Subsystem deep-dive

The Knowledge Graph

My notes, articles, and podcasts turned into a connected graph Claude can query, plus a second, separate graph that audits Cloze's help center for contradictions.

0
Brain nodes
0
Connections
0
ClozeKB nodes
0
Contradictions found
How it's built
Notes in, queryable graph out

graphify reads the source material, extracts concepts and their relationships, de-duplicates, and writes one graph.json that several consumers read.

Sources

graph-visible material

Brain notes (Projects / Sessions / Stack), articles + news digests, 3,530 podcast summaries.

Build

supervised · Max-covered · $0 marginal

/graphify --update (I run it), concept + relationship extraction, dedup pass (canonicalize).

graph.json

21,106 nodes · 21,515 edges

brain-graph MCP (7 query tools), LLM wiki: 3,703 files, auto-regen, Obsidian graph view.

Two graphs
One brain, one audit

Same engine (graphify), two independent corpora, two MCP servers. Kept fully separate by design.

Brain graph

My second brain across everything: notes, reading, listening. Nodes: 21,106. Connections (edges): 21,515. LLM wiki files: 3,703. Served by brain-graph MCP.

ClozeKB graph + audit

A domain audit: Cloze's help center, graphed and fact-checked. Nodes: 2,458. Named communities: 343. Contradictions found: 41. Served by cloze-graph + cloze-audit.

What Claude can do with it
7 query tools, offline & free

The graph is exposed as a read-only MCP server, so any Claude session can ask it questions. No tokens, no network, just the local graph.

get_node

Fetch a concept and everything known about it.

get_neighbors

What's directly connected to a concept.

query_graph

Search the whole graph by topic or term.

shortest_path

How two ideas connect, step by step.

get_community

Pull a whole cluster of related notes.

god_nodes

The most-connected hubs. My core themes.

graph_stats

The size and shape of the graph at a glance.

+ 5 audit tools

cloze-audit surfaces contradictions, coverage gaps, and redundant articles with sourced quotes.

The design calls
Why it's built this way
Supervised ingest. I run /graphify --update by hand. It's Max-covered (no metered cost) and keeps a human in the loop, which matters because Brain ingests untrusted web text, an injection surface an unattended agent shouldn't touch.
Dedup pass. A canonicalization step merges duplicate concepts so the graph stays clean as new material lands.
Auto-regenerating wiki. An agent-crawlable wiki (3,703 files) rebuilds itself whenever the graph changes. Pure-local, no token cost.
Queries are free. Building the graph costs (supervised). Querying it doesn't. The MCP server reads the local file, so every Claude session gets the graph for $0.