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AI-first knowledge base and startup OS with file-based storage, AI agents, scheduled jobs, and embedded apps
npx skill4agent add aradotso/trending-skills cabinet-ai-knowledge-baseSkill by ara.so — Daily 2026 Skills collection.
npx create-cabinet@latest
cd cabinet
npm run dev:allgit clone https://github.com/hilash/cabinet.git
cd cabinet
npm install
cp .env.example .env.local
npm run dev:allnpm install -g @anthropic-ai/claude-code# .env.local
KB_PASSWORD=your_optional_password # Leave empty for no auth
DOMAIN=localhost # Or your custom domainnpm 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)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 pagesdata/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)---
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 -->data/.agents/active/<agent-name>/index.md// 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...
`
)schedule# 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 midnightserver/cabinet-daemon.tsnode-cronindex.htmldata/data/
my-dashboard/
index.html ← Cabinet renders this as an embedded app
data.json
style.cssdata/my-dashboard/index.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>src/lib/git.ts// 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---
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 -->src/app/api/// 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// 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' })
})src/stores/// 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>
)
}data/.agents/.library/| Department | Agents |
|---|---|
| Leadership | CEO, COO, CFO, CTO |
| Product | Product Manager, UX Designer |
| Marketing | Content Marketer, SEO Specialist, Social Media, Growth Marketer, Copywriter |
| Engineering | Editor, QA Agent, DevOps Engineer |
| Sales & Support | Sales Agent, Customer Success |
| Analytics | Data Analyst |
| Operations | People Ops, Legal Advisor, Researcher |
# 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<!-- 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 -->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 week1. 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 notesclaude "analyze the data in research/competitors.md"# Check if port 3001 is in use
lsof -i :3001
# Kill if needed
kill -9 $(lsof -t -i:3001)
# Restart
npm run dev:allnpm install -g @anthropic-ai/claude-code
# Verify
claude --version# Check daemon logs
npm run dev:daemon
# Verify cron syntax at https://crontab.guru
# Check agent frontmatter has valid schedule fieldcd data
git status
# If not initialized:
git init
git add .
git commit -m "initial"# Next.js uses 3000, daemon uses 3001
# Override in package.json scripts or set PORT env var
PORT=3002 npm run devindex.htmldata/http://localhost:3000/apps/my-foldernpm 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 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';
}
}CHANGELOG.md