superpowers VS spec-kit

It's crazy to me that some GitHub repos, that were just created in the last year, have more stars then some of the most popular programming frameworks out there. At the time of writing, Superpowers, is around 226,000, ahead of the Vue repo and way ahead of Next.js. A separate repo based on Andrej Karpathy's coding advice has 174,000. And they aren't even code. They're folders of markdown files that tell your AI coding agent how to write software better.

Meanwhile, the ecosystem materialized. obra/superpowers shipped a complete software development methodology built on composable skills — 1,276+ stars and growing. The cobusgreyling/loop-engineering repo cataloged patterns from Osmani and Cherny into a practical reference.

Projects like Superpowers proved that agents can follow structured methodologies — brainstorm before coding, write tests before implementation, review against specs before declaring success. The skills are mandatory workflows, not suggestions. The agent checks for relevant skills before any task.

If you're looking for complementary skills and plugins then have a look at Addy Osmani's Agent Skills or Superpowers, both provide essential coding assistant skills like Test-Driven Development (TDD). OpenSpec provides consistency, and your workflow can evolve around it.

Before any implementation, I plan. I use the brainstorming skill from Superpowers to think through the approach, then Grill Me - a separate skill that probes for contradictions, gaps, and missing assumptions, question by question. Once I'm satisfied, I save the result as a PRD and move to writing-plans (also from Superpowers), which produces a detailed implementation plan: class names, properties, architecture, tests.

These don't replace the spec; they govern how the agent acts on it. Superpowers uses guided Q&A to clarify intent, then runs sub-agents behind a verification-before-completion gate. GSD manages context in waves for solo developers. HVE Core runs an RPI loop: Research, Plan, Implement, Review.

> It would be extremely cool to be able to write one or two lines of prompt in my harness, and have a light model iterate with me a few times writing/proposing requirements, guidelines and explanations, refining the prompt until it's ready to be sent to the actual LLM.

I feel like the vast majority of AI-using coders already do this via skills suites like Superpowers (see /superpowers:brainstorming), no? https://github.com/obra/superpowers

virgin project:

1/ spec driven dev (https://github.com/github/spec-kit)

2/ then degrade to multiple sessions (no worktrees) debugging various problems until its done

The documentation from all the architecture debates I had with the AI, the back-and-forth about structure, what to build, and what to cut, is over 21,000 lines, managed through GitHub Spec Kit that tracked specs and architecture decisions as the project evolved. That ratio says something interesting about what the job actually looks like now. It was very out of my comfort zone as someone with a CS degree and a full career without any AI tools even existing. As this project took four months, I swapped my tooling quite a bit. I finalized the project leveraging Codex with the Grill Me Skill. It was a lot more reminiscent of wordsmithing an English essay than anything hands-on-keyboard, which I'm still adjusting to.

uv tool install specify-cli --from git+https://github.com/github/spec-kit.git

I am using https://github.com/github/spec-kit and am pretty content. I also tell it in the constitution.md to generate the .fsi files first, then write tests for it before implementation. That might very well be useless superstition since it is soo easy to fool yourself.

One pattern I've read about is sharing and reviewing formal work specs like https://specs.md/ or https://github.github.com/spec-kit/ INSTEAD of code review.

An optional Clarify phase sits between Specify and Plan; the agent asks the questions a human reviewer would ask before committing to an approach. The Spec Kit repo is open source, MIT-licensed, and sat at roughly 90,000 stars with active v0.7.x releases as of April 2026 (github.com/github/spec-kit).

GitHub Spec Kit README

What makes this better/different than spec-kit? It seems to have a very similar philosophy. I wonder if they could work together? Or would they just be duplicative?

https://github.com/github/spec-kit