superpowers VS rtk

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

Haven't benchmarked it, but for agentic coding e.g. Claude Code I found this useful:

https://github.com/rtk-ai/rtk

RTK filters and truncates shell output before the model sees it. AVB reported 2.5M tokens saved across coding agents in two weeks. [26] [27]

The numbers are stark. Across 2,927 real-world developer commands, RTK saved 10.3M tokens from 11.6M input tokens—an 89.2% reduction [Source]. The tool is not guessing; it is measuring.

rtk-ai/rtk (GitHub)

CLI proxy that reduces LLM token consumption by 60-90% on common dev commands

What’s really on my mind: I’m particularly curious about what it would mean to integrate features from RTK into KiroGraph. I’m not aiming for a “one tool for everything” approach, but if I had to prioritize, this is probably what I’d focus on first (spoiler here).

After test it with understand project, implement new feature, testing and commit. It is good for around 30B model. I am quite interested about understanding project, codebase and testing. So I want to do further testing with random open source repo. I choose rtk repo here RTK AI. So I clone that to my local. Then I ask it a question Check this repo and explain to me how does it work. It respond to me.