Superpowers vs. Gstack vs. GSD: A Complete Comparison of Three AI Agent Dev Tools

You've probably seen all three names at least once: Superpowers, Gstack, and GSD (Get Shit Done).

They all live in the "make AI development more efficient" space, and they all circulate in the Claude Code ecosystem—but if you ask "which one should I use?", you first need to understand that they solve three entirely different problems. Picking the right tool for the wrong problem won't help you.

This post's mission: clearly explain the core purpose of each tool so you can understand their differences, their ideal use cases, and when to stack them together—all in one read.

The Short Answer: Three Tools, Three Levels of Problems

Tool	Core Problem	Solution	Primary Target
GSD	You have AI tools but still can't finish tasks	Fix execution breakpoints and the completion mindset	You (the human execution layer)
Superpowers	AI agents rush ahead and produce inconsistent results	Enforce a workflow; skills govern agent behavior	The agent's process layer
Gstack	Agents use a "generic assistant" brain at every stage, diluting quality	Role switching; give AI the right cognitive mode	The agent's quality layer

These three layers don't overlap, but they can be stacked—we'll cover how at the end.

GSD: Fix Yourself First

Core problem: Why can't you finish things even with AI tools?

GSD (Get Shit Done) is not an agent framework, not a skill toolkit, and not a slash command collection. It's a mental framework for diagnosing your own execution breakpoints.

GSD's premise: most people don't see a dramatic productivity boost after adopting AI tools because the problem lives in three very specific places:

Vague task syndrome: Your to-do list is full of intentions, not actions. "Do X" is not a task. "Open this file, edit paragraph three, add these two data points, save" is. AI generates more content for you, but that content just sits at the intention layer.
High activation cost: Every AI conversation starts with a blank context—you have to rebuild the background from scratch. AI makes this problem worse, not better. You'd rather open a new chat and tackle something easy than face a task that requires reconstructing context.
Fear of finishing: The phrase "let me tweak it a bit more" has killed countless things that could have shipped. Ironically, AI makes iteration incredibly cheap—what once took 30 minutes now takes 3—so you can loop forever without ever converging.

GSD's solution is establishing a belief before any tool technique enters the picture: Done > Perfect. An imperfect finished product is more useful than a perfect plan that never ships.

Who GSD is for

People who have lots of AI tools but still have an overflowing task list
People who plan a lot but have low completion rates
People who feel busy but can't point to what they've actually finished

What GSD doesn't do

It doesn't manage how agents run or whether their output is good
It doesn't provide concrete technical workflows
It's a mindset framework—you still have to judge and execute

Superpowers: Stop Agents from Rushing

Core problem: AI agents jump straight to action and produce inconsistent results.

Superpowers is a "development workflow" system for AI agents. It consists of a set of Markdown files called Skills plus one iron rule: if there's a relevant skill, use it first; if there's a process, follow it first.

The problem it solves is concrete: an agent without a process, when asked to do one thing, will immediately start editing ten files. It might look fast, but the rework rate is extremely high. You end up spending a lot of time, the agent burns a lot of tokens, and the result is fragile.

Superpowers breaks development into fixed phases:

Explore requirements first (using-superpowers → brainstorming)—clarify "what to do" before diving in
Then write a plan (writing-plans)—big features get a roadmap before code
Then implement (optionally with test-driven-development)—define what "correct" looks like before writing to it
Finally verify (verification-before-completion)—you need evidence before claiming it's done, not a feeling

The logic behind these skills: turn good development habits from "remembered and executed" into "follow this script whenever you see this skill." Agents don't forget, and they don't skip steps—as long as the process skill is there, they follow the process.

A few Superpowers rules worth memorizing:

"This one's simple" is the most common excuse for skipping the process, and the most common source of problems.
"I explained it really clearly" doesn't mean alignment is done. A big part of what process skills do is force that alignment to actually happen.
"Superpowers slows the agent down" is an illusion. You might have more conversation turns, but overall time usually drops because you eliminate a lot of "thought it was done but it wasn't" back-and-forth.

Who Superpowers is for

Developers already using Claude Code or similar tools
People whose agents feel "hardworking but unpredictable"
People who want a repeatable, predictable development rhythm

What Superpowers doesn't do

It doesn't control what cognitive mode the agent uses—only the sequence of steps
It doesn't provide code review or QA quality gates
For people completely new to AI agents, the learning curve may be a bit steep

Gstack: Give AI the Right Cognitive Mode

Core problem: Agents use a "generic assistant" brain at every stage, diluting quality.

Gstack is a Claude Code skill suite built by Garry Tan (President of Y Combinator). Its core insight:

You can't use a single "generic assistant" mode to simultaneously execute CEO-level product decisions, paranoid-engineer code review, and QA functional testing—these cognitive modes conflict with each other. Mixing them together produces a diluted version of all three.

