Knowledge Graph — Document Relationship Mapping

Parse markdown files to build a map of how a project's documents connect. Useful for orientation (what exists, how it's organized), finding related content before writing or reviewing, and maintenance (orphans, broken links, missing back-links).

What It Parses

The bundled resources/graph.py script extracts relationships from these patterns:

Markdown links — [text](path) explicit references between documents
Wikilinks — [[entity-name]] shorthand references common in knowledge bases
Mermaid relationship blocks — graph, flowchart, and relationship lines in fenced mermaid blocks
YAML front matter references — fields like arc, chapter, characters, location that connect documents by metadata
Entity mentions — names and terms that appear across documents without explicit links

What It Outputs

Connectivity graph — file → list of outbound links and the files that link back to it
Orphaned files — documents nothing links to (potential dead content or missing integration)
Broken links — references to files or paths that don't exist
Missing back-links — A references B, but B doesn't reference A (useful for wiki and knowledge base maintenance)
Clusters — groups of tightly connected documents (helps identify topic areas)
Entity mention map — which documents mention which entities, even without explicit links

Running the Script

Run:

uv run resources/graph.py [root_directory]

If no directory is specified, it searches from the current working directory. The script uses only the Python standard library, so uv run works without a project environment or third-party packages.

The output is plain text, structured with clear section headers. Pipe it, redirect it to a file, or read it directly.

When to Read the Script Source

Read resources/graph.py if you need to understand exactly what patterns it matches, extend it for a project with custom link conventions, or debug unexpected output. For normal use, just run it and read the report.

Interpreting Results

Orphaned files aren't automatically problems — some documents are entry points (READMEs, indexes) that are linked to but not from. Missing back-links matter most in wikis and knowledge bases where bidirectional navigation is expected. Broken links are almost always worth fixing.