Turn Your AI Assistant Into a Virtual Engineering Team With gstack
Most AI coding assistants feel a bit like an extremely fast intern.
They can write code, summarize files, and move surprisingly quickly. But they can also hallucinate, miss edge cases, wander off scope, and confidently ship something that should have stayed in a branch far away from production.
That gap is exactly where gstack becomes worth a look.
Instead of treating AI like one all-purpose assistant, gstack frames it more like a structured engineering team. You get distinct roles, explicit workflows, and a repeatable rhythm that nudges the model to think, plan, build, review, test, and ship with more discipline.
In other words, less one-size-fits-all assistant, more structured workflow.
What Is gstack?
gstack is an open-source toolkit created by Garry Tan, president and CEO of Y Combinator. It is built for Claude Code first, and the repo also documents support for hosts such as OpenClaw, Codex CLI, OpenCode, Cursor, Factory Droid, Slate, and Kiro.
At its core, gstack gives your AI assistant a set of highly opinionated roles and commands that act like specialized team members. Instead of asking one assistant to do everything, you invoke purpose-built workflows for planning, design, engineering review, QA, security, release, and post-launch reflection.
That shift matters because a lot of AI coding failures are really process failures.
A lot of AI coding friction comes from role confusion. The same assistant is expected to brainstorm product direction, make architecture decisions, write UI, review code, test behavior, think about security, and prepare deployment. It can do some of that. It usually does none of it with consistent judgment.
gstack tries to reduce that by splitting the work into structured skills.
The Core Idea: One AI, Many Roles
The big idea behind gstack is simple: stop using AI as one generic helper and start treating it like a coordinated team.
The workflow follows a sprint-style rhythm:
Think -> Plan -> Build -> Review -> Test -> Ship -> ReflectEach stage has its own expectations. One role pushes on product direction. Another challenges design quality. Another looks for engineering risks. Another behaves like a paranoid QA or security lead. The result is not magic, but it is far more disciplined than dumping everything into a single prompt and hoping the model stays sharp.
That is really the appeal here.
You are not just installing prompts. You are installing a process.
What Makes gstack Different?
gstack is built around a few strong opinions about how AI-assisted software development should work.
1. It pushes for completeness
One of the ideas in the project ethos is that when AI lowers the cost of implementation, you should be more willing to aim for completeness.
That means full features, deeper test coverage, and more edge-case handling instead of stopping at the first version that merely looks done.
2. It forces research before action
Rather than jumping straight into code generation, gstack leans into planning and review first. That helps reduce the classic AI failure mode where the tool starts building too early, based on a shallow understanding of the problem.
3. It keeps humans in control
This is important. gstack is opinionated, but it is not supposed to replace judgment. The system recommends, reviews, and proposes paths forward, but the human is still meant to choose what happens next.
4. It uses role separation as a quality control mechanism
This may be the most useful part.
When one assistant tries to be strategist, product manager, designer, engineer, QA, and security reviewer all at once, the result usually gets blurry. gstack forces clearer modes of thinking by separating those jobs into distinct workflows.
That alone can make the output more consistent.
The Skills That Matter Most
gstack reportedly includes 23 skills spanning the software development lifecycle. You do not need to memorize all of them to understand the point. A few categories tell the story.
Product and Planning
/office-hoursreframes ideas, asks harder questions, and helps turn vague concepts into a design doc./plan-ceo-reviewacts like a founder or CEO, pushing on scope, leverage, and whether the idea should expand, shrink, or change./plan-eng-reviewbehaves more like an engineering manager, covering architecture, edge cases, security concerns, and testing expectations./autoplanchains multiple planning roles together automatically.
This is the part many people skip when using AI tools. They jump straight to implementation and then wonder why the output feels brittle.
Design
/plan-design-reviewcritiques designs, scores them, and calls out weak or generic UI decisions./design-htmlgenerates production-style HTML and CSS./design-reviewreviews implemented work and helps tighten it up.
The useful bit here is not just that it can generate UI. Plenty of tools can do that. The better idea is that design gets its own review layer instead of being treated like an afterthought.
