plugins/obsidian-wiki-engine/skills/obsidian-graph-traversal/SKILL.md
Semantic link traversal for Obsidian Vaults. Builds an in-memory graph index from wikilinks and provides instant forward-link, backlink, and multi-degree connection queries. Use when exploring note relationships or finding orphaned notes.
npx skillsauth add richfrem/agent-plugins-skills obsidian-graph-traversalInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill requires Python 3.8+ and standard library only. No external packages needed.
To install this skill's dependencies:
pip-compile ./requirements.in
pip install -r ./requirements.txt
See ./requirements.txt for the dependency lockfile (currently empty — standard library only).
Status: Active
Author: Richard Fremmerlid
Domain: Obsidian Integration
Depends On: obsidian-markdown-mastery (WP05, obsidian-parser)
This skill transforms static vault notes into a queryable semantic graph. It answers questions like "What connects to Note X?" and "What are the 2nd-degree connections of Concept A?" — instantly, without rescanning the vault.
Performance Target: < 2 seconds for deep queries across 1000+ notes.
python ./graph_ops.py build --vault-root <path>
python ./graph_ops.py forward --note "Note Name"
python ./graph_ops.py backlinks --note "Note Name"
python ./graph_ops.py connections --note "Note Name" --depth 2
python ./graph_ops.py orphans --vault-root <path>
build, every .md file in the vault is parsed using the obsidian-parser![[...]]) are filtered out{source: [targets], ...} and {target: [sources], ...}.graph-index.json at the vault rootmtime — if a file changed since last build, only that file is re-indexedThe graph index enables the agent to:
tools
Ingests repository files into the ChromaDB vector store. Builds or updates the vector index from a manifest or directory scan using ingest.py. Use when new files need to be indexed or the vector store is out of date. <example> user: "Index these new plugin files into the vector database" assistant: "I'll use vector-db-ingest to add them to the vector store." </example> <example> user: "The vector store is missing recent files -- update it" assistant: "I'll use vector-db-ingest to re-index the changes." </example>
data-ai
Removes stale and orphaned chunks from the ChromaDB vector store for files that have been deleted or renamed. Use after files are removed or moved to keep the vector index in sync with the filesystem. <example> user: "Clean up the vector store after I deleted some files" assistant: "I'll use vector-db-cleanup to remove orphaned chunks." </example> <example> user: "The vector database has chunks for files that no longer exist" assistant: "I'll run vector-db-cleanup to prune them." </example>
testing
Audit Vector DB coverage -- compares the live filesystem manifest against the ChromaDB index to identify coverage gaps.
development
3-Phase Knowledge Search strategy for the RLM Factory ecosystem. Auto-invoked when tasks involve finding code, documentation, or architecture context in the repository. Enforces the optimal search order: RLM Summary Scan (O(1)) -> Vector DB Semantic Search -> Grep/Exact Match. Never skip phases.