Gstack's solution: 9 slash commands, each corresponding to a specific cognitive mode.

Slash Command	Role	Core Question
`/plan-ceo-review`	Founder / CEO	Is this feature worth building? Is there a better solution?
`/plan-eng-review`	Engineering Manager	Architecture, boundaries, edge cases, test coverage
`/review`	Paranoid Senior Engineer	Issues that pass CI but blow up in production
`/ship`	Release Engineer	Sync, test, push, PR—end to end
`/browse`	QA Engineer (with eyes)	Click through your app in a real browser
`/qa`	QA Lead + bug fixer	Find problems, fix problems, output a health score
`/qa-only`	QA Reporter	Report only, don't touch the code
`/setup-browser-cookies`	Session Manager	Import real cookies for headless sessions
`/retro`	Engineering Manager (retrospective)	Analyze commit history and shipping velocity

Gstack's typical workflow: start with /plan-ceo-review to confirm it's worth doing → use /plan-eng-review to design the architecture → implement → /review for production-level issues → /qa for browser testing → /ship for one-command delivery.

The difference isn't how smart the AI is—it's what question it's being asked. /review systematically checks for race conditions, N+1 queries, and trust boundary violations—not because the model is stronger, but because its cognitive mode makes it ask those questions.

Who Gstack is for

Developers already writing production code with Claude Code
Independent developers or small teams who want more consistent quality assurance
People who've reached a certain scale and started worrying about "what am I missing"

What Gstack doesn't do

It's token-heavy when fully running—each role has its own context
It doesn't address the execution mindset before "should I do this at all" (that's GSD's domain)
It doesn't enforce process sequence—you have to judge when to call which skill

Direct Comparison

Dimension	GSD	Superpowers	Gstack
Problem it solves	Human execution breakpoints	Agent process discipline	Agent cognitive quality
Primary user	All AI tool users	Claude Code users	Advanced Claude Code users
When it intervenes	Before starting any work	When the agent begins working	When switching between development stages
Learning curve	Low (mindset framework)	Medium (install + habit building)	Medium (knowing when to use each of 9 commands)
Token cost	None	Medium	High (each role has independent context)
Biggest strength	Gets things actually done	Gives agents structure, reduces rework	Makes agents ask the right questions at the right time
Weakest at	Agent quality control	Role switching and quality auditing	Execution mindset and task activation

Can These Three Tools Be Used Together?

Yes—and they complement each other well, because each one operates at a different level.

Here's a typical combined use case:

Scenario: You're building a new feature

GSD (your execution layer): Use the GSD mindset to define "what does done look like?" and "what are the completion criteria?" Turn this task from a vague intention into a piece of work with a clear Definition of Done.
Superpowers (the agent's process layer): When starting a Claude Code conversation, run using-superpowers first → brainstorming if requirements are unclear → writing-plans for big features → verification-before-completion before claiming it's done. The agent won't rush, and the process is guaranteed.
Gstack (the agent's quality layer): Call the right skill at the right time—run /plan-ceo-review before confirming it's worth building, /plan-eng-review after architecture design, /review after implementation, /qa + /ship before releasing. Ask the right question at each stage.

The simplified version:

GSD checks whether you're actually ready to move → Superpowers checks whether the agent is on the right path → Gstack checks whether the agent is doing each step well enough

Where Should You Start?

If you're just getting started with AI-assisted development: start with GSD.
No matter how capable the agent is, if your task boundaries are unclear and your completion criteria are vague, what the agent produces for you is just faster wandering. Get your own execution discipline solid first, then worry about agent control.

If you already have an AI workflow but results are inconsistent: Superpowers.
Does your agent feel "hardworking but unpredictable," with lots of rework or results you can't anticipate? That's probably a process problem, not a model problem. Superpowers' skill suite makes agent behavior predictable.

If you're already writing production code with Claude Code: Gstack.
You have a process, but you're starting to worry about "am I missing something that passes CI but blows up in production?" or "is this feature actually worth building?"—Gstack's role modes are designed to fill those gaps.

If you want the full experience: use all three.
GSD manages you, Superpowers manages the process, Gstack manages quality—with all three layers in place, a solo developer can run at a rhythm that approaches what a full development team produces.

Final Thought

These three tools get compared because they all appear under the "make AI development more efficient" label—but they're actually targeting three completely different failure modes:

You can't get yourself moving: GSD
The agent went in the wrong direction: Superpowers
The agent didn't do it well enough: Gstack

Figure out which layer you're stuck at, and you'll know which tool to reach for first.