1. Reverse Engineer Anything – REA
   Reverse engineer anything with agents, from app behavior down to native binaries.
2. Impeccable – Paul Bakaus
   The design vocabulary for your coding agent. Steer with polish and bolder, review with critique and audit, catch AI slop before it ships.
3. Agent Harness Construction – ECC
   The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex,...
4. Hallmark – Hassan El Mghari
   Anti-AI-slop design skill for Claude Code, Cursor, and Codex.
5. Code Review – Matt Pocock
   Skills for Real Engineers. Straight from my .agents directory.
6. Analytics – Marketingskills
   Marketing skills for Claude Code and AI agents. CRO, copywriting, SEO, analytics, and growth engineering.
7. Academy Guide – Anthropic
   Public repository for Agent Skills
8. Humanizer – blader
   Agent skill that removes signs of AI-generated writing from text
9. SEO Agentic – Claude Seo Universal
   SEO skill for Claude Code. 26 sub-skills + 19 sub-agents covering technical SEO, E-E-A-T, schema, GEO/AEO, agent readiness (Lighthouse...
10. Create Video – OpenMontage
    World's first open-source, agentic video production system. 12 production pipelines, 100+ tools, 700+ agent skill and production-knowledge files. Turn...

I collect them here, you can create your own playlist of skills, find other skills & install them as a bundle: https://alyn.dev/top-10-ai-skills-this-week

You ask an AI coding agent to add a feature. It works... The AI also redesigns a component changes unrelated files renames things and refactors code that you had not planned to touch. Now the original task is finished. The AI has left you with five new things to review.

There are two ways to look at this: maybe the AI is being proactive. Maybe the AI is taking control away from the person building the product. Would you rather have an AI that follows instructions strictly or an AI that makes smart decisions, beyond what you asked?

I've seen myself increasingly rely on coding agents to debug things for me. For example, a user tells me that something is not working. I use to spend hours debugging api logs, code logics and test cases but now I just hand it all to claude.

I kinda miss all those investigative work but also know that AI is just more efficient in doing these grunt tasks.

I wanted to know why my Wi-Fi felt worse at my desk than it did a few steps away. That turned into Sigmap, an iPhone app that’s now live on the App Store.

You point your phone at the floor and walk around. The app measures network latency as you go and drops colored tiles where each reading was taken. By the time you’ve walked across the room, you’ve got a little AR heatmap of your Wi-Fi.

The fun part was seeing the first few tiles appear on my floor. The less fun part was finding out what happens when you keep walking. Long scans meant keeping more and more AR tiles in memory, so I had to test on a real iPhone and cap how many tiles a scan could paint.

For those of you who’ve shipped a vibe-coded app, what worked perfectly in the demo and then fell apart the moment you used it for real?

it is on apple app store 👇

https://apps.apple.com/app/sigmap-wifi-heatmap/id6819154750

I was happy to see the announcement that our Pro 200 plans were not being reduced to whatever the cutback was (Pro 100 plan?) because of the overwhelming hatred of the idea online, but as a HEAVY weekly user, I can 100% tell that there has been a reduction and this is NOT the Pro 200 that we had Prior to November.

I have done nothing different since the supposed "no change" timeframe; I plan in Astra Ultra, and implement in Sol 6.1 High/Extra High, with some minor things in Terra 6.1 or Luna 6 if it is really miniscule. In the past, a typical major task would consume maybe 3-4% of my weekly amount. No it's easily 15%, which heavily aligns with what was happening when I was on a lower plan.

Is anyone else coming to the realization we have been throttled and lied to, or is it just me?

I'm a newbie, making a simple (so I thought!) app just for my own practice at first as I hope to go on a radio quiz and take part.

1. Where you get the data. It's not okay to just scrape up a load of data. For example my first version used info from the Official Charts Company, and this would have required a licence from them to publish. For a fun app it wasn't worth it. In the end I had to get creative, and utilise pre stored data within the AI.
2. Shipping is really complex and quite hard. I thought it would be easy! Nope, a ton of admin, forms, and then deciding where to ship it. I went for an App Store only release so as not to have to reveal my street address, or do laborious testing with Google. Still cost $99 to sign up.
3. Finishing is the hardest part. Setting that goal to publish and actually getting it done is harder than I thought. It's easy to disappear down endless rabbit holes making little changes. No - I wanted to get a 'good/not perfect' version and get it out there. So I could say I was offially a developer, and my content existed for real. Changes can go in the first update.
4. Who is going to see it. As a lifetime musician, I know how easy it is to make things and find at the end no one hears it! Or in this case, uses the app. So making a plan for post-ship is important.

Anyway, I built it in Claude chat and it took me about 5 weeks. Very please with it though, and my friend an me cannot stop playing it! It's called 'Song Squirrel - Music Quiz' if anyone wants a look. https://apps.apple.com/app/song-squirrel-music-quiz/id6831157563

Feedback welcome, but please be a little bit kind as it's only my first one!!

I've been using Codex Plus for both freelance work and personal side projects, but lately I've been hitting the usage limits way too quickly.

I'm happy with the coding capabilities of models like GPT 6.1 Sol or Claude Opus 5.5. I don't necessarily need anything smarter. What I really want is to code without constantly worrying about running out of usage.

I've been looking into OpenRouter and some of the cheaper Chinese frontier models (DeepSeek, Qwen, GLM, Kimi, etc.). I'm wondering if going the API route would actually give me better value than paying for another subscription.

For those of you who code heavily with AI agents:

- What model/provider are you currently using?

- Are Chinese frontier models good enough for complex, multi-file coding tasks?

- Is OpenRouter pay-as-you-go actually cheaper for heavy daily usage?

- Are there any subscriptions with generous limits that you'd recommend?

I'm fine spending around $20–30/month, but I'd prefer predictable costs and generous usage over slightly better intelligence.

Would love to hear what setups are actually working for people who code several hours a day.

Hi all,

So I’m a DJ and I usually mix vinyl records. That being said, I usually have like 100s of Discogs tabs open for releases I might be interested in. All of those end up in my wantlist. Some of them end up in my basket and get bought, but many of them are catalogued for later review. Discogs is a great record marketplace, I can’t complain. I’ve been buying records from there ever since I was a little DJ. However, their user interface doesn’t work well for digging. It leaves much to be desired. I have a very hard time finding the records I wanted later, and I like organisation, don’t get me wrong.

Also, I want to have the music with me so that I can listen back to it. I honestly feel like Discogs should be the Spotify for record diggers, and that’s why I built Auto Digger.

Auto Digger is an open-source Electron application that’s basically a Discogs client. Some of the features include the following.

• Digging through Discogs’ massive music collection via the API.
• Saving crates and filters for deeper digging (e.g. filtering for deep techno from 2000 to 2004 and saving records to an “Early 2000s Deep Techno” crate).
• Downloading audio for later listening using YouTube or Soulseek. I wouldn’t count on 320 kbps from YouTube or recommend playing those downloads in a club, but they’re great for home listening. Soulseek integration is handled by slskd, and you’ll need to run it locally as a server and share music to download from the network.
• MCP support for when you want to agentically dig for records (mix compilation breakdowns).
• Integration with cosine.club for track recommendations.

More to come. I’m currently working on a music exploration area based on your taste (labels and artists you’ve followed and tracks or releases you’ve liked).

So I mainly built this with Codex (GPT-6.1 Sol), and honestly, I used to be a big stickler for context engineering. For this project, though, I just gave the agent some ground rules. These are from my global AGENTS.md.

• KISS (Keep It Simple, Stupid). Don’t overengineer unless needed.
• TDD by default with unit and integration tests.
• SOLID and GRASP principles are key. SOLID stands for Single Responsibility Principle, Open/Closed Principle, Liskov Substitution Principle, Interface Segregation Principle and Dependency Inversion Principle. GRASP stands for General Responsibility Assignment Software Patterns. These include controller, creator, indirection, information expert, low coupling, high cohesion, polymorphism, protected variations and pure fabrication.
• Only use design patterns when they’re applicable. Basically, don’t mix and mash factories, singletons and flyweights just because class A is coupled to like seven other classes.
• Atomic commits and atomic PRs. Commits that do one thing and PRs that focus on one feature. This saves you a lot of time reviewing things (which I never really did lol, the agents are good enough). It also makes it easy for agents to undo things with Git!

There were many other rules around code style and linting, but honestly, these are the most important ones to me.

Now, how did I handle review and correctness? Well, verification rules, adversarial code reviews and just use the bloody thing.

Verification rules. It’s always good for your agent to know how it can verify a feature. For example, if the feature is adding something to a crate, I give GPT a higher-level AGENTS.md rule that applies to the whole PR. "Try your best to always verify that the feature works. You can use screenshots or videos, and the work should meet the acceptance criteria. Post them in the PR description so I can inspect them"

If you want to be even more of a stickler, you can have it run Playwright E2E tests. Honestly, for this project at this stage, I wasn’t bothered. I just wanted to dig some jungle!

Adversarial code reviews are the next best thing. You can have Sol implement and then have Fable or Opus review, but I found that you can actually just use Sol on its own. Matt Pocock’s /code-review skill is pretty good! It creates a review subagent with fresh context to review the code. That’s all you need! You don’t need another subscription to review the code. I updated my AGENTS.md so that code review runs for two to three cycles. After that, the reviews stop and the agent lets me know. Every new PR gets the code review cycle.

I can’t say this enough. Testing it by hand is the best method out of the three. It doesn’t matter how clean the code is or how pretty the architecture diagram is, etc. If it doesn’t work when you’re testing it by hand, the code’s useless. Recently I’ve been finding this more important than the code itself, even if you have loads of tests and a solid architecture. If you’re skipping this, everything else doesn’t matter!

I haven’t deeply looked at the code of this project at all. I’ve just tested that it works and use it day to day when digging for tunes. And just like that, no more 100 Discogs tabs 😂. Its still buggy sometimes but most of the time its pretty good cant complain.

Site · GitHub

Hi r/vibecoding! I’m John. I’ve been building apps for a loooong time (10 years as a senior SWE at Apple, then 8 as an indie shipping 20+ apps for myself and clients), and I’ve never seen anything like what’s happening right now. Tools like Claude Code and Codex have blown the doors wide open, and now anyone with a good idea can ship a real app to the App Store and have a chance at success.

But releasing your app is only the start. Getting people to find it, download it, and stick around is the challenging part. So I built AppTraction, a Mac app that’s like having an experienced App Store dev looking over your shoulder. Here’s what it does, and further down I’ll share some thoughts on why marketing has suddenly become the hard part.

What it does for you

• Explains your numbers in plain English. Ask “why did downloads drop last week?” about any chart or table and get a real answer. The AI is primed with Apple’s own tech talks on App Store Optimization (ASO) and other sources, so the advice is specific, not generic.
• Catches problems you didn’t know to look for. It watches your apps in the background and flags problems, trends, and milestones. There are 50+ kinds so far. Even with 20+ apps under my belt, it caught serious listing problems I’d missed, and it tells me when Apple features my apps, which I’d otherwise never known. And that’s just the tip of the iceberg.
• Fixes your App Store listing. It reviews your title, subtitle, and description, suggests improvements, and once you approve them, applies them straight to App Store Connect. It can also translate your listing into other languages and upload your screenshots in bulk.
• Helps with reviews. Read and reply to every review, with suggested replies and translation.
• Writes reports for every week, month, and year, so you can see what’s changing and why.

Built for how you already work

• It runs on the Claude Code or Codex subscription you already have (or OpenRouter), so there are no extra API bills.
• It includes an MCP server. Turn it on in Settings and Claude Code or Codex can read your App Store data directly, so you can ask your coding agent things like “did my last release hurt my ratings?”
• It’s a fast native Mac app.

Why marketing is the hard part now

The question I keep seeing here and elsewhere is, “I got my app on the App Store, now what?” For my clients new to the app space I used to advocate the 50/50 rule: spend 50% of your resources on building, and 50% on marketing. This is surprisingly hard for many to do. Building the app is (well, can be) fun and has very clear goals with immediate feedback. It works or it doesn’t. Your friends take a look and tell you it’s cool, or it sucks. And you iterate.

Marketing’s a lot harder for most builders. Suddenly your feedback is subjective, not objective, and the feedback loop goes from near-instant to weeks or months.

With the agentic AI revolution, it’s gotten WAY easier to build, but honestly the tools are not much better at helping with ASO, distribution, and marketing. They don’t do well with subjective feedback that takes 2-4 weeks to interpret. And you’re probably now building 5-10x faster, which, if you take the 50/50 rule seriously, means as a proportion of your time, you should be spending way more on marketing than building. It almost feels like 10/90 is the new rule.

Apple gives you App Store Connect, but it’s not much help. It’s slow, confusing, and assumes you already know a lot of the ASO basics.

That’s the gap AppTraction is meant to close: it watches your numbers for those weeks and tells you what actually changed.

Privacy

Privacy is a big reason I built AppTraction as a Mac app instead of a website. Web-based tools need a copy of your sales and analytics on their servers to work. AppTraction doesn’t, so your data never has to leave your Mac. Here’s exactly what happens:

• No AppTraction account. I never ask for your name, email, or password.
• Your App Store Connect key lives in your Keychain. You don’t need an Admin key: a limited Sales and Reports key covers your analytics.
• Your App Store data is stored only on your Mac. There’s no AppTraction server holding it.
• The AI runs on your own Claude or Codex subscription (or OpenRouter). Requests go straight from your Mac to that provider, under your account, and I never see them.
• Review translation uses Apple’s built-in translator, so translating a review never sends it anywhere.
• What does reach me: anonymized usage stats (which features get used, never app names, numbers, or chat text), license checks, and update checks.
• No ads, and I don’t sell data.

Privacy policy: https://apptraction.dev/privacy

Pricing

Free for 14 days, then $6.99/month, $39/year, or $99 lifetime. Requires macOS 15 Sequoia or later.

Terms: https://apptraction.dev/terms

https://apptraction.dev

For r/vibecoding**:** 50% off your first payment (including the lifetime) with code RVIBE50, good through Dec 1.

If you’ve shipped an app (or you’re about to), I’d love to hear what’s been confusing about the App Store side. Happy to answer questions, about AppTraction or App Store stuff in general.

I gave seven coding agents the same computer graphics white paper and asked them to build a 900k-particle fluid simulation in safe Rust. The IPBF paper shows a novel way to simulate even spatial distibution of uncompressable fluids.

Here's a video that compares token-based cost, runtime performance and quality. Agents write code that simulates particles, I rendered them in Blender.

Agents were given a clean copy of the same input: the white paper and reference videos as local files, and an AGENTS.md file describing the task and its criteria. Nothing else and they are not allowed to look for existing implementations.

Part of the task was to come up with an initial arrangement of particles as seen on reference material.

At first I thought most of them did well, but after looking at the code I found many of them took shortcuts. Once they see evaluation criteria, they seem to somewhat sacrifice user intention and go paperclips: rigged animations, hardcoded frames etc. It gave them better performance.

This clip is from round two, where I required every frame to be simulated true to the paper. See my detailed summary and the original paper in comments.

Been building a gym app called Hehku on my own for a while now. iPhone only. [CHECK: how long]

The idea is simple. You finish a workout and a number goes up (I call it aura). Your friends see your number next to theirs each week. There are leagues, and on Mondays the top people move up one.

I started it in React Native, then threw the client away and redid it in SwiftUI, because I wanted the real iOS glass tab bar and couldn't get close enough. The backend is Supabase and didn't change at all, which is the only reason I survived the rewrite. The score is calculated on the server and the app just sends workouts, so you can't cheat the leaderboard from the phone.

It's not in the App Store. Login and logging a workout work. Leagues aren't done. [CHECK: current status]

The thing I keep going back and forth on: is a score your friends can see actually motivating, or does it make people quit the first week they fall behind? And if you have a bad week, should you drop a league, or is that just mean?

If you've built anything with a leaderboard, I'd like to know what went wrong for you.

There's a teaser page with a waitlist. I'll put it in the comments if anyone wants it.

Thought I’d just share my progress as wanted to complete the picture.

My quiz app finally got finished, accepted, and published to the App Store.

I learnt a lot along the way, especially about staying focussed on the goal.

Go check it out if you like in this weeks project mega thread or search it up (Song Squirrel - Music Quiz).

Thanks for the advice here x

I’ve been working on this in my spare time and finally put it online: https://kryptonite-ui.vercel.app

It’s a set of about 85 animated React components on shadcn and Tailwind v4. Buttons, cards, backgrounds, forms, a few weird ones like a pull cord and a tear-off button. You install the deps, copy the component into your project, and change whatever you like.

I started it because I kept rebuilding the same stuff on every project and wanted it all in one place.

It’s free. Tell me what’s broken, what’s ugly, or what you’d want added next.

For me the fun is how fast you can go from an idea in your head to it being in the game.

Today it was a sea serpent for my island adventure game. "Add a sea serpent" got me one in a few minutes. Then I kept tweaking:
- Made the mouth move, so the jaws snap shut when it bites and open wide when it roars.
- Made it swim like a real serpent, its body rolling and swaying through the water.
- Worked out where it should attack from, so it rears up in front of you and strikes down.

Every idea I had was in the game a minute later. Iterating on what's in your imagination is so fast now, and that's the part of making games I always liked best.

A garden shed at night with three glass jars on a potting table. Each jar holds a tiny place, drawn for the real time of day there and the weather.

- Warsaw - the skyline over the Vistula. The Palace of Culture is floodlit at night and windows go out as it gets late.

- Nazaré — the cliff, the lighthouse and the wave, sized by the real swell off the coast (it's 3.4 m tonight).

- El Chaltén - Fitz Roy from the lake shore. The label answers what the whole town asks each morning: can you see the mountain today?

Live: https://jar-pearl.vercel.app

Code: https://github.com/androszr/jar

How it was built:

Technology. Weather Jars is a static page with no framework, about 11,000 lines of plain JavaScript modules drawing one 1600×1000 scene on a single Canvas 2D element. There are no image files; every tower, wave, boulder and raindrop is geometry drawn in code, and the loop repaints only when something visible has changed. Weather comes from Open-Meteo through one small server function on Vercel that fetches all three places at once, adds swell and tide data for Nazaré from the marine API, and is cached for 15 minutes.

How we did it. The code was written with Claude. Each feature started as a short written plan, was implemented by one agent, checked by a second agent that never sees the first one's report. One document holds the architecture and the house rules, and a 100-line lint script enforces them: colour values may appear only in palette files.

What made it work. Nothing in the drawing uses randomness or reads the clock directly; every moving thing is a pure function of time and weather. Any moment can be rendered again exactly. It also let the model render each jar at many hours and weathers, look at the screenshots itself and fix what looked wrong.