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Gbrain

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garrytan
gbrain

Search and write the company knowledge brain. Use for any question about the org, people, projects, decisions, or history, and to persist durable knowledge beyond this scope's notebook.

Overview

Publishergarrytan
Repositorygbrain
Skill namegbrain
Stars
30.1K
Forks
4.5K
Bundled files
3
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.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Gbrain 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/garrytan/gbrain.git /tmp/gbrain
mkdir -p .claude/skills
cp -r /tmp/gbrain/docs/integrations/qm-harness-snippets .claude/skills/gbrain
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gbrain 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 Gbrain 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 Gbrain 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.

gbrain — the company brain

This sandbox has the gbrain CLI connected (thin-client) to the org's central brain. It is the deep, indexed, cross-source memory: org docs, shared channel knowledge, and every agent's durable notes. Your scope's own notebook stays the fast per-turn memory; the brain is where knowledge outlives a scope and becomes searchable by everyone entitled to it.

First-run setup (once per sandbox — skip if gbrain remote doctor passes)

Your scope's brain credentials arrive via the deployment's secret handoff (keychain entry or one-time secret drop named gbrain). Then:

bash
gbrain init --mcp-only \
  --issuer-url "https://brain.<org>.com" \
  --mcp-url "https://brain.<org>.com/mcp" \
  --oauth-client-id "<client id from the handoff>" \
  --oauth-client-secret "<client secret from the handoff>"
gbrain whoami   # must succeed before using any other command

Pass the secret with --oauth-client-secret, not via GBRAIN_REMOTE_CLIENT_SECRET: an env-sourced secret is deliberately NOT written to ~/.gbrain/config.json, so every later command would fail with "No client_secret available" once the variable is out of scope. The flag persists it to the config file on this sandbox's durable disk, which is what the tool's credential capture expects.

Do not run gbrain remote doctor — it needs admin scope, which your client does not have (by design). gbrain whoami is the read-scope health check.

Reading (do this liberally)

bash
gbrain search "who decided X and why"     # hybrid semantic + keyword search
gbrain get <slug>                          # read one page
gbrain query "question" --json             # search tuned for agent consumption

You can read: the shared agent-memory source, org read-only sources (wiki, handbook), and everything under them. Reads are isolation-enforced server-side; you only ever see sources your client is entitled to.

Writing (durable knowledge only, under YOUR prefixes)

Your client is write-fenced to slug prefixes — your own namespace plus the channels you belong to. Writes outside them are rejected server-side.

bash
# personal durable memory (your namespace):
gbrain put emp-<your-slug>/people/jane-example --content "..."

# shared channel knowledge (channels you are in):
gbrain put chan-eng/decisions/2026-08-database-choice --content "..."

Conventions:

  • Write conclusions and durable facts, not chat transcripts. One page per entity/decision/topic; update the page rather than appending near-duplicates.
  • Markdown with YAML frontmatter; the brain chunks, embeds, and links it.
  • Cross-reference liberally: gbrain link <from> <to> (from must be in your namespace; linking TO any readable page is fine).
  • When you learn something channel-relevant in personal work, mirror the conclusion into the channel prefix with a (said in <where>) provenance note.

When to reach for the brain

  • Any question about the org, a person, a project, a decision, or history → gbrain search FIRST, then answer.
  • You produced knowledge with value beyond this conversation → gbrain put.
  • Something looks wrong (auth errors, empty results you don't expect) → gbrain whoami to confirm which client and scopes you're using, and report its output.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Gbrain AI skill do?

Search and write the company knowledge brain. Use for any question about the org, people, projects, decisions, or history, and to persist durable knowledge beyond this scope's notebook.

Why use Gbrain on TypingMind?

Because you install it once and use it with any model. Gbrain 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 Gbrain in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/garrytan/gbrain/tree/master/docs/integrations/qm-harness-snippets. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Gbrain?

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 Gbrain?

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

Is the Gbrain AI skill free?

Yes. It is published on GitHub by garrytan 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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