← Back to KB Index
GStack — AI Engineering Workflow
gstack-overview.md

GStack — AI Engineering Workflow

Summary: GStack is a collection of structured skill files that give AI agents persistent roles for software development — CEO reviewer, engineering manager, designer, QA lead, release engineer, debugger. The core insight: an AI agent interacting with a browser needs sub-second latency and persistent state, so gstack runs a long-lived Chromium daemon over localhost HTTP with CDP (Chrome DevTools Protocol), keeping cookies and login sessions alive across commands.

Key Concepts

Persistent Browser Architecture

Skill System

Skills are Markdown files invoked by name (e.g., /office-hours). Each skill is a specialist:

SkillRole
`/office-hours`Start here — reframes product idea before writing code
`/plan-ceo-review`CEO-level review — find the 10-star product
`/plan-eng-review`Lock architecture, data flow, edge cases, tests
`/plan-design-review`Rate design dimensions 0-10
`/design-consultation`Build complete design system from scratch
`/review`Pre-landing PR review — catches bugs that pass CI
`/debug`Systematic root-cause debugging
`/qa`Open real browser, find bugs, fix, re-verify
`/ship`Run tests, review, push, open PR — one command
`/browse`Headless Chromium — real clicks, ~100ms/command
`/careful`Warn before destructive commands
`/freeze`Lock edits to one directory — hard block
`/guard`Activate careful + freeze simultaneously

Why Bun

  1. **Compiled binaries** — single executable, no node_modules, no npx
  2. **Native SQLite** — cookie decryption without better-sqlite3
  3. **Native TypeScript** — no compilation step in development

Key Conventions

Related

[[bmad-agents]], [[kelly-handbook-multi-agent]], [[kelly-tweets-agents]]