Knowledge Graph Setup

Your knowledge graph is what your AI reads before it answers a question about your work: your clients, offerings, processes, people, tools, and policies, one Markdown page per concept, linked to each other, written and kept current by your AI assistant. It lives in a knowledge/ folder on your computer, in an open format called the Open Knowledge Format, so it opens in any editor and moves to any tool.

The pattern comes from Andrej Karpathy’s LLM wiki note: raw sources you never edit, a wiki of pages the AI maintains, and a schema that disciplines how it maintains them, with three operations — ingest, query, lint — that keep it alive.

New to Markdown? See Markdown Basics.

What Your Knowledge Graph Is

A folder that looks like this once the build has run (Claude Code names; in the ChatGPT app .claude/ is .agents/ and CLAUDE.md is AGENTS.md):

my-business/                     ← you open your AI here
├── .claude/skills/              ← two procedures the build writes for you
│   ├── ingest/SKILL.md             fold a document into the pages it affects
│   └── lint/SKILL.md               sweep every page against the rulebook
├── raw/                         ← your documents, exactly as they arrived; read, never edited
├── knowledge/                   ← the graph itself
│   ├── clients/                    one folder per kind of thing
│   │   ├── acme.md                 one page per real one
│   │   └── bowman.md
│   ├── engagements/
│   ├── playbooks/
│   ├── notes/                      answers worth keeping, and knowledge with no other home yet
│   ├── overview.md                 the page you would hand a new hire
│   ├── index.md                    every page, one line each
│   ├── log.md                      what changed, and when
│   └── SCHEMA.md                   the rulebook: your kinds, how they connect, the conventions
├── CLAUDE.md                    ← standing rules your AI reads every session
└── types.md                     ← the blueprint you approved before the build

raw/ is what you received; knowledge/ is what you learned. The build keeps them apart on purpose.

Not the AI Registry

The AI Registry records what you build with AI: your workflows, the skills and agents that power them, the processes they serve. The knowledge graph records what your business knows: clients, offerings, how the work is done. Both are Open Knowledge Format bundles. They live side by side in the same folder, registry/ and knowledge/, and never merge. If you already have a registry, the knowledge graph build leaves it untouched.

Before You Start

The Hands-on AI skills are in your AI tool. The build is run by a skill called building-knowledge-graph. If you installed the Hands-on AI plugin (Claude or ChatGPT), you already have it. If you add skills by uploading ZIP files (Cursor, Codex CLI, Gemini CLI, or a plan that blocks plugins), upload one of these:
- building-knowledge-graph.zip — skill folder at the root
- building-knowledge-graph-flat.zip — SKILL.md at the root, for tools that ask for that
Haven’t set up the skills yet? Follow Set Up the Skills for your tool, then come back here.
A folder for your project. Any folder works: on your computer, a synced cloud-drive folder, or a clone of a GitHub repository. Open your AI at that folder’s root, not inside knowledge/. If you made a my-business repository for the course, use that.
Two or three real documents about your work handy: a proposal, a client summary, a process doc, a team charter. The skill asks where they are and makes the raw/ folder for them. Leave out anything with personal data about identifiable people: payroll, health, individual HR records.
A jotted list of the five to ten most important named things in your work: clients, products, projects, tools, policies, teams. The interview starts there.

What You Need

A tool that opens a folder on your computer and writes files into it: Claude Code, Cowork, or the ChatGPT desktop app’s Codex view. Cursor, Codex CLI, and Gemini CLI also work with the ZIP. A browser chat window (claude.ai, ChatGPT on the web, Gemini, Microsoft 365 Copilot) cannot build a knowledge graph; the skill will say so and stop.

Claude Code: open Terminal, type cd (with a space), drag your folder into the Terminal window, press Enter, then type claude and press Enter. Or open the folder in the desktop app.
Cowork: choose the folder as your working folder when you start.
ChatGPT desktop: in the Codex view, create a project, Edit project → Add folder, choose your folder, and Make primary.

Build It

Say: “Build my knowledge graph.”

Your assistant works in phases and never moves on until you say so. Nothing is built until you send one word.

Phase	What your assistant does	What you do	You know it worked when
Home	States the folder it is in, your platform, and what is already there (a registry, an earlier graph, a saved type list)	Confirm the folder; give the name it should record you as	It asked a question and created nothing
Look around	Makes `raw/`, scans your folder, asks where your documents are, reports what it found	Point it at your documents, or say skip	It said what it found and what it did not, in plain words
Interview	Asks which work we are mapping (you, your team, the company), then the named things, who you serve, what repeats, what you look up	Answer as you would to a new hire	Every question was answered or skipped for a stated reason
Propose types	Proposes four to seven kinds of things, each with three real examples, and argues with you	Rename, merge, delete. Push back	You argued with it at least once and the list uses your words
Connect them	Proposes the sentences between the kinds, using your real examples, and asks what you would look up by following each	Confirm, correct, or strike each sentence	Every kept connection has a heading you chose
Approve	Writes exactly one file, `types.md`, and stops	Say the list is right	One file exists; nothing else has been created
build	Constructs `knowledge/`, the rules file, and the two skills	Send `build` as its own message	The folder above exists; the report lists graph, rules, skills
Run the loop	Guides one ingest, one question, one lint, one confirmed page	Drop a document in `raw/`; ask; approve fixes; confirm a page	A page changed with a cited claim; the answer named files; the lint report named files; a page shows `verified`

While it interviews you, your assistant may show a worked example from a fictional consulting practice. It illustrates the shape of a good answer and is never copied into your graph.

Keep It Alive

Three operations, all already in your folder:

Ingest. Drop a document in raw/ and say ingest it, or just tell your assistant something you know. It proposes which pages would change and what would land on each, writes after you say yes, and reports every file it touched. Each claim carries a footnote back to its source.
Query. Ask a question about your work. Your assistant reads index.md first, opens only the pages that could hold the answer, answers from those pages only, and cites them. If the answer is worth keeping, it offers to file it as a Note.
Lint. Say lint. Your assistant reads the rulebook and sweeps every page: contradictions, claims past their date, orphans, connections missing their other side, and pages worth writing. It reports first and fixes only what you approve. Safe to schedule; ingest is not, because it writes.

To see the graph drawn: install Obsidian, choose Open folder as vault, pick knowledge/, and click the graph icon.