Hi all. Out of respect to other humans this is written by a human. You all should take an Uber to get to the carwash. My name is Ilya and I want to share my ecosystem of skills and agents ( and a couple of rules + hooks ) that I’ve built for myself over the past 5 months because I wasn’t happy with anything that the market currently offers. I use it on daily basis, and it only contains stuff that I needed to solve problems I faced, and I’m super happy with how it works. Quick context: currently I work in strategy consulting. But I got lucky enough to get consistent exposure to managing people for over 20 years. Running my own business, turning around others’ businesses, playing colony management games , managing consulting teams, and most importantly - managing a mid-sized guild in an MMO (if you’ve done this you know). I am not a software engineer, although I do code a bit. The main idea was to organise AI in a way I would organise a team of very capable people. So this is mostly for thinking work , including coding, not just for coding.
Why slow AI gives us speed. It’s good, but the flip side - it’s bad in some situations, and I see that many people miss it entirely. AI is great at following directions. If the direction is wrong because you rushed it, the wrong thing gets executed very quickly. The fix is unsexy and requires patience: spend time on the brief upfront, make the AI push back when something doesn’t make sense, then check what came out before stacking the next step on top. Feels slower, is slower at first. But you end up with what you actually wanted instead of another slop-fest, so it’s net faster eventually.
The 7 principles I’ve built this on Slow is fast - to own the understanding you can’t rush Bad communication kills results (human-to-human, human-to-AI, and human-to-self - we’re often misleading ourselves thinking that we know what we want) We don’t know what we don’t know - AI must help you to see outside of your bubble Any computer task is doable by AI if AI is properly organised - tasks are small enough, well defined, and well assessed Solve for problems that exist now, not theoretical or aspirational ones, to stay focused (and save tokens) Context is king - shit in, shit out AI can help you deal with AI - especially by doing the boring organisational work for you
Two examples of how it works to start with /shaping - my most-used skill. It’s a small workflow where orchestrator uses 3 underlying skills in a dialogue mode and helps me to frame the problem depending on where I am in my understanding of it. It solves multiple problems - more often than desired, I think I know what the problem is, but in reality the problem is somewhere else. Often, it helps me to find a better (and simpler!) solution. This is somewhat similar to why companies pay for consulting - because they know that finding the right question is 90% of the answer. This is, as you guessed, slow - but it helps to improve defining the direction for work. Which is a big deal in management, including managing AI. /critic - this is when it comes to comparing what was produced to what was intended. It invokes a subagent, that is taught to assess the quality of stuff produced. It then gives an actionable unbiased feedback. Obviously, if the direction was wrong, there won’t be much value in it, but when the direction is right - it does miracles for me. Works best for non-code artefacts (PRD, architecture, skills, slides, written documents). Together they bracket the work - shaping at the start to figure out what’s actually being asked, critic at the end to check the output matches it.
What’s in it Four plugins (title is a bit misleading for controversy, sorry), MIT. Each works alone, but they compose:
- rageatc-core - thinking infrastructure. Ideation, understanding, solutioning, briefing, research, producer-critic-learner loops, writing skills, persuading. The most-used plugin.
- rageatc-tech (small one) - a bit of extra tools the agent can reach: browse, PDFs, with fallbacks when primary tools aren’t available.
- rageatc-code - software building the slow way. An improved version of Superpowers by Jesse Vincent embedded in my workflow. TDD enforced, architecture before code, scale-adaptive. Heavy on persistent project knowledge - PRD, architecture, roadmap, orchestration plan.
- rageatc-design - design systems for UI work. Greenfield or extracted from existing code. This is an amazing interface-design by Damola Akinleye embedded in my workflow. Most software work uses all four. Non-coding work usually only needs core and tech.
vs Superpowers rageatc-code draws heavily from Superpowers by Jesse Vincent - TDD enforcement, worktree isolation, verification discipline. What rageatc-code adds on top: persistent project knowledge (PRD, architecture, roadmap that survive sessions), scale-adaptive workflow (matches rigour to project size), and tight integration with rageatc-core’s upstream thinking pipeline. When to pick which: Superpowers if pure coding workflow with strong TDD bias. Rageatc if you want the thinking work upstream of coding done with the same discipline.
Who this is NOT for Not for quick-and-dirty work, vibe coding, or pure code-only workflows. Also not for anyone optimising hard for token spend - this system is opinionated about context, not stingy with it. Also if you have no patience, that’s a bad choice. If any of those describe you, this won’t fit. If you want AI working seriously on serious things, give it a look.
Repo: https://github.com/isvlasov/rageatc-oss (install guide at the top) If you want a low-commitment taste, install rageatc-core and try /shaping next time you start something. That’s the most representative entry point. Happy to answer questions in comments. submitted by /u/Porkribswithcoleslaw
Originally posted by u/Porkribswithcoleslaw on r/ClaudeCode
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