Unobsidian — Obsidian Vault Knowledge Graph

    1

    Explore local Obsidian note links, backlinks, and tags with your AI agent. Free and open source.

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    Unobsidian — Obsidian Vault Knowledge Graph

    Example session with this skill installed

    In a small example vault, show the one-step neighborhood of ProjectA as D3 JSON. Preserve incoming links, outgoing links, and embed relations. Explain which notes are direct neighbors.

    • Read your context and instructions
    • Compiled the unobsidian

    Example: one-step neighborhood

    This reproducible demonstration uses synthetic notes, not a personal vault:

    • ProjectA.md contains [[Plan]] and #project.
    • Plan.md contains [[ProjectA]], ![[ProjectA]], and [[Research]].
    • Research.md contains #research.

    Command (replace the example vault path with your own):

    python "${CLAUDE_SKILL_DIR}/scripts/get_neighbors.py" --vault "/path/to/example-vault" --node "ProjectA" --depth 1 --format d3

    Complete JSON returned by Unobsidian 0.1.1 for this example:

    {
  "nodes": [
    {"id": "Plan", "type": "file"},
    {"id": "ProjectA", "type": "file"},
    {"id": "#project", "type": "tag"}
  ],
  "links": [
    {"source": "Plan", "target": "ProjectA", "relation": "wikilink"},
    {"source": "Plan", "target": "ProjectA", "relation": "embed"},
    {"source": "ProjectA", "target": "Plan", "relation": "wikilink"},
    {"source": "ProjectA", "target": "#project", "relation": "tag"}
  ]
}

    Plan is a direct note neighbor: it both links to and embeds ProjectA, while ProjectA links back to Plan. #project is a direct tag neighbor. Research is two steps away, so it is excluded at depth 1. No note contents were changed.

    Next step: use depth 2 to include Research, or resolve Plan to its path and read the actual note before summarizing its contents.

    ProjectA.md

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Explore your vault with an AI agent

    Unobsidian is a free, MIT-licensed skill for exploring a local Obsidian-style vault as an explicit knowledge graph. Obsidian does not need to be installed or running.

    What it does

    • Extracts note links, embeds, and tags into compact adjacency text or D3-compatible JSON.
    • Explores 1–3 steps around a note or tag while preserving original link directions and relationship types.
    • Finds backlinks and searches nested tags.
    • Resolves note IDs to local file paths so your agent can read the relevant notes.
    • Generates an interactive HTML graph visualization on request.

    Requirements and scope

    Requires Python 3.10+, obsidian-parse==0.1.1, local access to your vault, and an agent able to run the bundled Python scripts. Designed for Claude Code; other agents need equivalent local script access. A text-only chat or marketplace connector alone cannot execute the scripts.

    Graph queries do not edit notes. Visualization writes an HTML file and loads D3.js from d3js.org. Same-name notes in different folders are not distinct graph nodes. Queries reparse the vault each time; this is not semantic search or a live synchronized index. Read note contents before drawing conclusions from graph links.

    Free and open source

    Source, installation instructions, releases, and issue tracker: https://github.com/agent-hanju/unobsidian

    If you find Unobsidian useful, a star on the original GitHub repository is appreciated — entirely optional.

    Questions or feedback: agent.hanju@gmail.com. Bug reports are also welcome on GitHub Issues.

    Version 0.1.1. Independent open-source project; not affiliated with Obsidian.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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    Requires

    Python 3.10+
    obsidian-parse==0.1.1 (install from the included requirements.txt)
    Local Obsidian-style vault directory
    An AI agent with local shell and file-reading tools
    Python

    Listed1 day ago

    What's inside

    Frequently Asked Questions