Engineering and Debugging
/reviewacts like a staff engineer looking for bugs and bad assumptions that basic CI might miss./investigatebehaves more like a methodical debugger, tracing issues before rushing into a fix.
That matters because AI is often too eager. It likes fast answers. Debugging usually needs slower thinking.
QA and Security
/qaand/qa-onlyare described as browser-driven QA flows that test running apps using natural language instructions, likely leaning on tools such as Playwright under the hood depending on host setup. If that part is your main interest, these AI-powered browser automation tools are a useful comparison point./csoacts like a security lead, running through threat-modeling and common security concerns using frameworks such as OWASP Top 10 and STRIDE./browseand/open-gstack-browserextend the browser-based workflow side.
If these pieces work well in practice, they address one of the biggest weaknesses in AI-assisted development today: generated code often looks plausible long before it has actually been exercised like a real user would exercise it.
Shipping and Operations
/shipfocuses on release prep, tests, PR flow, and documentation./land-and-deployhandles merge-and-deploy style work./canarywatches for regressions after deployment./document-releaseupdates supporting docs./retroadds a retrospective layer after the work is done./benchmarkmeasures performance.
That is a more complete loop than just “generate code, then vibe check it.”
Safety Rails and Memory
/careful,/freeze, and/guardare meant to reduce destructive or sloppy actions./learnis positioned as a longer-term memory mechanism across sessions.
That kind of scaffolding helps because AI tools are often at their most dangerous when they are most confident.
How to Install gstack in Claude Code
The simplest path is through Claude Code itself.
Open Claude Code and run:
git clone --single-branch --depth 1 https://github.com/garrytan/gstack.git ~/.claude/skills/gstack && cd ~/.claude/skills/gstack && ./setup
The ./setup script handles most of the setup work. Depending on your environment, it may ask for confirmation or install dependencies.
After that, add the relevant gstack skills to your CLAUDE.md or project context so Claude knows they are available in the current project.
Once installed, you can start using commands like:
/office-hours/autoplan/review/qa https://your-app-url/ship
The key point is that these are not just aliases. They are structured skill files with explicit prompts, behaviors, and expectations.
Can You Use gstack Outside Claude Code?
Yes, but the experience depends on the host.
OpenClaw
OpenClaw can spawn Claude Code sessions, so gstack can fit into that flow once it is installed in the underlying Claude Code environment.
In practice, that means you can tell spawned sessions to load gstack and run specific workflows. For example:
- Security audit: “Load gstack. Run
/cso.” - Full feature flow: “Load gstack. Run
/autoplan, implement the plan, then run/ship.”
Cursor
Cursor is also listed in the repo’s documented host support. The setup flow uses a host flag such as ./setup --host cursor, which installs skills into the Cursor skills path.
Other Compatible Hosts
The README also documents other supported hosts including Codex CLI, OpenCode, Factory Droid, Slate, and Kiro.
This part of the ecosystem moves quickly, so the safest advice is still the least glamorous one: check the current README before treating any setup path as permanent.
Why gstack Feels Timely
The most interesting thing about gstack is not the raw number of skills.
It is the philosophy behind it.
We are moving out of the phase where people ask, “Can AI write code?” That answer has been yes for a while.
The real question now is: Can AI work like a disciplined engineering organization instead of a chaotic autocomplete machine?
That is where tools like gstack start to feel more relevant.
They are not trying to make AI smarter in the abstract. They are trying to make AI behave better by giving it structure, checkpoints, roles, and consequences.
That feels like a better direction than treating every task like a one-shot prompt.
Final Thoughts
If you build with AI regularly, gstack is worth looking at.
Not because it promises magic, and not because every workflow will be perfect, but because it pushes in the right direction. It recognizes that raw model capability is only part of the story. Process matters. Review matters. Role clarity matters. Shipping discipline matters.
That is true in real teams, and it turns out it is also true when the “team” is made of prompts, skills, and browser automation.
If you want to try it, start with the repo, install it in Claude Code, and run something simple like /office-hours on your next idea.