superpowers
nori-skillsets
| superpowers | nori-skillsets | |
|---|---|---|
| 71 | 6 | |
| 223,236 | 132 | |
| 22.7% | 3.0% | |
| 9.7 | 9.8 | |
| 3 days ago | 5 days ago | |
| Shell | TypeScript | |
| MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
superpowers
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Loopcraft: Stop Prompting, Start Designing Loops
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.
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From Fallacies to Superpowers: Eight Agent Skills That Make AI-Assisted Development Work
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.
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Spec-Driven Development with OpenSpec
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.
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What I Got Wrong About Claude Code (And How I Fixed 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.
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Spec-Driven Development: When Structure Helps and When It Becomes Tax
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.
- Codex app plugin integration can be better?
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Show HN: Promptloop – create, run, and improve prompt evals from the terminal
> 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
- Superpowers: An Agentic Skills Framework for AI Coding Workflows
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How I Make Claude Code's 5-Hour Usage Window Last Longer on Claude Pro
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)
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How Superpowers Forces Skill Execution
obra/superpowers – GitHub
nori-skillsets
- Show HN: Switch skills between agents, locally manage multiple configs
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Cal.com is going closed source
> My strong hunch is they are moving to closed source because it is now trivial to copy a product with AI clean rooms. Which, tbf, is a totally valid reason to move closed source.
The "clean room" part of clean-room reverse engineering implies that there is no exposure to the original copyrighted code on the part of those doing the reimplementation, whether human developers or AI. Traditionally, if you're working of the source code itself, you have one party translate the source code back into a design document, specifying behavior, and then you have another party implement that design spec with original code.
If you already have a running copy of the software to model the behavior off of, then you don't need the original source code in the first place. So going closed source will have zero effect on the capacity of AI tools to be used for clean room reverse engineering: all you need is the runtime.
> But I'd want to see more adoption of something like the Ship of Theseus license (https://github.com/tilework-tech/nori-skillsets/pull/465/cha...) before giving up on open source entirely
This license doesn't seem valid: a license can't redefine what qualifies as a derivative work. That's determined by copyright law itself, and if copyright law says that a clean-room reimplementation isn't a derivative work, then it isn't restricted by copyright, so doesn't need a license in the first place.
- Show HN: Ship of Theseus License
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Ask HN: How can I get better at using AI for programming?
Nori uses Claude Code's skills extensively, which you can see here: https://github.com/tilework-tech/nori-profiles/tree/main/src...
We use Claude Code's ability to use skills by defining a bunch of really useful and common skills that are necessary for writing software. For e.g. brainstorming, doing test driven development, or submitting a git commit.
The specific skills you linked are interesting demos of what you can do with skills! But most of them are not useful for the day to day of building software
What are some alternatives?
BMAD-METHOD - Breakthrough Method for Agile Ai Driven Development
svelte-bench - An LLM benchmark for Svelte 5 based on the OpenAI methodology from OpenAIs paper "Evaluating Large Language Models Trained on Code".
spec-kit - 💫 Toolkit to help you get started with Spec-Driven Development
coding-clippy
claude-code - Claude Code is an agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster by executing routine tasks, explaining complex code, and handling git workflows - all through natural language commands.
para-speak - Local fast speech-to-text transcription made easy.