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Knowledge Graph Skill: Turn Your Vault into an AI Second Brain


The InfraNodus skill can turn an Obsidian vault, a notes folder, or any collection of documents into a persistent, queryable knowledge graph. This page describes that part of the skill specifically: how to build a knowledge graph from your AI second brain, keep it updated as your material grows, and use it to generate responses that are grounded in your own knowledge rather than the model's training data.

Invoked inside your vault ("graph this vault", "analyze this folder", or /infranodus), the skill mines your markdown notes, PDF text, and document structure into saved knowledge graphs on your InfraNodus account — and from then on your AI agent answers questions about the vault's themes, connections, and gaps by querying those graphs first, before reading any files.

Get Started

Why Your Second Brain Needs a Knowledge Graph

By default, an AI reading your knowledge base uses RAG vector search: it embeds your query, retrieves the chunks that look most similar, and answers from those. This works well for lookup ("what did I write about prompt caching?") and badly for the questions a second brain is actually for: "what is missing from this knowledge base?", "what other ideas could I link to this one?". Insights are rarely located in the chunk most similar to the question — they sit in the relations between distant parts of your material, across paths that similarity search does not rank highly.

A knowledge graph fixes this: concepts become nodes, their relations become edges, topics become clusters, and the sparse regions between the clusters become visible as the gaps in what you have collected. The model gets a structural view of your material it can traverse (GraphRAG), and you get to see the same view it is working from — so you can observe and steer what the AI retrieves instead of trusting an opaque retrieval harness. See AI second brain and AI knowledge base for the full reasoning behind this architecture.

What the Skill Does for Your Vault

Run inside a vault or notes folder, the skill executes a three-step pipeline:

  1. Extract — a deterministic script mines the folder: markdown notes, text from PDFs, document structure, and [[wikilinks]] connections you already made.
  2. Upload — the extracted statements are uploaded as scoped knowledge graphs to your InfraNodus account, where you can also explore them visually, see the topical clusters, and find the structural gaps.
  3. Report — the skill writes an infranodus/manifest.json routing file into your vault and appends a dated section to an INFRANODUS_REPORT.md log with the main topics, gaps, and ideas for developing the material further.

From then on, the vault is graph-aware: whenever the manifest exists, questions like "what are the main themes in this vault?", "what's underdeveloped here?", or "how do these two ideas connect?" are answered by querying the saved graphs first, which is faster and more structural than re-reading the files each time.

Launched without a target, the skill inventories the folder and asks what to build: the full vault graph, a specific folder, documents matching certain terms, or a single document. Start with one folder — if it proves useful, widen it.

Build the Graph from Your Vault in 8 Steps

None of these require programming:

  1. Install the InfraNodus skill and connect the InfraNodus MCP server to your Claude (you'll need an API key from your free InfraNodus account).
  2. Open your vault in Cursor or VSCode.
  3. Open a terminal inside it.
  4. Launch Claude Code with claude.
  5. Type /infranodus.
  6. Answer the questionnaire — start by analyzing one folder rather than the whole vault.
  7. The skill writes the infranodus/ folder with the manifest and a report with the topics, gaps, and ideas for developing the material further.
  8. Because the MCP server is connected, the graph analysis and retrieval tools are now available to the model directly.

If you keep your notes in Obsidian and want the graph view alongside the vault itself, add the InfraNodus Obsidian plugin — or the VSCode / Cursor extension for the same graph view inside the IDE.

A Knowledge Graph That Updates

The graph is not built once. Every time you add new material to your vault, you can re-run the ingestion — uploads to the same graph append new statements, so the graph grows with your knowledge base instead of being rebuilt from scratch.

Each uploaded graph records its provenance — endpoint, account, and the date it was verified — in the local manifest, so the skill always knows which server and account a graph lives on. Adding material changes the graph, which changes which gaps are open, which changes what is worth asking next: your second brain becomes a feedback loop rather than an archive.

Ask Questions, Get Responses Grounded in Your Knowledge

Once the graph exists, the skill routes your questions to the right retrieval tool automatically:

What you ask What the skill uses
"What does my knowledge base say about X?" retrieve_from_knowledge_base — GraphRAG retrieval over the saved graph, grounded in your own material
"What would my knowledge base advise here?" generate_responses_from_graph — answers and expert advice generated from the graph in its own voice
"What is this vault about?" generate_contextual_hint, analyze_existing_graph_by_name — structural overview of a saved graph
"What am I missing?" generate_content_gaps → generate_research_questions / generate_research_ideas — under-connected clusters turned into questions worth asking
"Remember this for later" memory_add_relations / memory_get_relations — structured memory saved as graph relations

The model consults the structure first, then goes to the files — so the answers stop being summaries of the nearest paragraph and start connecting the distant parts of your material. The gap analysis does the hard part: it identifies which question is worth asking, out of material that is already there.

Related Tools & Approaches

  • InfraNodus skill — the full skill this workflow is part of: text network analysis, SEO, comparison, and repo graphs.
  • LLM Wiki skill — when you want the model to write an interlinked knowledge base on top of your sources; this skill maps what exists, llm-wiki authors new knowledge, and both share the same manifest.
  • AI Second Brain and AI Knowledge Base — the architecture behind this workflow: storage, semantic layer, query.
  • Knowledge graphs for PKM — if you keep notes in Obsidian, Roam, or Logseq and want to start from the graph side instead.

What's Next

Get the InfraNodus MCP