| name | startups-do |
| description | startups.do + Startups.Studio — define entire AI-generated startups as code, or operate them through the multi-surface control plane (CLI, API, SDK, MCP, web). |
startups.do + Startups.Studio
You are an expert in startups.do and Startups.Studio — the platform for building startups as structured Business-as-Code objects, operated by a default team of AI agents across every surface.
When to Use
Activate when the user is defining a startup, operating a business via the platform, running a Foundation Sprint, generating startup ideas from a Thesis, or working with the Startups.Studio control plane.
Business-as-Code
A startup is a structured system, not scattered SaaS state. Define it in TypeScript:
import { Startup } from 'startups.do'
import { engineering, product, sales, marketing } from 'teams.do'
import { dev, sell } from 'workflows.do'
export default Startup({
name: 'Acme AI',
teams: {
engineering,
product,
sales,
marketing,
},
workflows: {
build: dev,
sell,
},
services: [
'llm.do',
'payments.do',
'database.do',
'org.ai',
],
})
That's a company. It builds products, sells them, and grows — operated by AI agents using the same agents.do SDK.
The Default Agent Team
Every new startup created via Startups.Studio ships with a core team of named AI agents pre-configured as agents records:
| Agent | Role | Domain |
|---|
| Priya | Product Manager | product |
| Ralph | Implementation Specialist | engineering |
| Tom | Tech Lead / TypeScript Architect | engineering |
| Rae | Frontend / React Lead | frontend |
| Mark | Marketing Lead | marketing |
| Sally | Sales Lead | sales |
| Quinn | QA Lead | qa |
Agents and human roles are interchangeable in the type system — swap in a human with zero code changes:
import { priya } from 'agents.do'
The Multi-Surface Control Plane
Startups.Studio exposes one shared data model across five surfaces:
| Surface | Use it when |
|---|
| CLI | Terminal-first workflows — startups.studio discover customer |
| API | Direct HTTP integration — native Payload REST + /api/register |
| SDK | TypeScript code — typed collection clients |
| MCP | Agent clients — discoverable tools over the entire collection model |
| Web apps | Human visibility — admin, docs, marketing, ops |
All surfaces read/write the same records. Move between them without rebuilding state.
Collection Families (50+ Collections)
Identity & Workspace: studios, startups, domains, users, templates
Discovery & Strategy: ideas, hypotheses, experiments, lean-canvases, story-brands, business-models, competitors, differentiators, approaches, theses, advantages
Model & Execution: nouns, verbs, things, functions, workflows, actions, tasks, routines, documents, events
Agents & Operations: agents, goals, projects, approvals, sandboxes, browsers
Commercial: customers, contacts, leads, deals, accounts, campaigns, products, services, subscriptions, budgets, cost-events, revenue-events
Data & Integrations: sources, resources, integrations, chats, messages
Agents Collection Schema
Every agent in a startup — AI or human — is a typed record:
{
name: 'Priya',
startup: { id: 'startup_abc123' },
role: 'Product Manager',
objective: 'Define what gets built and in what order',
agentType: 'ai',
adapterType: 'claude-code',
capabilities: ['product-planning', 'sprint-management', 'stakeholder-communication'],
budgetMonthlyCents: 5000,
}
Foundation Sprint — Codified
Startups.Studio runs Jake Knapp's Foundation Sprint as CLI commands, producing a Founding Hypothesis:
If we help [customer] solve [problem] with [approach], they will choose it over [competitors] because our solution is [differentiation].
startups.studio discover customer
startups.studio discover problem
startups.studio discover advantages
startups.studio discover competitors
startups.studio define hypothesis
An AI agent can run the entire 2-day sprint in minutes. A human can work through it interactively.
Thesis-Driven Startup Factory
Define a strategic Thesis and the platform generates startup ideas at scale:
startups.studio theses create --json '{
"statement": "AI agents can replace professional services costing $50–200/hr",
"domains": ["onet", "naics"],
"filters": {
"onet": { "minMedianWage": 50, "automationProbability": ">0.6" },
"naics": { "sectors": ["54", "52"] }
}
}'
Reference data powering generation:
| Collection | Source | Size |
|---|
| Industries | NAICS | 1,012 codes |
| Occupations | SOC/ONET | 867 codes |
| Processes | APQC | ~1,500 codes |
| Products/Services | UNSPSC | ~70,000 codes |
Platform Services
import { llm } from 'llm.do'
import { payments } from 'payments.do'
import { db } from 'database.do'
import { org } from 'org.ai'
import { search } from 'searches.do'
await llm`summarize this article`
await payments.charge(customer, amount)
await db.find({ collection: 'users', where: { active: true } })
await org.users.invite(email)
| Service | Description |
|---|
agents.do | Named AI agents — Priya, Ralph, Tom, Rae, Mark, Sally, Quinn |
teams.do | Functional agent teams |
humans.do | Human workers — same syntax as AI agents |
workflows.do | Event-driven orchestration |
functions.do | AI function invocation |
database.do | AI-native data layer |
llm.do | LLM inference |
payments.do | Stripe Connect billing |
searches.do | Semantic & vector search |
actions.do | Tool calling & side effects |
triggers.do | Webhooks, schedules, events |
integrations.do | External service connectors |
analytics.do | Metrics, traces, insights |
org.ai | Identity, SSO, users, secrets |
Bootstrapping a New Startup
npx create-startups "AI-powered legal contract review for SMBs"
Best Practices
- Every startup is one shared model — CLI, API, SDK, MCP, and web all read/write the same records
- Start with the
create-startups CLI for fastest path from idea to first authenticated request
- Agents and humans are interchangeable — design interfaces for both from day one
org.ai manages identity and secrets — always provision it first- Use the
agents collection to define your team — both AI and human roles — before building workflows
