Two Claude Code Skills That Make Building 10x Faster
AI agents are good enough now that building is the easy part. The waste is everywhere else: your agent starts every task from scratch, and it never checks whether the thing you're asking for already exists.
Two Claude Code Skills That Make Building 10x Faster
AI agents are good enough now that building is the easy part. The waste is everywhere else: your agent starts every task from scratch, and it never checks whether the thing you're asking for already exists. Here are two skills that fix that, one for problems already solved on GitHub, one for problems only solved in research papers.
We build software, and the biggest time sink we kept hitting wasn't the building. It was everything the agent skips before it builds. A skill is just a file of instructions your agent follows as a workflow, and these two give your agent a workflow it's missing. They work in Claude Code or Codex, and the first one changed how we pull things off GitHub entirely.
The problem: your agent always starts from zero
When you get an idea, you turn on plan mode, explain it, and the agent plans how to build it. There's a huge angle you both skipped: what if what you want already exists, as a paid tool or an open-source project?
Your agent never checks. And it isn't only new projects. Say you want to add a feature to a product you already have. You tell the agent, and it starts exploring ways to solve the problem. It never looks at whether that problem is already solved and sitting on the internet, or how competitors solved it. So you spend time and tokens on a problem someone already finished.
Skill 1: Advise Project Approach
Someone built a skill for exactly this, called Advise Project Approach.
It works out where you are first. A brand-new idea and a feature added to something you've already built are different problems, so it treats them differently. New idea: it works out how the whole thing should be built. Existing product: it looks at what you've got and tells you what to keep and what to change.
It checks whether it knows enough before searching. If your idea is vague, it comes back and asks questions first: who this is for, what it needs to do for that person, and your budget constraints (can you pay for something, or do you want free only). If you've already given it that, it skips the questions. And whatever you don't answer, it doesn't quietly guess. It writes down what it's assuming so you can see it and correct it.
Then it researches something specific. It finds the paid tools that already do this and the open-source projects doing the same thing. If official docs and GitHub aren't enough, it asks whether it should also go into X, Reddit, and YouTube. In our testing it usually finds the answer fast enough not to need those.
Takeaway: Before your agent plans how to build a thing, it should check whether the thing already exists. This skill makes that the first step.
The GitHub trap it avoids
There's a trap in looking at what everyone else built: your agent goes straight for whatever's most popular, and popular on GitHub just means the most stars. Stars are a bookmark count that only ever goes up, so it tells you a lot of people liked a project at some point. It doesn't tell you it fits what you're doing.
The bigger version of that trap is the setup itself. The biggest project doing your thing is usually big because there's a whole team behind it and a lot of users, so the way they built it is right for them. Copy it and you inherit all their complicated setup and none of the reasons for it.
So the skill writes down two things for every project it finds: what actually applies to you, and what's only there because of how big they got. That's the difference between a plan built for your situation and a plan built for somebody else's company.
It checks the cost, which agents usually miss
Say you want to add DMs to your app. You don't have to build that. Services like Stream and SendBird have built the whole thing and you plug it in. But look at the cost. SendBird's free plan is around 100 users. Stream starts near $500 a month for 10,000. Past that, both charge for every extra person who opens a chat.
That cost angle is what your agent usually misses, so the skill checks cost at three separate points: while you're building it and nobody's using it, the day real people turn up, and once it grows. You find that out now instead of six months in.
Takeaway: "You can just plug in a service" is only half the answer. The skill prices it at build, launch, and scale, so cost doesn't ambush you later.
Skill 2: Neuro Archive, for problems nobody has shipped
The first skill is great when the thing already exists on GitHub. But when something is experimental and nobody has shipped it, you get stuck. That doesn't mean nobody has worked on it. The concept might already exist, just in research, not in a product.
That's where the second skill comes in, called Neuro Archive. It searches arXiv, where actual researchers publish their work. People at universities and labs whose whole job is to spend months, sometimes years, on one problem, then write down exactly what they found and what didn't work. It's all free and public, and almost everything you're building with started there. The paper behind the technology that made ChatGPT possible went up on arXiv years before there was a product.
How it runs matters. It takes your problem, picks the research areas that match, and pulls the papers. Then, instead of reading them all in one context, every paper gets its own sub-agent with a fresh context window that the main agent prompts on its own. That's one reader per paper, none of them seeing what the others read, and after all of them report, it commits to one answer.
Our own case: we were pushing files onto people's laptops automatically and had no idea what security issues that would cause. The skill pulled 12 papers, went back and forth over which one actually applied, landed on one about package systems, implemented the technique, tested it, and confirmed it worked.
How we actually used them
We wanted to build a way to publish a skill once so everyone in our community automatically gets the updated version, instead of everyone sitting on a stale copy with no idea it changed. (A community member raised this, and the concern was valid.)
We referenced Advise Project Approach and told it what we wanted. It did light research, then realized it needed to ask questions. It suggested we build for our own team first before the whole community, which was good advice. It asked which agents this was for: if it were only Claude Code, the answer already existed, because Claude Code has plugins that auto-update. But we and much of our audience use Codex too, which complicated things. It asked whether the thing would edit skills (no, only store them and send updates) and whether updates should auto-pull (pushing to the app manual, but anyone who had the skills gets them automatically). Then it wrote the project approach into a docs folder.
What it handed us was the skills CLI, a tool you run in your terminal to install skills into your agent. It also told us we didn't need a web app yet: for now, while it was team-only, the skills could just live on GitHub, and we'd revisit storage at community launch.
But one part had no prior art: automatically pushing instructions onto other people's laptops. The first skill flagged that as the risky part and then stopped, because there was nothing to point us at. That's exactly the gap Neuro Archive filled, with the package-systems paper above.
The result is a CLI tool. On our side it's two commands: point it at a skill you've got, then publish it (you can tell Claude Code to publish for you). Everyone else installs it once, it attaches to Claude Code and Codex, and their agent pulls updates in the background whenever they reopen it.
Run them together with Headstart
Running the two separately has a catch. The second one is expensive, and most of what you build doesn't need it, so you skip it. Then the one time you actually needed it is the time you didn't bother.
So we built a third skill, Headstart, that combines them. It runs the first on everything and only pulls in the second when it hits something genuinely new. In testing that's worked really well.
Takeaway: Check GitHub for what exists, check arXiv for what's only been researched, and let a combined skill decide when each one is worth running.
FAQ
What do these skills actually do?
They make your agent check whether your idea already exists before it builds. The first searches paid tools and open-source projects on GitHub. The second searches research papers on arXiv for experimental problems nobody has shipped.
Do they work in Codex, or only Claude Code?
Both. You install a skill by giving your agent the GitHub repo link and telling it to install, and it lands in your skills folder.
Why not just trust the most-starred GitHub project?
Stars are a bookmark count that only goes up, so they show past popularity, not fit. Big projects are also built for big teams, so copying them gives you their complexity without their reasons. The skill separates what applies to you from what's only there because of their scale.
When would I need the arXiv skill?
When what you're building is experimental and nobody has shipped it, but the concept likely exists in research. It reads one paper per sub-agent, then commits to a single answer.
What is Headstart?
A skill that combines the two, running the GitHub check on everything and only calling the expensive arXiv search when it hits something genuinely new.
The takeaway
Agents made building cheap, which means the waste moved upstream, into building things that already exist. These two skills put a check in front of that: does this exist on GitHub, and if not, has it been solved in research. Point your agent at what's already out there before it writes a line, and you skip the part that was quietly costing you the most.
Both skills, plus the combined Headstart skill, live inside AI Labs Pro.