Our Five Pillars - Stacktora

1. Stack Definition Engine

stacktora.json widens from “recipe for the initial generate” into the single source of truth for the whole project over its life: runtime, datastores, CI, references to where real secrets live (never the secrets themselves), deployment targets, and team coding standards. Edit the recipe, regenerate everything, forever — and, see the diff before it touches anything already running.

2. AI Context Layer

AI coding agents — Claude Code, Codex, and whatever comes next — read stacktora.json directly instead of inferring a project's setup by scanning files and guessing. Fewer wrong assumptions, cheaper and more accurate prompts, and it works for any agent, not just tooling Stacktora builds itself. This is no longer a bet on where the industry might go — it's the exact ground Vercel and Supabase are actively fighting over this quarter.

3. Recipe Marketplace

Installable, opinionated starting recipes — an official Stripe SaaS starter, a healthcare-compliant stack, a fintech baseline — shared or sold by companies, consultancies, and the community. This is the same instinct behind the Terraform Registry or Vercel's integrations marketplace: real network effects come from other people building on top of your format, not just from what you build yourself.

4. Enterprise standardization

A company.stacktora.json that enforces an organization's standards across every developer, every repo, every new hire — and every AI agent touching the codebase. Internal platform teams already build this by hand, badly, at every company that reaches a certain size. Stacktora could ship it as a product instead of leaving every company to reinvent it.

5. Becoming a standard

The long game. If CI systems, cloud platforms, and AI tools can all read the same recipe format, stacktora.json stops being “a file our tool happens to generate” and starts being infrastructure — the way Git, Docker, and Terraform's own HCL became infrastructure.

#supabase #vercel #json #cli #git #docker