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AI Agent2026
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gstack

A 23-skill Claude Code toolkit that composes an LLM-driven virtual engineering team (CEO, designer, eng manager, QA, security, release) into slash-command workflows — includes real-browser QA, a persistent GBrain memory, multi-agent integrations, and team auto-update semantics.

Introduction

Most developer tooling adds one helper; this intentionally treats the LLM as an entire multidisciplinary team. gstack packages 23 opinionated Claude Code slash-commands and a small set of CLIs so a single developer (or an automated agent) can run product interrogation, engineering design, automated reviews, security audits, browser-driven QA, and release automation with end-to-end handoffs.

What Sets It Apart
  • Composable specialist skills: each slash command maps to a familiar role (CEO review, eng review, design review, QA, CSO, release engineer), so chains of skills model a full sprint lifecycle (Think → Plan → Build → Review → Test → Ship → Reflect). This makes end-to-end automation explicit rather than ad hoc prompting.
  • Real-browser verification and security focus: built-in headed/headless Chromium browsing for authenticated QA, screenshotting, and domain-skills; layered prompt-injection defenses and threat-modeling skills (OWASP + STRIDE) baked into workflows.
  • Persistent project memory and team-mode installs: optional GBrain integration to keep project-specific knowledge across sessions, plus a repo-level setup that auto-updates teammates' agent tooling to a single source of truth.
  • Cross-agent and vendor-aware: while centered on Claude Code, gstack includes adapters (OpenClaw, Codex CLI, and others) and CLIs (model benchmarking, taste profiles) to compare models and coordinate multi-agent workflows.
Who it's for and tradeoffs

Great fit if you: are a technical founder or senior engineer who wants to automate review/test/release rituals; run Claude Code (or compatible agents) and want reproducible, role-based prompts; maintain multiple parallel sprints and need browser-driven QA and persistent agent memory.

Look elsewhere or be cautious if you: cannot accept the operational surface area (it integrates a browse daemon, optional Supabase memory, and multiple agent hooks), require strict enterprise governance without additional controls, or need a turnkey SaaS — gstack is a local-first, repo-centric toolkit that expects you to manage auth, cookies, and deployment policies. It also depends on underlying agent behavior (Claude Code and other CLIs); model or provider changes can affect outcomes and require prompt/tooling updates.

In short: gstack is a pragmatic experiment in treating LLMs as modular specialists and wiring them into the engineering lifecycle. It surfaces clear productivity wins (automated QA, cross-model review, auto-updating team mode) but brings engineering and security tradeoffs you should audit before enabling in sensitive repos.

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