Cabinet Ai Knowledge Base logo

Cabinet Ai Knowledge Base

Organization
reason-machines
cabinet-ai-knowledge-base

AI-first knowledge base and startup OS with file-based storage, AI agents, scheduled jobs, and embedded apps

Overview

Publisherreason-machines
Repositorytrending-skills
Skill namecabinet-ai-knowledge-base
Stars
89
Forks
16
Bundled files
Instructions only
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 reason-machines on GitHub. Read the source before you install it.

Installation

Install the Cabinet Ai Knowledge Base 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/reason-machines/trending-skills/tree/main/skills/cabinet-ai-knowledge-base
  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/reason-machines/trending-skills.git /tmp/trending-skills
mkdir -p .claude/skills
cp -r /tmp/trending-skills/skills/cabinet-ai-knowledge-base .claude/skills/cabinet-ai-knowledge-base
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cabinet Ai Knowledge Base 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 Cabinet Ai Knowledge Base 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 Cabinet Ai Knowledge Base 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.

Cabinet AI Knowledge Base

Skill by ara.so — Daily 2026 Skills collection.

Cabinet is an AI-first startup OS and knowledge base where everything lives as markdown files on disk. No database, no vendor lock-in — self-hosted with AI agents that have memory, scheduled jobs, embedded HTML apps, git-backed history, and a full web terminal.


Installation

Quick Start (recommended)

bash
npx create-cabinet@latest
cd cabinet
npm run dev:all

Open http://localhost:3000 — the onboarding wizard builds your AI team in 5 questions.

Manual Clone

bash
git clone https://github.com/hilash/cabinet.git
cd cabinet
npm install
cp .env.example .env.local
npm run dev:all

Prerequisites

  • Node.js 20+
  • Claude Code CLI: npm install -g @anthropic-ai/claude-code
  • macOS or Linux (Windows via WSL)

Environment Configuration

bash
# .env.local
KB_PASSWORD=your_optional_password   # Leave empty for no auth
DOMAIN=localhost                     # Or your custom domain

Key Commands

bash
npm run dev          # Next.js dev server on port 3000
npm run dev:daemon   # WebSocket + job scheduler on port 3001
npm run dev:all      # Both servers together (use this for development)
npm run build        # Production build
npm run start        # Production mode (both servers)

Architecture

cabinet/
  src/
    app/api/         → Next.js API routes
    components/      → React components (sidebar, editor, agents, jobs, terminal)
    stores/          → Zustand state management
    lib/             → Storage, markdown, git, agents, jobs
  server/
    cabinet-daemon.ts → WebSocket + job scheduler + agent executor
  data/
    .agents/.library/ → 20 pre-built agent templates
    getting-started/  → Default KB pages

Tech stack: Next.js 16, TypeScript, Tailwind CSS, shadcn/ui, Tiptap, Zustand, xterm.js, node-cron


Project Structure (data directory)

Cabinet stores everything as markdown files under data/:

data/
  getting-started/
    index.md
  my-project/
    index.md
    research.md
    index.html        ← Embedded HTML app (auto-rendered as iframe)
  .agents/
    .library/
      ceo.md
      product-manager.md
      researcher.md
    active/
      my-ceo/
        index.md      ← Agent definition
        memory.md     ← Agent memory (auto-updated)

Agent Definition Format

Agents are defined as markdown files with YAML frontmatter:

markdown
---
name: Research Scout
role: researcher
schedule: "0 */6 * * *"   # Cron: every 6 hours
skills:
  - web-search
  - summarization
  - reddit-scout
goals:
  - Monitor competitor activity
  - Surface trending topics in AI tooling
  - File weekly summary reports
---

# Research Scout

You are a research agent for [Company Name]. Your job is to...

## Memory

<!-- Agent memory is auto-appended here by the daemon -->

Creating a Custom Agent

Via the UI

  1. Navigate to the Agents panel in the sidebar
  2. Click "New Agent" and select a template or start blank
  3. Fill in role, goals, and schedule
  4. Cabinet creates data/.agents/active/<agent-name>/index.md

Programmatically

typescript
// src/lib/agents.ts pattern — create an agent file directly
import fs from 'fs/promises'
import path from 'path'

const agentDir = path.join(process.cwd(), 'data', '.agents', 'active', 'my-agent')

await fs.mkdir(agentDir, { recursive: true })

await fs.writeFile(
  path.join(agentDir, 'index.md'),
  `---
name: My Custom Agent
role: analyst
schedule: "0 9 * * 1"
goals:
  - Analyze weekly metrics
  - Post summary to #reports channel
---

