superpowers VS agentskills

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

tl;dr - yes, the current term de jour is "context engineering", which ironically involves "skill" writing among other things. There is definitely a way to prompt/message them, such as asking questions or not cursing at them.

Read Antropic blog/docs, they have among the best knowledge share. These too:

https://agents.md

https://agentskills.io/

While tools handle actions, skills handle behavior. A skill is a reusable set of instructions or a workflow that tells an agent how to perform a specific type of task well.

There is also an Agent Skills bundle in the repo that teaches your coding agent to use the CLI properly. Drop it into your OpenCode, OpenClaw, or Claude Code skills directory and the agent learns to prefer --json, branch on exit codes, and read status --json before configuring an OpenAI-compatible client.

Here's the detail that turns a nice feature into an industry shift. The skill format isn't a Hermes invention or a Nous Research lock-in. It was originally developed by Anthropic, released as an open standard, and it has since been adopted by a genuinely surprising list of tools: Claude Code, Cursor, OpenAI's Codex, Gemini CLI, GitHub Copilot, Goose, OpenHands, Letta, and a few dozen more.

It's built as an Agent Skill, so you install it as a skill and don't touch the agent itself. It turned out better than I expected, so I open-sourced it.

Skills, the procedural memory layer. Anthropic's skills documentation covers the platform-native version. The open standard at agentskills.io makes skills portable across agents — Claude Code, Codex, Gemini CLI, the Hermes orchestrator from yesterday's piece. A skill captures a pattern you have already executed enough times to formalize.

@shopify/ucp-cli is agent-first by design. Every command takes and returns structured JSON, so a model can compose payloads and parse results without scraping human-readable output. ucp skills add installs a bundled SKILL.md — the operating manual that teaches an agent when to search versus discover, how to render totals, how to surface required disclosures, and when to hand off to a human. The full command surface mirrors the UCP shopping service:

Anthropic formalized the skills concept for Claude Code, and the format is now an open standard gaining adoption across AI coding tools. The refund scenario above could be expressed as in the following example:

An open standard. The format is published at agentskills.io, so the same SKILL.md file works across more than just Claude. Community marketplaces collectively list more than 4,000 skills as of May 2026. The official source remains github.com/anthropics/skills.

They're folders of instructions, scripts, and reference materials that Claude loads on demand. They live in .claude/skills/ and follow the Agent Skills open standard, which means they work across Claude Code, Codex, Gemini CLI, Cursor, and others.