superpowers VS BMAD-METHOD

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

I sometimes use Superpowers skills such as writing-plan and writing-spec. The Superpowers brainstorming skill stores design specs under docs/superpowers/specs/YYYY-MM-DD--design.md, and the writing-plans skill stores implementation plans under docs/superpowers/plans/YYYY-MM-DD-.md. (GitHub, GitHub)

obra/superpowers – GitHub

Squad coordinates parallel agents. BMAD-METHOD simulates a full agile team of specialized agents.

The GitHub repo has install instructions. My advice: start with BMM Core (the base module) and don't install everything at once. Pick a real feature on a real project and spec it before you write any code.

The closest cousin to this approach is BMAD-METHOD, which makes the same bet on partitioning AI agents by SDLC role under explicit human direction. The load-bearing difference is where the collaboration bus lives: BMAD uses Git plus markdown files in the repo, while Trail uses Plane work-items with one ticket-system account per persona — which is what makes the identity attribution mechanically enforced rather than merely by convention.

I sometimes wonder if I'm in a different universe to other devs. Anytime AI coding is brought up, comments are overwhelmingly negative and often point out correctness, quality, slop, etc.

There's also the 'more stuff is being delivered, but it's not right, full of holes and papercuts'.

I'm 22 years into development and couldn't think of going back to non AI programming now. Not only has it sped up velocity by an order of magnitude, it's also helped me unlock side projects that I would never even begin in the past as I knew I didn't have that time.

It's just like any tool though, and I've found enormous differences in outcome depending on how you drive it. Launching into 'build this' and expecting it to output code that you would manually write would not get you there; and I feel this is where most developers stall out.

Getting the right outcomes takes a lot of harness set up - the same as if you wanted to hire new devs and get them productive without peering with them. You would set up linting, good test coverage and approaches, thorough documentation about what your project is, the domain, the architecture etc. This at least gets good code consistency for the most part.

For how to build, https://github.com/bmad-code-org/BMAD-METHOD is really good and I've onboarded a few Saas projects into it now. Tech speccing and multiple cycles of elicitation are what deal with all the edge cases that you normally only encounter during coding. It does front-load all of the planning brainwork; but condensing that into a couple days of solid speccing is far more productive than spreading it out over months.

It's taken a while to get to this point, and most agents aren't good for substantial work out of the box. Most of the time what the agent does will be a product of its environment.

BMAD ("Breakthrough Method for Agile AI-Driven Development") is a different animal. It's a multi-agent framework with 43K+ stars at the time of writing — 12+ AI personas (Analyst, PM, Architect, Scrum Master, Developer, QA, UX Designer...) modeled as Markdown "Agent-as-Code" files. v6 hit stable recently after an extended alpha, with features like Scale Adaptive workflows, BMad-CORE engine, and a builder toolkit for custom agents.

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💻 GitHub

I used BMAD-METHOD for 3 months -- a persona framework that requires manual invocation (*analyst, *pm, *architect). The concept was powerful. The friction was constant. I wanted the experts to just... show up.

SPECLAN isn't the only tool exploring this space. The BMAD Method uses specialized AI agent personas for structured development. OpenSpec adds a spec layer for existing codebases. GitHub's Spec Kit provides CLI templates for spec-driven workflows. Kiro from AWS takes a steering-file approach.