# My Custom Agent

You are a data analyst agent. Every Monday at 9am you will...
`
)

Scheduled Jobs (Cron)

Agents use standard cron syntax in their frontmatter schedule field:

yaml
# Common schedule patterns
schedule: "0 */6 * * *"    # Every 6 hours
schedule: "0 9 * * 1"      # Every Monday at 9am
schedule: "0 8 * * *"      # Every day at 8am
schedule: "*/30 * * * *"   # Every 30 minutes
schedule: "0 0 * * 0"      # Weekly on Sunday midnight

The Cabinet daemon (server/cabinet-daemon.ts) reads agent files and registers jobs via node-cron. Jobs run agent prompts through Claude Code and write results back to the agent's memory file.


Embedded HTML Apps

Drop an index.html in any folder under data/ — Cabinet automatically renders it as an embedded iframe with a full-screen toggle:

data/
  my-dashboard/
    index.html    ← Cabinet renders this as an embedded app
    data.json
    style.css

Example data/my-dashboard/index.html:

html
<!DOCTYPE html>
<html>
<head>
  <title>Metrics Dashboard</title>
  <style>
    body { font-family: sans-serif; padding: 20px; background: #1a1a1a; color: #eee; }
    .metric { font-size: 2rem; font-weight: bold; color: #55c938; }
  </style>
</head>
<body>
  <h1>Weekly Metrics</h1>
  <div class="metric" id="count">Loading...</div>
  <script>
    fetch('./data.json')
      .then(r => r.json())
      .then(d => document.getElementById('count').textContent = d.value)
  </script>
</body>
</html>

No build step required. Version controlled via git automatically.


Git-Backed History

Every save auto-commits. Cabinet wraps git operations in src/lib/git.ts:

typescript
// Auto-commit on every page save (Cabinet handles this internally)
// To access history via the UI:
// 1. Open any page
// 2. Click the history icon in the toolbar
// 3. Browse diffs and restore any version

// To inspect from the shell:
cd data
git log --oneline
git diff HEAD~1 HEAD -- my-project/research.md
git checkout HEAD~5 -- my-project/research.md  # Restore older version

Markdown Page Format

Cabinet pages are standard markdown files with optional frontmatter:

markdown
---
title: Competitor Analysis
tags: [research, competitors, q2-2026]
created: 2026-04-07
agent: research-scout
---

# Competitor Analysis

## Summary

...

## Last Updated by Agent

<!-- Agent appends updates here -->

API Routes

Cabinet exposes Next.js API routes under src/app/api/:

typescript
// Read a page
GET /api/pages?path=my-project/research

// Save a page
POST /api/pages
Body: { path: "my-project/research", content: "# Research\n..." }

// List directory
GET /api/files?dir=my-project

// Run an agent manually
POST /api/agents/run
Body: { agentId: "research-scout" }

// Get agent status
GET /api/agents/status?id=research-scout

// Search all pages
GET /api/search?q=competitor+analysis

Example: Calling the API from TypeScript

typescript
// Read a knowledge base page
const response = await fetch('/api/pages?path=my-project/research')
const { content, path } = await response.json()

// Save a page
await fetch('/api/pages', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({
    path: 'my-project/research',
    content: '# Research\n\nUpdated content...'
  })
})

// Trigger an agent run
await fetch('/api/agents/run', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({ agentId: 'research-scout' })
})

Zustand State Management

Cabinet uses Zustand stores in src/stores/. Key patterns:

typescript
// Access the page store in a component
import { usePageStore } from '@/stores/pageStore'

function MyComponent() {
  const { currentPage, savePage, pages } = usePageStore()

  const handleSave = async (content: string) => {
    await savePage({ path: currentPage.path, content })
  }

  return <div>{currentPage?.title}</div>
}

// Access agent store
import { useAgentStore } from '@/stores/agentStore'

function AgentPanel() {
  const { agents, runAgent, agentStatus } = useAgentStore()

  return (
    <ul>
      {agents.map(agent => (
        <li key={agent.id}>
          {agent.name} — {agentStatus[agent.id]}
          <button onClick={() => runAgent(agent.id)}>Run</button>
        </li>
      ))}
    </ul>
  )
}

Pre-Built Agent Templates

Located in data/.agents/.library/. Available roles:

DepartmentAgents
LeadershipCEO, COO, CFO, CTO
ProductProduct Manager, UX Designer
MarketingContent Marketer, SEO Specialist, Social Media, Growth Marketer, Copywriter
EngineeringEditor, QA Agent, DevOps Engineer
Sales & SupportSales Agent, Customer Success
AnalyticsData Analyst
OperationsPeople Ops, Legal Advisor, Researcher

To activate a template:

bash
# Copy a template to active agents
cp data/.agents/.library/researcher.md data/.agents/active/my-researcher/index.md
# Then edit goals and schedule in the copied file

Adding a New Agent Template

markdown
<!-- data/.agents/.library/custom-scout.md -->
---
name: Custom Scout
role: researcher
schedule: "0 8 * * *"
skills:
  - web-search
  - summarization
goals:
  - Monitor industry news daily
  - Summarize top 5 findings
  - Append to data/research/daily-digest.md
---

# Custom Scout

You are a research agent. Each morning you will search for recent developments
in [TOPIC] and append a dated summary to the daily digest.

## Instructions

1. Search for "[TOPIC] news" from the last 24 hours
2. Select the 5 most relevant items
3. Write a 2-3 sentence summary per item
4. Append to `data/research/daily-digest.md` with today's date as a heading

## Memory

<!-- Populated automatically -->

Common Patterns

Pattern 1: Knowledge Base with Agent Automation

data/
  company/
    overview.md          ← Human-maintained
    competitors/
      analysis.md        ← Agent-updated weekly
      index.html         ← Auto-generated dashboard
  agents/
    competitor-scout/
      index.md           ← Runs every Monday
      memory.md          ← Tracks what it found last week

Pattern 2: Research Pipeline

1. Researcher agent scouts Reddit/HN every 6h → writes to data/inbox/
2. Analyst agent summarizes inbox daily → writes to data/research/weekly.md
3. CEO agent reads weekly.md every Monday → writes strategic notes

Pattern 3: Using the Web Terminal

The web terminal (xterm.js + node-pty) gives full shell access inside the browser:

  • Press `Ctrl+`` or click the terminal icon in the sidebar
  • Run Claude Code directly: claude "analyze the data in research/competitors.md"
  • Full shell: edit files, run scripts, commit to git

Troubleshooting

Daemon not starting

bash
# Check if port 3001 is in use
lsof -i :3001
# Kill if needed
kill -9 $(lsof -t -i:3001)
# Restart
npm run dev:all

Claude Code not found

bash
npm install -g @anthropic-ai/claude-code
# Verify
claude --version

Agent jobs not running

bash
# Check daemon logs
npm run dev:daemon
# Verify cron syntax at https://crontab.guru
# Check agent frontmatter has valid schedule field

Git history not working

bash
cd data
git status
# If not initialized:
git init
git add .
git commit -m "initial"

Port conflicts

bash
# Next.js uses 3000, daemon uses 3001
# Override in package.json scripts or set PORT env var
PORT=3002 npm run dev

Embedded HTML app not rendering

  • File must be named exactly index.html
  • Must be inside a folder under data/
  • Check browser console for CSP/iframe errors
  • Try accessing directly: http://localhost:3000/apps/my-folder

Self-Hosting in Production

bash
npm run build
npm run start   # Runs both Next.js and daemon in production mode

# With PM2
pm2 start npm --name "cabinet" -- run start
pm2 save
pm2 startup
nginx
# Nginx reverse proxy
server {
  listen 80;
  server_name yourdomain.com;

  location / {
    proxy_pass http://localhost:3000;
    proxy_http_version 1.1;
    proxy_set_header Upgrade $http_upgrade;
    proxy_set_header Connection 'upgrade';
    proxy_set_header Host $host;
  }

  location /ws {
    proxy_pass http://localhost:3001;
    proxy_http_version 1.1;
    proxy_set_header Upgrade $http_upgrade;
    proxy_set_header Connection 'upgrade';
  }
}

Resources

Frequently asked questions

What does the Cabinet Ai Knowledge Base AI skill do?

AI-first knowledge base and startup OS with file-based storage, AI agents, scheduled jobs, and embedded apps

Why use Cabinet Ai Knowledge Base on TypingMind?

Because you install it once and use it with any model. Cabinet Ai Knowledge Base 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 Cabinet Ai Knowledge Base in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/reason-machines/trending-skills/tree/main/skills/cabinet-ai-knowledge-base. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Cabinet Ai Knowledge Base?

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 Cabinet Ai Knowledge Base?

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

Is the Cabinet Ai Knowledge Base AI skill free?

It is published on GitHub by reason-machines. 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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