts logically and validate prerequisite structures. It's essential for mapping complex curricula and ensuring dependencies don't create circular requirements.

Provide a course description and a list of concepts. The skill processes these inputs using jsonschema validation to generate a complete DAG with taxonomy assignments, dependency edges, and quality metrics.

Orphan nodes appear when concepts lack prerequisites or dependents; cycles occur when circular dependencies form. Identifying these structural issues through validation prevents invalid course sequences and helps designers refine concept relationships.