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Brain

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coco-research
brain

Use when the user says brain, project knowledge, what do we know about, open tasks, or recent decisions, or runs /brain or /brain-update. SQLite project_brain.db tracker for entities, relationships, tasks, threads, decisions, events.

Overview

Publishercoco-research
Repositorycoco
Skill namebrain
Stars
550
Forks
12
Bundled files
12
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.

  • 12 bundled files

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

  • Open source

    Published by coco-research on GitHub. Read the source before you install it.

Installation

Install the Brain 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.
    https://github.com/coco-research/coco/tree/main/systems/brain/skills/brain
  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/coco-research/coco.git /tmp/coco
mkdir -p .claude/skills
cp -r /tmp/coco/systems/brain/skills/brain .claude/skills/brain
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Brain 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 Brain 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 Brain 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.

/brain --- Project Knowledge Tracker

A structured SQLite database (project_brain.db) that lives in each project folder, replacing flat markdown memory files with queryable, relational knowledge.

Quick Reference

bash
BRAIN="python3 ~/.claude/skills/brain/scripts/brain/brain_cli.py"

# Initialize in current project folder
$BRAIN init

# Create a project
$BRAIN add-project "My Project" --slug my-project --desc "Example project description"

# Add entities (upsert --- safe to run repeatedly)
$BRAIN add-entity my-project person "Alice Example" --external-id 2882
$BRAIN add-entity my-project team "Engineering" --external-id 33
$BRAIN add-entity my-project module "PlatformHub"

# Add relationships
$BRAIN add-rel 1 2 member_of
$BRAIN add-rel 1 3 administers

# Tasks
$BRAIN add-task my-project "Configure external reviewer access" --priority 1
$BRAIN update-task 1 --status in_progress
$BRAIN tasks my-project --status open

# Threads (group related work)
$BRAIN add-thread my-project "External User Access" --category request
$BRAIN link-thread 1 task 1
$BRAIN link-thread 1 decision 1

# Decisions
$BRAIN add-decision my-project 2026-04-08 "Use Stakeholder role for external users" \
  --context "No license consumed, view-only" --decided-by You

# Events
$BRAIN add-event my-project 2026-04-08 call "Charlie Customer Call" \
  --summary "Priorities deck shared" --participants "Alice,Bob,Charlie,You"

# CoCo context (session start)
$BRAIN context my-project

# Search across everything
$BRAIN context my-project --search "Morgan"

# Entity graph (show all connections)
$BRAIN graph 1

# Thread detail (show all linked items)
$BRAIN thread-detail 1

/brain:update --- End-of-Session Flush

This is the most important command. When the user runs /brain:update, Claude MUST do a thorough review of the entire conversation and write everything learned to the brain DB. This is a forcing function --- do not skip anything.

Procedure

  1. Scan the full conversation from top to bottom. For each project in the brain DB, extract:

    New entities --- any person, team, role, system, or module mentioned for the first time New relationships --- any connection between entities discovered (X owns Y, A reports to B, etc.) New decisions --- anything that was decided, agreed, confirmed, or resolved New events --- meetings, calls, emails read, milestones hit New tasks --- action items, to-dos, next steps, follow-ups Task updates --- tasks that changed status (done, blocked, in_progress) Entity updates --- new info about existing entities (role change, new metadata)

  2. Present a summary table to the user showing what will be written:

    BRAIN UPDATE SUMMARY
    ====================
    New entities:      3 (Alice Chen, Bob Kumar, ...)
    New decisions:     2 (Use Stakeholder role, Sandbox all-or-nothing)
    New events:        1 (Charlie CSM call Apr 8)
    New tasks:         4 (Get reviewer details, Get sandbox access, ...)
    Task updates:      2 (task #3 -> blocked, task #5 -> blocked)
    New relationships: 1 (Alice administers Platform)
    Entity updates:    1 (Charlie Sohn: added CSM role metadata)
  3. Wait for user confirmation ("looks good" / "y" / adjustments)

  4. Execute all writes via Python operations and report counts.

When to prompt the user

If the conversation has been long (>10 exchanges) and the user hasn't run /brain:update, gently suggest it:

"We've covered a lot this session. Want me to run /brain:update to capture everything before we wrap?"

Do NOT auto-run it. Always wait for the user to invoke or confirm.

Behavior Rules

Auto-write (no confirmation needed)

  • Entity sync from your custom MCP (roles, teams, users)
  • Changelog entries when entities are updated
  • Task status updates
  • Event logging from emails and meetings

Confirm-write (propose to user first)

  • New decisions
  • New relationships between entities
  • Deleting or archiving entities

Semi-auto (extract and confirm)

  • After reading emails: propose entities, events, tasks to add
  • After meeting notes: propose decisions, action items, new stakeholders

DB Location

Each project folder gets its own project_brain.db:

MyProject/project_brain.db
E&C/project_brain.db
Optimize/project_brain.db

CoCo aggregates across all known DBs at session start.

Memory Architecture

The brain DB is the source of truth for all project knowledge. It syncs to two other stores:

StoreBackendWhat syncsWhy
MemPalaceChromaDB (~/.mempalace/palace)Entities, decisions, events → drawersSemantic search across projects
brain.jsonJSON (~/.coco/brain.json)Person entities → people sectionCoCo people graph, attention rules

Sync happens automatically at end of /brain-update, /brain-init, and /brain-rescan via brain.memory_bridge.full_sync().

Write path: Always write to brain DB first → sync propagates to other stores. Read path: Use Memory Bus (memory_search) for cross-store federated queries, or brain CLI for structured queries.

Schema (v1)

11 tables: projects, entities, relationships, tasks, threads, thread_items, decisions, events, changelog, tags, taggables

Entity types: person, team, role, system, module, org_unit, document Relationship types: member_of, owns, administers, reports_to, depends_on, blocks, scoped_to, created_by Task statuses: open, in_progress, blocked, waiting, done, cancelled Thread categories: feature, incident, request, decision, research Event types: meeting, email, call, milestone, deploy

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 Brain AI skill do?

Use when the user says brain, project knowledge, what do we know about, open tasks, or recent decisions, or runs /brain or /brain-update. SQLite project_brain.db tracker for entities, relationships, tasks, threads, decisions, events.

Why use Brain on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/coco-research/coco/tree/main/systems/brain/skills/brain. 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 Brain?

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 Brain?

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

Is the Brain AI skill free?

It is published on GitHub by coco-research. Check the repository for licensing terms. 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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