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,locationthat 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.
The graph is a starting point for investigation, not a verdict. An orphaned file might be intentionally standalone. A cluster of tightly linked files might be a well-maintained topic area or might indicate circular references with no external connections.