README.md

---

great_cto is the orchestration layer above the coding agent you already use. A pipeline of 69 specialist agents — architect, design-advisor, senior-dev, code-reviewer, QA, security, devops — plans, builds, reviews, and deploys a real application: backend, frontend, generated tests, live URL.

You are stopped exactly twice: once on what gets built, once on whether it ships. Everything between runs unattended.

   describe a product
        │
   🤖  spec · architecture · data model · screens
        ▼
   👤  checkpoint 1 — approve the design
        │
   🤖  scaffold → backend → frontend → tests → review → security
        ▼
   👤  checkpoint 2 — approve the deploy
        │
   🤖  deployed · repo · live URL

The board at localhost:3141 fills itself in — pipeline state, pending gates, per-agent cost, 30-day spend. You do not feed it; you check it.

Numbers, measured

One feature, end to end, fully traced	1h 26m · $3.40 in tokens — the receipts
A whole product — 7 built in the open benchmark	median $171 in tokens · 70/100 quality (58–86) — reproduce it
Typical month, 20 pipeline runs	~$34 — you pay your own LLM provider, nothing else
Products it knows how to build	60, across 15 US industries, through 6 reusable pipelines

The quality score is produced by running each product's own tests, not by counting files — which is why it says 70 and not a rounder, prettier number.

Quick start

npx great-cto init            # Claude Code (default) · add --host codex for OpenAI Codex

Restart your AI host, then:

/start "build a dispatch & scheduling app for an HVAC business"

The pipeline takes it from there. Day to day you touch three things:

/start "…"	describe a product or feature — the pipeline runs it
/inbox	what needs you: pending gates, P0s, blocked tasks
/digest	weekly DORA metrics + cost-per-feature roll-up

Requires Node ≥ 18.17. Companion plugins (Superpowers, Beads) install automatically. After init, verify the host actually loaded the plugin — claude plugin list --json should show no errors for great_cto.

When it asks you

One setting in .great_cto/PROJECT.md decides where the pipeline stops:

approval-level	Stops at	Per feature
product-only	what we build · whether it ships	2
gates-only (default)	the design · the deploy	2
strict	+ code review	3
auto	nothing	0

A regulated archetype — fintech, healthcare, gov — keeps its security, compliance and ship gates at every level, including auto. A lighter level delegates judgement; it never skips compliance. Full table: docs/GATES.md.

What makes it different

• Specialists, not a generalist — 69 agents with narrow jobs and their own review gates, instead of one assistant that types faster than it thinks. The roster →
• Critics before code — architecture, spec, and schema critics run before planning, where a mistake still costs hours instead of days.
• Scope enforced at write time — an agent physically cannot touch files outside its brief. Not flagged at review; refused at write.
• QA that distrusts itself — critical paths written as Gherkin before test code, then mutation testing asks whether the suite would catch anything at all.
• Memory across sessions — decisions, lessons, and promoted patterns persist per project and globally; an interrupted run resumes knowing which stages ran.
• Cost you can see — per-agent spend, estimate-vs-actual drift, and cost-per-accepted-change on the board, not in a spreadsheet.

Everything runs locally, MIT-licensed, on your own keys. Your code stays on your machine; prompts go to your LLM provider and nowhere else. Telemetry is off by default (docs/PRIVACY.md).

Limitations

• For one builder — a solo founder or CTO. Two or more engineers sharing the pipeline have outgrown it.
• Not a CI/CD system — gates run locally; you still merge through GitHub Actions.
• Not certification-audited — PCI/HIPAA/SOC2 scaffolds are starting points, not certifications.
• Not deterministic — LLM output. Gate verdicts deserve a sanity check.

Documentation

Docs hub → · Getting started · Gates & approval levels · Agents · Commands · Archetypes · Architecture · MCP · FAQ · Everything else — critics, jurisdictions, cost breakdown, CI, alerts

Community

Issues · Discussions · Blog · Security policy · Contributing

MIT — LICENSE. Built by @avelikiy: CTO building AI-native trading and fintech platforms; great_cto is my own loops, automated one agent at a time.

If it saved you time, a star helps other solo builders find it.