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Knowledge Graph

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jdrhyne
knowledge-graph

Three-Layer Memory System — automatic fact extraction, entity-based knowledge graph, and weekly synthesis. Manages life/areas/ entities with atomic facts and living summaries.

Overview

Publisherjdrhyne
Repositoryagent-skills
Skill nameknowledge-graph
Stars
240
Forks
30
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by jdrhyne on GitHub. Read the source before you install it.

Installation

Install the Knowledge Graph AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/jdrhyne/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/clawdbot/knowledge-graph .claude/skills/knowledge-graph
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Knowledge Graph in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Knowledge Graph on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Knowledge Graph is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Knowledge Graph Skill

Maintain a lightweight, append-only entity graph that compounds durable facts across sessions.

When to Use

  • Extract durable facts from recent work or conversation history
  • Rewrite entity summaries from active facts
  • Answer "what do we know about X?" without reopening large transcripts
  • Keep shared context for people, companies, and projects inside the workspace

Data Model

Store the graph under:

text
<workspace>/life/areas/
  people/<slug>/
  companies/<slug>/
  projects/<slug>/

Each entity folder should contain:

  • summary.md for the short, current snapshot
  • facts.jsonl for atomic, append-only facts

Use one JSON object per line:

json
{
  "id": "<slug>-NNN",
  "fact": "Plain-English fact",
  "category": "relationship|milestone|status|preference|context|decision",
  "ts": "YYYY-MM-DD",
  "source": "conversation|manual|inference",
  "status": "active|superseded",
  "supersedes": "<older-id>"
}

Fact Rules

  • Keep facts atomic. One durable fact per entry.
  • Append new facts instead of rewriting history.
  • When something changes, add a new fact and mark the old one as superseded.
  • Skip ephemera, greetings, speculation, and low-value chatter.
  • Check existing facts before adding duplicates.

Durable facts usually include:

  • role or relationship changes
  • key decisions
  • long-lived preferences
  • major project milestones
  • stable operating context

Workflows

Fact Extraction

  1. Read the recent daily note and the recent conversation window.
  2. Identify durable facts worth preserving.
  3. Resolve entity type and slug.
  4. Create the entity folder if it does not exist.
  5. Append new facts to facts.jsonl.
  6. Note extraction activity in the daily note if the workspace uses one.

Weekly Synthesis

  1. List entities changed during the week.
  2. Load active facts only.
  3. Rewrite summary.md in 3 to 8 concise lines.
  4. Ensure contradicted facts are marked superseded.
  5. Record a short synthesis note in the daily log if applicable.

Entity Lookup

  1. Read summary.md first.
  2. Open facts.jsonl only if the summary is stale or the user asked for detail.
  3. Fall back to broader memory search only when the entity is missing from the graph.

Low-Token Recall

Recall should be triggered, not automatic.

  • Recall when the user names a tracked person, company, or project.
  • Recall when the user explicitly asks to remember, recall, or summarize prior context.
  • Inject only the short summary by default.
  • Avoid loading raw facts unless the user asked for specifics or contradictions need resolution.

Setup

Create the core directories once:

bash
mkdir -p life/areas/people life/areas/companies life/areas/projects

If multiple agents share one workspace, point them at the same life/ directory so they operate on the same entity store.

Safety Boundaries

  • Do not store sensitive secrets, credentials, or highly personal data unless the user explicitly asked for it.
  • Do not create entities or facts for casual chat that has no durable value.
  • Do not inject the graph into every conversation by default.
  • Do not delete historical facts; supersede them with a newer fact instead.

Frequently asked questions

What does the Knowledge Graph AI skill do?

Three-Layer Memory System — automatic fact extraction, entity-based knowledge graph, and weekly synthesis. Manages life/areas/ entities with atomic facts and living summaries.

Why use Knowledge Graph on TypingMind?

Because you install it once and use it with any model. Knowledge Graph is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Knowledge Graph in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jdrhyne/agent-skills/tree/main/clawdbot/knowledge-graph. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Knowledge Graph?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Knowledge Graph?

As many as you like. As long as a model supports skills, you can use Knowledge Graph with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Knowledge Graph AI skill free?

Yes. It is published on GitHub by jdrhyne under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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