Pratique du Shadowing: 5 Claude Code skills I use every single day - Apprendre l'anglais à l'oral avec la vidéo
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239 phrases
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I've been an engineer for nearly a decade and in all of that time, right now, process has never been more important.
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At your fingertips now, you have access to a fleet of middling to good engineers that you can deploy at any time.
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But the weird thing about these engineers is they have no memory, they do not remember things they've done before,
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and so you need extremely strict and well-defined processes to get those agents to actually do things that are useful.
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So this means that you as a developer are looking constantly for ways to steer your agents, to keep them on the right track.
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And for me that has resulted in a lot of skill building.
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Here is the repo of all the skills that I'm using right now, each of which I have gone through and designed.
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Some of these I use relatively rarely, but some of them I use every single day.
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And these skills help me encode my process so
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that AI has a really strict path it can walk down every single time.
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And as a result of using all of these skills, the code quality that the AI is producing has shot up.
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Now if you think that process is important and
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that real engineering skills are important then boy do I have a course for you.
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This course is called Claude Code for Real Engineers.
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It's a two week cohort that starts on March 30th and for seven more days it is 40% off.
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If you feel like you're behind the curve on Claude Code
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and you want to get way ahead of the curve in just two weeks then blimey this is the place for you.
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But let's start talking about our skills with number one which is maybe my favourite.
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This This is the grill me skill.
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This skill, yes, it is just three sentences long.
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And let's just read it out in full to describe what it does.
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Interview me relentlessly about every aspect of this plan until we reach a shared understanding.
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Walk down each branch of the design tree, resolving dependencies between decisions one by one.
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And finally, if a question can be answered by exploring the codebase, explore the codebase instead.
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The concept of a design tree comes from this book by Frederick P.
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Brooks, which is the design of design.
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Actually, I don't know if it comes from this book, but this book is where I saw it first.
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The design tree is this idea
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that as you're coming towards a design you need to walk down all of the branches of a design tree.
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For instance, you might be designing a search page
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and you need to decide whether you want an advanced search or a text box.
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If you choose advanced search then you need to figure out all of the filters
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and all of the sorting methods that you need on advanced search.
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And you keep on walking down the tree until you figure out your design kind of in full
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or as full as you can before actually committing to code.
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This grill me skill, when I invoke it, I invoke it when I want to reach a shared understanding with the LLM.
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I found that relatively recently Claude Code will tend to just spit out a plan really early
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when I go in plan mode, and it tends to just create a document before I feel I've reached a shared understanding with the LLM.
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But the grill me skill forces that conversation, it forces the LLM to interview me about every single part.
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Here's a conversation I had with Claude recently about adding a feature to my course video editor codebase.
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I gave it some research that I'd done in a markdown file and I said grill me, I'd like to think about adding this to the right page.
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It loaded up the skill and the thing I want to show you is just how many questions it asked me.
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So the first thing it did is it just explored the relevant stuff in the codebase, which is good.
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And we zoom down, we can see it asks question 1, where does the document live?
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Question 2, what's the UI layout?
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Question 3, which modes get the document panel?
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Question 4, the document lifecycle?
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Question 5, what does the write document tool look like?
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Question 6, the edit tool shape?
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7, question all the way down to question 9, question 10, question 11, question 12, all the way down to question freaking 16 here.
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And this is a relatively short grilling session in my book.
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I've had sessions where I've sat there for nearly half an hour, 45 minutes with the AI answering questions on really complex features.
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You know, that could be 30, 40, 50 questions, all from this absolutely tiny skill.
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That's one thing I want you to take from this.
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Skills don't have to be long to be impactful.
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You've just got to choose the right words for the LLM at the right time
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and this design tree resolving dependencies has just been absolutely great for me.
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By the way, if you want these skills then they will be at a link below.
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Once I have reached a shared understanding with the LLM, once I have grilled my idea and sort of understood all of its ramifications,
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if I then decide I want to implement it then I invoke my next skill which is a write a PRD skill.
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I actually did this in the conversation we were just looking at.
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So it said anything I've missed or got wrong and I said write a PRD.
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I was suffixing it with user because I have some
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that sort of live in the project so that's the reason why I did that.
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Here's what the skill looks like.
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This will be invoked when the user wants to create a PRD.
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You may skip steps if you don't consider them necessary.
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So for instance in the previous conversation it said we've already done a deep interview let's move to step four.
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So step one is to ask the user for a long detailed description.
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Number two is to explore the repo to verify their assertions.
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Number three is basically to interview the user relentlessly.
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So just a copy of the grill me skill again.
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Next we sketch out the major modules you will need to build or modify to complete the implementation.
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We're going to look at this later
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because it links to skills I'm going to show you in a bit in this video.
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And finally once you have a complete understanding of the problem and the solution, use the template below to write the PRD and the PRD should be submitted as a GitHub issue.
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The way that my dev flow works is I take these PRDs in GitHub.
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I turn them into more GitHub issues that reference the parent PRD.
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And then I have a Ralph loop that just loops over each issue until it's done.
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If we go back to the conversation where we were before, we can see that it created this PRD here.
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This was four days ago, as you can see.
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We've got a problem statement.
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The article writing page currently regenerates the entire document on every AI interaction.
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And the solution was to add a split pane document editing experience to the article writer.
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Chat stays on the left, a new document plan, blah, blah, blah.
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So this is a big feature.
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We're adding document editing to a kind of AI chat feature.
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The important thing here is the user stories.
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There are many, many user stories as part of this.
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And this comes from Agile methodology.
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And we're basically trying to describe the kind of desired behavior of our system in language, which is not an easy thing to do.
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I still haven't properly like landed on the right format for these.
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This is just something I sort of like.
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But you could easily use like Cucumber Language for these or whatever you're kind of used to do, used to working with.
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We then zoom down to the bottom and we just sort of pass in some implementation decisions.
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The implementation decisions here, we don't want to be like over prescriptive because we want these to be durable.
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Because if the code ends up getting out of date with the PRD, then we're going to have issues when we actually go to implement it.
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But you can see the theory here.
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This is the kind of, it's a really good description of the destination that we're going to.
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But what we don't have from the PRD is the actual journey, is the way we're going to get to this destination.
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And if we leap back to that conversation, this is where I use my next one, which is PRD to issues.
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What this does is it takes a PRD, takes the destination, and it turns it into a Kanban board of different issues that can be independently grabbed.
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So the first step in here is it locates the PRD.
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If the PRD is not already in your context window, fetch it with this instruction.
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Explore the code base if you need to.
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And then, oh, oh, oh, oh, draft vertical slices.
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It's not always clear how you should break a PRD down into individual tasks.
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This is something that developers have been doing for Yonks, right?
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And we've developed a kind of intuition for how to do it.
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In my opinion, the best way to do it is to break it into tasks
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that flush out the unknown unknowns really quickly.
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For instance, if you're integrating with a new kind of service or integrating two things which you haven't integrated before,
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then you should do that work first because it's going to give you feedback on whether your approach is even valid.
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The right analogy here is the tracer bullet analogy.
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I won't go into what that means but basically each issue is a thin vertical slice that cuts through all integration layers, not a horizontal slice of one layer.
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In the conversation it broke down that really complicated PRD into just four slices.
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It first created a kind of engine with some tests applied to it.
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This is actually quite a good vertical slice because this was the engine
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that was going to then power the rest of the setup.
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If this engine wasn't working for whatever reason or it wasn't feasible then we would need to flush that out quickly.
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And this is what this breakdown does.
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The PRD2 issues also establishes blocking relationships between the tasks.
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For instance, number 2 here is not actually blocked by anything, so it can be picked up independently to 1.
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This is really useful if you have a parallel agent setup where you can actually fire two agents at it at once, for instance in background tasks.
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And it also means that in the future you can add other issues to this, like QA issues that you find or things that need to be improved,
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and you can then establish blocking relationships between that and all the other things.
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We can see that number three here is blocked by 1, the editing engine, and the number 4, the Monaco editor toggle, is blocked by number 2.
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So I said yes to all of these and it created then all of these GitHub issues.
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These issues reference the parent PRD so that the local agent can fetch it and view it.
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And it sort of just breaks down what to build really.
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And crucially, it references the previous user stories in the PRD.
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We can then see a comment actually from ClaudeCode that ended up implementing this.
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It's a pure function document editing engine with 28 tests covering all acceptance criteria.
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And we can then take a look at the commit that references this issue.
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So this was basically my Ralph loop came and just implemented this based on the issue, commented on it, closed it, and then the next issue was unblocked.
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So so far the grill me skill can help you flesh out an idea, the writer PRD skill can help you take that idea and turn it into a document,
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and then the PRD to issue skill helps you then turn that destination document into an actual journey.
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But then how do you actually execute on that skill?
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How do you make it like, how do you make the implementation really rock solid and increase the code quality of what gets produced?
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We have got a TDD skill.
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TDD means Test Driven Development.
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And when you invoke this skill, it basically forces the agent, or encourages the agent rather, to follow a red-green refactor loop.
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Unusually for my skills, there is actually a lot in here.
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So it's not just the skill itself, it's also ideas on refactoring, on mocking, on what deep modules are.
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Doing really, really good TDD has been the most consistent way that I've improved agents' outputs.
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So let's have a look at what's actually in here.
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What we can see is, I'll just skip over the philosophy stuff, I'll let you guys read that.
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We are basically looking at this workflow.
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Now the first one here is really important.
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Confirm with the user what interface changes are needed.
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Now I made a video on interfaces and implementations recently, but let me just give you the pricing.
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When an AI looks at a bad codebase it will look at
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or it will see something like this where it has a ton of tiny modules here that are kind of undifferentiated.
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They're not really grouped together, it doesn't really understand how these things relate.
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And so it has to do a lot of work kind of working out, okay what's responsible for what, what are the dependencies, how does this actually, how does the codebase even function.
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Whereas if you restructure this into several larger modules with just kind of thin interfaces on top,
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the interface being the functions that are actually exported from this, the things that the callers actually call, then it's a lot easier for AI to navigate this codebase
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and it's a lot easier to work out how to test these modules.
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Because you just test them at their interfaces, you test them at their boundaries.
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You can check out the whole video on that below.
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So what this TDD skill is encouraging here is basically trying
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to make these interface changes really top of mind for the AI.
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To get it to understand that when it changes an interface that's an important decision it needs to take time over.
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You confirm with the user which behaviours to test.
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You design the interfaces for testability, linking to a dock.
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And then we have some more stuff around planning here.
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It then goes into a lovely loop where it writes one test at a time.
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And it writes the test first.
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Now I've talked about red green refactor before so I'll link the video below if you're interested.
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But I've found that red green refactor with agents is incredible.
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And it basically does this loop until it's complete.
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It just writes a the failing test, then writes the code to make that test pass.
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And finally it goes through and looks for refactor candidates.
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I haven't found that this is amazing, it hasn't been brilliant, because often LLMs are quite reluctant to refactor their own code.
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If you were to clear the context of the LLM then it would wipe its own memory
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and it would be a lot less precious about the code that it's just written.
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But while its own code is sitting in its own context window, it's quite reluctant to change it.
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is what I prompt my RALF loops with in order to get them to do red-green refactor.
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Now, TDD demands a lot of you, or rather it demands a lot of your codebase.
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TDD is really hard to do in a badly structured codebase, because the test boundaries of this are really unclear.
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Should it just sort of test these modules on their own?
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Should it test these modules on their own?
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What are the boundaries here?
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Whereas when your codebase looks more like this, then it's a lot easier to test because the module boundaries are really clear.
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So wouldn't it be great if there was a skill that made your codebase look more like this?
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Well isn't it nice?
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We've got an improved codebase architecture skill.
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The process for this one is that we explore the codebase and explore it naturally as an agent would.
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We're trying to find confusions.
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We're not like… we're trying to surface naturally what the AI finds confusing so that it can then help it out later.
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Where does understanding one concept require bouncing around between many small files?
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Where have pure functions been extracted just for testability but the real bugs hide in how they're called?
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Where do tightly coupled modules create integration risk in the seams between them?
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All of these are questions that a senior engineer would be asking about your codebase.
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Number two is you present candidates, so you present a numbered list of deepening opportunities.
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In other words, opportunities to deepen shallow modules in your codebase into deeper ones.
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The user then picks a candidate and then you design multiple interfaces, so it says to spawn three sub-agents in parallel,
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each of which must produce a radically different interface for the Deepen module.
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In other words, we're extracting that code and designing possible ways that it could look in the future.
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Designing it in multiple different ways is a really great way that you can then decide on the right idea.
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I've seen this agent spawn like five different sub-agents for a really big refactor.
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The coolest thing about this is you don't need to know a lot about interface design in order to get this working.
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After comparing, give them your recommendation which design you think is strongest and why.
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And if elements from different designs would combine well, then propose a hybrid.
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Notice that I've made this really language agnostic, really kind of sort of everything agnostic, really.
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You can just run this in any code base and just get a decent answer for how it could be improved.
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There might be four or five candidates that really could use some work, but really I think you should only be sort of doing one of these at a time because
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They really are quite hard to get your head around
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and they require a human in the loop to sit with them
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and improve the code base because these decisions do require taste.
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Finally it creates a GitHub issue.
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So it creates a refactor RFC as a GitHub issue using ghissue create.
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Usually once this is done I will then go with my prd2issues skill, reference that GitHub issue that's just been created and get it to, you know, this describes the destination.
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We then need a journey to get there.
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So just doing this every so often in a codebase, once a week, just to identify opportunities.
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Or if you have a sudden surge of development and you create a whole extra wing of features,
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then this skill will be really useful in just making sure it conforms to the rest of the codebase, making sure that it's not too sloppy.
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And as you keep running this, as you keep refining your codebase, you're going to notice the quality of the agent's output goes up.
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Because the old adage really does apply.
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If you have a garbage codebase then the AI is going to produce garbage within that codebase.
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Because to be honest if you took all of these skills
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and just said okay this is like a little mini markdown
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book of processes for humans then it wouldn't look out of place.
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I've found that the most successful way to get code quality up from agents is just to treat them like humans.
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Humans with weird constraints, sure, humans that have no memory and are just sort of cloned come out of the birthing pod and go right to work.
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But if you, like me, think these real engineering skills are super important, then this course is absolutely for you.
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What I noticed while I was creating the course is that I'm really not teaching clawed code that much.
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I'm teaching kind of what our sub-agents, I'm talking about the constraints of LLMs, the sort of weird smart zone, dumb zone stuff with the context window.
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We're talking about steering, which is essentially just a way of documenting stuff inside your codebase.
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How to tackle massive tasks, understanding tracer bullets and building those into our skills.
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Understanding how to build really great feedback loops and doing exercises with them, and crucially how to hook these up to an autonomous agent.
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Every part of this course just sort of like leads onto the other and I'm super happy with how it turned out.
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So over the course of two weeks you'll be working through
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that self-paced material with me as your guide in Discord and on live office hours.
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And if that sounds fun to you then the link is below.
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Thanks for watching folks, I'll be coming back with a lot more stuff this week.
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What would you you like me to cover next?
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I find the intersection between this real engineering and AI is such an awesome place to make content about.
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But anyway, thanks for watching and I'll see you in the next one.
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Vocabulaire et conseils d’expression pour cette leçon
Cette leçon d’expression orale de niveau C1 s’appuie sur la vidéo « 5 Claude Code skills I use every single day ». Les mots qui reviennent le plus souvent : skill, prd, codebase, issue, code. Cette vidéo contient 239 phrases et 3622 mots à répéter en shadowing. La partie parlée dure 16:41. Le locuteur parle vite, environ 217 mots par minute : attendez-vous à des liaisons et à des sons réduits. 82 % des mots font partie des 3 000 mots les plus courants en anglais ; le reste mérite d’être vu avant de commencer.
Vocabulaire clé de cette vidéo
Les 15 mots les plus avancés de la vidéo, avec leur prononciation et leur sens :
| Mot | Prononciation | Sens |
|---|---|---|
| codebase nom | codebase | |
| module nom | /ˈmɑd͡ʒul/ | module |
| refactor verbe | refactoriser | |
| grill nom | /ɡɹɪl/ | grille |
| slice nom | /slaɪs/ | tranche, tronçon |
| invoke verbe | /ɪnˈvoʊk/ | invoquer |
| deepen verbe | /ˈdiːpən/ | approfondir |
| integrate verbe | /ˈɪn.tