Claude Code Never Has To Build From Scratch Again

AAI LABS
Computing/SoftwareSmall Business/StartupsInternet Technology

Transcript

00:00:00AI agents have gotten really good now.
00:00:02Building is the easy part of making something now.
00:00:04The problem is that whenever you ask your agent to do something,
00:00:08it has to start from scratch.
00:00:09In order to fix this problem, we've found two skills,
00:00:12which are basically just files with instructions written in them
00:00:15so that your agent can follow a workflow.
00:00:17So in other words, we've got two workflows
00:00:19that basically allow you to build faster with your AI agent,
00:00:22whether you are using cloud code or codex.
00:00:25And the first skill also reveals a really interesting thing about GitHub,
00:00:28which changed the way that we download stuff from it now.
00:00:31So if you're new to the channel, then welcome.
00:00:33We're a software company and this is AI Labs,
00:00:35where we show you how to optimize your workflows with AI
00:00:38using proven methods from our own team.
00:00:41And in this video, we're going to go over both these skills
00:00:43and look at how they enable you to build faster.
00:00:46Before we actually introduce the skills,
00:00:48we need to take a closer look at the problem.
00:00:50So whenever you get a new idea
00:00:52or you want to build something to solve your problem,
00:00:54you go to your agent, turn on the plan mode
00:00:56and you explain your idea to it.
00:00:58It plans for a while
00:00:59and then it tells you how it's going to build what you want.
00:01:01But there is a huge angle
00:01:03that you and your agent have completely missed.
00:01:05What if what you want to build already exists,
00:01:08whether it's a paid tool or an open source one?
00:01:10Your agent never checks
00:01:11if the thing that you want already exists or not.
00:01:14And this doesn't only happen
00:01:15if you're starting a new project.
00:01:17Let's say that you already have a product
00:01:19that you are working on
00:01:20and you decide that you want to add a new feature in that product.
00:01:23You're going to open up your coding agent
00:01:25and tell it about this new feature,
00:01:27but it's just going to start exploring ways
00:01:28that the problem can be solved.
00:01:30It never looks if that problem is already solved
00:01:32and put out on the internet.
00:01:33It never looks how other competitors have solved that problem.
00:01:36This is a serious problem
00:01:38because you're spending all that time
00:01:39and tokens on a problem that has already been solved.
00:01:42So someone built a skill to solve this exact problem.
00:01:45The skill is called advise project approach.
00:01:48And the first thing it does
00:01:49is that it works out where you actually are.
00:01:51Because telling it about a brand new idea
00:01:53versus telling it about a feature you want to add
00:01:56to something you've already built
00:01:57are two completely different problems
00:01:59and it treats them in different ways.
00:02:01If it's a new idea,
00:02:02it works out how the whole thing should be built.
00:02:04But if you've built something already,
00:02:06it looks at what you've already got
00:02:08and tells you what to keep and what to change.
00:02:10But it doesn't go straight to searching either.
00:02:12Before it looks anything up,
00:02:14it checks whether it even knows enough about what you're doing.
00:02:16If your idea is still vague,
00:02:18it comes back and asks you some questions first.
00:02:20For example, who this is actually for,
00:02:22what does it need to do for that person
00:02:24and what your budget constraints are,
00:02:26can you actually get something paid
00:02:27or do you want to stick to a free option?
00:02:29And if you've already given it all of that,
00:02:31then it just skips the questions entirely.
00:02:33And whatever you don't answer,
00:02:35it doesn't quietly guess either.
00:02:36It writes down what it's assuming
00:02:38so you can see it and tell it where it's wrong.
00:02:40So by the time it actually starts researching,
00:02:42it's looking for something specific.
00:02:44It goes and finds the paid tools that already do this
00:02:47and the open source projects doing the same thing.
00:02:49But if official docs and GitHub
00:02:50aren't going to be enough on their own,
00:02:52it comes back and asks whether it should go into X,
00:02:55Reddit and YouTube as well,
00:02:56where it can get additional information.
00:02:58With the time that we've spent with it,
00:03:00testing it in different places,
00:03:01it doesn't usually need to use those sources
00:03:03as it finds the answer pretty quickly.
00:03:05But there's a trap in looking at what everyone else has built.
00:03:09Your agent goes straight for whatever's most popular.
00:03:11And popular on GitHub basically means
00:03:13whichever project has the most stars,
00:03:15which is a bookmark count that only ever goes up.
00:03:18So it tells you a lot of people liked it at some point.
00:03:20It doesn't tell you it fits what you're doing.
00:03:22And the bigger version of that is the setup itself.
00:03:25The biggest project doing your thing is usually big
00:03:28because there's a whole team behind it
00:03:30and a lot of people using it.
00:03:31So the way they built it is right for them.
00:03:33If your agent just copies that,
00:03:35you get all of their complicated setup
00:03:37and none of the reasons for it.
00:03:38So the skill makes itself write down two things
00:03:41for every project it finds.
00:03:43What actually applies to you
00:03:44and what's only there because of how big they got.
00:03:47And that's the difference between a plan built for your situation
00:03:50and a plan built for somebody else's company.
00:03:52And then there's the money.
00:03:53Let's say that you want to add DMs to your app.
00:03:55You don't have to build that.
00:03:57There are services like Stream and Sendbird
00:03:59that have already built the whole thing
00:04:00and you just plug it into your app.
00:04:02But then look at what it costs.
00:04:04Sendbird's free plan is about 100 users.
00:04:06Stream starts at around $500 a month for 10,000.
00:04:09And past that, on both of them,
00:04:11you're paying for every extra person who opens a chat.
00:04:13It's this cost angle that your agent usually misses.
00:04:16This is why the skill has to check
00:04:18what it costs you at three separate points.
00:04:20First, while you're building it and nobody's using it,
00:04:22then the day real people actually turn up
00:04:25and then once it grows.
00:04:26So you find that out right now instead of six months in.
00:04:28But before we move deeper into this,
00:04:30it would be great if you subscribe to the channel
00:04:32and hit the hype button.
00:04:34This small gesture of support goes a long way for us.
00:04:36So before we jump in and see how the skill actually works,
00:04:39you need to understand the problem
00:04:41that we needed to solve here.
00:04:42Skill updates only go one way.
00:04:44We change something and the person who downloaded it
00:04:46has no idea.
00:04:47Their copy just stays the way it was.
00:04:49So we needed something of our own
00:04:51where we could publish once
00:04:52and then everyone who's part of our community
00:04:54gets access to the updated version.
00:04:57The update would be sent out to our own team first
00:04:59and then to the whole community as well.
00:05:01This problem was actually highlighted
00:05:03by someone in the community
00:05:04and their concern was actually valid here.
00:05:06So to install the skill,
00:05:08you just need the link to the GitHub repo,
00:05:10which we're going to paste in the description below.
00:05:12And you can go inside whatever agent you're using,
00:05:15paste that link in and tell it to install the skill.
00:05:17We're using Claude code here.
00:05:18So if I look inside the .claude folder
00:05:20and go inside skills,
00:05:22you're going to see that the advise project approach skill
00:05:24is sitting right there.
00:05:25After installing it,
00:05:26we referenced the skill and told it what we wanted.
00:05:29We wanted to build a web app
00:05:30in which we could drop our skills
00:05:32and anyone who's downloaded them onto their laptop
00:05:34automatically gets those updates
00:05:36without having to worry about
00:05:38whether the one they've got is still the current one.
00:05:40And so it started researching.
00:05:42It started doing some minor research
00:05:43and after that,
00:05:44it realized that it needed to ask us some questions.
00:05:47And then first of all,
00:05:48it asked us who it was for.
00:05:49It gave us numerous options
00:05:51and it gave us good advice
00:05:52that before we actually implemented it
00:05:54for the full community,
00:05:55we could build it for our own team
00:05:57because our own team does use internal skills as well.
00:06:00So this is why we told it
00:06:02that we needed to build it for the team right now.
00:06:04And then another thing that made it more complex
00:06:06was which agents this project was supposed to be for.
00:06:09So if it was only supposed to be for Claude Code,
00:06:11it had found out that the solution was already there
00:06:14because Claude Code has something called plugins
00:06:16and those would get updated automatically.
00:06:18But since a lot of our audience
00:06:20and we ourselves use Codex as well,
00:06:22this is why we included other agents as well,
00:06:24which complicated things.
00:06:25And then it asked whether the thing that we were building
00:06:28was actually going to be used to edit the skills.
00:06:30And we clarified that no,
00:06:32it was only supposed to store the skills
00:06:33and send updates to the people
00:06:35who had those skills in the first place.
00:06:37And it basically did more research
00:06:39and then it confirmed whether we wanted the updates
00:06:41to be automatically pulled to the device.
00:06:43And we clarified that yes,
00:06:45pushing to the app should be manual
00:06:46whenever we felt that the skills needed an update,
00:06:49but anyone who had those skills
00:06:50should automatically get them.
00:06:52And it made this docs folder
00:06:53and inside that docs folder,
00:06:55it was actually writing that project approach
00:06:58But the approach that it took had some flaws
00:07:00that it couldn't figure out how to fix them on its own,
00:07:03which is why we needed to use another skill.
00:07:05But before we look at that second skill,
00:07:07let's have a word by our sponsor.
00:07:08Lovable.
00:07:09Check this self-checkout e-commerce store
00:07:11for watches we made with just a single prompt
00:07:13and after approving a plan on Lovable.
00:07:15We started with explaining our app
00:07:17with a prompt in plan mode.
00:07:19First, it asked further questions
00:07:20about the app we're building.
00:07:21Then it proposed a plan
00:07:22where the whole app's details
00:07:24and the decisions it made are verified.
00:07:25And after some time, we got our store's first view.
00:07:28Lovable also provides its MCP server,
00:07:31which can be connected to any AI agent
00:07:33and you can build using cloud code, cursor, or codex.
00:07:36Payments are usually where AI builders stall,
00:07:39but Lovable sets it up and handles the global tax,
00:07:41letting the developer test safely
00:07:43before the app goes live.
00:07:45Therefore, you get from an idea to a live app
00:07:47that takes real orders from customers.
00:07:49So start building with Lovable
00:07:51using the link in the description below.
00:07:53Before we move on to that skill,
00:07:55let me explain why we even need a second skill
00:07:57when the first one just handed us the whole plan.
00:07:59What the first skill came back with
00:08:01was the skills CLI.
00:08:03That's a tool you run in your terminal
00:08:04to install skills into your agent
00:08:06and it had figured out a lot of the parts
00:08:08that we wanted here.
00:08:09It also told us that we didn't need
00:08:10to make a web app for it
00:08:11and there was a better approach to this.
00:08:13It said that for now,
00:08:14while we perfected the app
00:08:16and made it just for the team,
00:08:17we didn't need to build something for the storage.
00:08:19The skills could be stored on GitHub for now.
00:08:21When we would launch it for the community,
00:08:23we would look at the storage problem again.
00:08:25As we already said,
00:08:26most of what you want to build
00:08:27has already been built by somebody else
00:08:29and it's sitting on GitHub
00:08:30and that's exactly what the first skill
00:08:32is good at going and finding.
00:08:34But when the thing is experimental
00:08:35and nobody has shipped it yet,
00:08:37you get stuck.
00:08:38But that doesn't mean nobody has worked on it though.
00:08:40The concept might already be there.
00:08:42And a small part of our app had that problem.
00:08:44What we're actually building
00:08:45is something that automatically pushes instructions
00:08:48onto other people's laptops
00:08:49and the first skill flagged that
00:08:51as the part to be careful with.
00:08:52Then it stopped
00:08:53because there was nothing out there
00:08:54for it to point us at.
00:08:56So that's where the second skill comes in.
00:08:57It's called Neuro Archive.
00:08:59It helps by searching a platform called Archive.
00:09:01It's where actual researchers publish their work.
00:09:04People at universities and labs
00:09:05whose entire job is to spend months,
00:09:08sometimes years on one specific problem
00:09:10and then write down exactly what they found
00:09:12and what didn't work.
00:09:13Other researchers then read that and pull it apart.
00:09:16And all of it is free and public.
00:09:17Almost everything you're building with right now
00:09:19started there.
00:09:20The paper behind the technology
00:09:22that made ChatGPT possible
00:09:23went up on Archive years before there was a product.
00:09:26So when you're building something experimental,
00:09:28that's where the concept is already sitting.
00:09:30The way the skill runs
00:09:31is that it takes your problem
00:09:32and picks the research areas that actually match it.
00:09:35Then it pulls the papers
00:09:36and the way it reads them is the part that matters.
00:09:39It doesn't read them all in one go.
00:09:40Every paper gets its own sub-agent
00:09:42which is basically another agent
00:09:44that your main agent can start
00:09:46with a fresh context window
00:09:47and the main agent can give it a prompt by itself
00:09:50so you don't need to do anything.
00:09:51That's one reader per paper
00:09:53and none of them can see
00:09:54what the others are looking at
00:09:55and after reading all of them,
00:09:57it commits to one answer.
00:09:58So the problem that this skill solved
00:10:00was that we were pushing files
00:10:01onto people's laptops automatically
00:10:03and we had no idea
00:10:04what security issues this was going to cause.
00:10:06It pulled 12 papers
00:10:08and then it went back and forth
00:10:09over which one actually applied to us
00:10:11and it landed on one.
00:10:12A paper about package systems
00:10:14and it implemented the technique
00:10:16that the paper talked about,
00:10:17tested it and made sure that it was working.
00:10:19So just like the previous skill,
00:10:21we gave it a link
00:10:22and it installed it in our skills folder
00:10:24as you can see right here.
00:10:25Then it loaded the skill
00:10:26and as you can see,
00:10:27it spawned sub-agents
00:10:28and they all read each paper one by one.
00:10:31And after implementing the fix,
00:10:32we ended up with a working version of our tool.
00:10:35It's a CLI tool
00:10:36which just means it runs inside the terminal.
00:10:38And since these coding agents
00:10:40already run in the terminal themselves,
00:10:42they can use it directly
00:10:43and do the whole thing on their own.
00:10:45So here's how the thing actually works.
00:10:46On our side, it's two commands.
00:10:48You point it at a skill you've already got
00:10:50and then you publish it.
00:10:51You can actually tell Claude Code to publish it
00:10:53and it would be able to help you with that.
00:10:55And everyone else's agent
00:10:56pulls it down in the background
00:10:58whenever they reopen their agent.
00:11:00Everyone just needs to install it on their laptop
00:11:02and it automatically attaches to Claude Code and Codex
00:11:05and just does the work.
00:11:06So right now we're using both of these separately
00:11:09and we'll link both of them down below.
00:11:11But running them separately has a problem.
00:11:13The second one is expensive to run
00:11:14and most of what you're building doesn't need it.
00:11:16So you don't run it.
00:11:17And then the one time you actually needed it
00:11:19is the time you didn't bother.
00:11:20So we've built a separate skill named Head Start
00:11:22which basically combines the two.
00:11:24It runs the first one on everything
00:11:26and it only pulls in the second one
00:11:27when it hits something that's actually new.
00:11:29And in our testing that's worked really well.
00:11:31That one's in our community AI Labs Pro.
00:11:34That's also where you'll find other skills and resources
00:11:37that we show you in our videos
00:11:38along with a place to interact with our team.
00:11:41So if you found value in what we do
00:11:43and want to support the channel
00:11:44this is the best way to do it.
00:11:46The link's in the description.
00:11:47That brings us to the end of this video.
00:11:49If you'd like to support the channel
00:11:50and help us keep making videos like this
00:11:53you can do so by using the super thanks button below.
00:11:55As always, thank you for watching
00:11:57and I'll see you in the next one.
00:11:59Thank you.

Key Takeaway

AI coding agents eliminate redundant development by integrating the 'advise project approach' and 'Neuro Archive' skills to evaluate existing software and academic research automatically.

Highlights

  • Coding agents waste time and tokens by starting from scratch instead of checking existing paid tools or open-source projects.

  • The 'advise project approach' skill identifies whether a new idea or a feature addition is being built and audits cost at three distinct lifecycle stages.

  • Popularity on GitHub is measured by stars that only increase, which reflects past interest rather than current relevance to a specific project.

  • The 'Neuro Archive' skill searches academic preprints on arXiv and assigns one sub-agent with a fresh context window to read each retrieved paper.

  • The 'Head Start' skill combines the project approach and the archive researcher into a single workflow that pulls academic literature only when encountering novel technical problems.

Timeline

The Root Problem of AI Coding Agents

  • AI agents build solutions from scratch without checking if open-source alternatives or paid tools already exist.
  • Starting a new project versus adding a feature to an existing product requires distinct problem-solving approaches.
  • Agents waste developer time and token budgets by exploring solutions to problems that are already solved on the internet.

Coding agents operate in plan mode to outline development tasks, but completely miss existing market solutions. This inefficiency occurs both when launching a new project and when integrating features into an active product. Consequently, agents spend substantial time and tokens solving problems that competitors or open-source contributors have already solved.

Evaluating Projects and Costs

  • The 'advise project approach' skill determines the current project state and asks clarifying questions before initiating research.
  • GitHub stars reflect historical popularity rather than technical suitability for a specific use case.
  • The skill audits service costs across three distinct stages: initial building, launch day, and subsequent growth.

The 'advise project approach' skill checks whether an idea is vague and asks targeted questions about target users and budget constraints before searching official documentation and GitHub. It forces the system to distinguish between features required for a specific project and unnecessary complexity inherited from large teams. Additionally, it evaluates cost structures for third-party services like Stream or Sendbird across multiple growth phases.

Installing and Testing the First Skill

  • Skills install into coding agents by pasting a GitHub repository link into the agent configuration.
  • The installed skill creates a documentation folder to outline the project approach.
  • Integrating support for both Claude Code and Codex introduces architectural complexity during multi-agent development.

Users install skills by referencing a GitHub repository link inside the agent interface, placing the files directly into the local skills directory. When building a tool to synchronize skill updates across devices, the agent analyzes team-specific requirements and multi-agent compatibility. However, purely architectural planning falls short when addressing experimental features without existing online implementations.

Deep Research with Academic Papers

  • The 'Neuro Archive' skill searches arXiv to find academic papers published by university researchers and labs.
  • Each retrieved paper receives a dedicated sub-agent with a fresh context window to analyze findings independently.
  • Automated package distribution security issues are resolved by implementing techniques sourced from academic literature.

When an experimental feature lacks existing implementations on GitHub, the 'Neuro Archive' skill queries academic research papers on arXiv. The main agent spawns independent sub-agents, assigning one reader per paper to maintain isolated context windows. This rigorous literature review approach enables the agent to identify applicable security patterns, test them, and implement verified fixes.

Unified Workflows and Community Resources

  • The 'Head Start' skill combines project advice and academic paper research into a single automated pipeline.
  • The combined skill runs standard project planning by default and invokes academic searches only for novel problems.
  • Community resources and advanced skills are accessible through the AI Labs Pro platform.

Running the project approach and archive search skills separately is inefficient because deep academic research is expensive and rarely needed for standard tasks. To solve this, the 'Head Start' skill merges both workflows, executing the lightweight project evaluation first and triggering archive searches exclusively when hitting genuinely new technical barriers.

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