rageatc-oss

Rage Against The C - pick your own C to rage against.

Four plugins for Claude Code / Cowork, built on an idea we're using AI wrong. They slow you down on the inputs so the outputs are worth keeping.

Quick install guide

What's inside

Skills and agents to help you with your work or life. Each plugin works alone, but they fit together. Most software work uses all four. For non-coding work you will only need core and tech.

Plugin	Purpose
rageatc-core-oss	To tackle even the most challenging thinking work. Ideate, structure the problem, then solve it with an agentic loop that reviews its own outputs and learns from them.
rageatc-tech-oss	Tools that extend what your AI agent can reach: browse websites, read and write PDFs. With fallbacks when the primary tool isn't available.
rageatc-code-oss	For building software the slow way. Tests come first, architecture is designed before coding starts, work is broken into small chunks, code is reviewed. Adapts to project size, from a one-line fix to a multi-week build.
rageatc-design-oss	For when your software has a UI. Builds a design system from scratch or extracts one from existing code, then keeps the build aligned to it.

For a detailed description of what's inside and how to use it - see WHATS-INSIDE.md.

Why I built this - written by a meatbag, not an AI

Hi all,

I'm Ilya. I have decades of structured problem-solving and management experience - starting from growing up in Russia in the 90s (lots of problems to solve, trust me), to leading large guilds in online MMOs, to building and restructuring businesses, to hiring and firing people, to working in strategy consulting, to being obsessed with science and understanding how things work under the hood, including AI.

I feel we're not using AI the right way. The speed at which AI produces output has misled us into thinking that giving AI directions could be fast too. Wrong.

So I built rageatc to make AI work the way I'd work with good people on a team - briefed properly, asked good questions back, given time on the parts that matter. The frameworks behind it come from science and best practices from industries where things matter, not consulting. You've likely heard of those, but too lazy to apply them in real life (me too) - AI isn't.

Started with one skill (writing-skills) for what I actually needed. Grew one skill or agent at a time as new real problems showed up. I use it daily - not every part of it, but there is no dead weight.

I've been using it personally for almost 5 months, and that's the best thinking setup I've seen so far. I want to share it with you.

This might look overwhelming, but it is in fact simple. Use a single skill or the full stack - your way. Trust the recommended workflows the AI will guide you towards, or build your own.

Principles

1. Slow is fast - own the understanding. Working with AI is about managing others. Same as with people, rushed instructions generate low quality outputs. Time invested up front, defining the task properly, pays back many times over during execution.

2. Bad communication kills results. Applies to all of these: human-to-human, human-to-AI, and often overlooked - human-to-self. We assume the other party understands us, but that's a trick - don't fall for it. If you're into aviation, you know how many communication rules that look stupid at first glance were written in blood - and they work. Clear messaging, and clear understanding of what you actually want, is critical.

3. We don't know what we don't know. We all live in our own bubbles. You can't ask a question about something you don't know exists, and that limits what you can do. Luckily, the bubble of modern LLMs is massive - they can help you explore the unknowns. AI helps you see outside your bubble, gain fresh perspectives, even on things you thought you knew. Use humanity's knowledge to your advantage - especially from industries you're less familiar with.

4. Any computer task is doable by AI if AI is properly organised. AI solves simple, well-defined tasks reliably, especially when given a guide. Any large task can be broken down into smaller chunks. Combine these two and you get AI working reliably on pretty much anything.

5. Solve for problems that exist now, not for theoretical ones. It's extremely easy to get carried away by AI's capabilities and try to find a solution for a problem you might encounter in future. That's dangerous. My approach is to solve what I have at hand now. Makes the plugins practical - problem first, solution next.

6. Context is king. Shit in = shit out, same applies for AI. The quality of your context (the input AI gets) is the main driver of the quality of output. So protect the orchestrator's context window. Store what was produced in files, not inline. Delegate token-heavy work to subagents - and make sure the orchestrator manages them the same way a good manager would manage you.

7. AI can help you deal with AI. LLMs are great at reviewing, including stuff produced by other LLMs. They can turn the boring task of providing sufficient context into an interesting one by interviewing you. And they follow instructions much better than humans - so codify your work.

Acknowledgements

This marketplace draws on:

interface-design by Damola Akinleye (MIT) - for rageatc-design-oss
Superpowers by Jesse Vincent (MIT) - for rageatc-code-oss

Status

Active development. Issues and PRs welcome - see CONTRIBUTING.md.

License

MIT. See LICENSE.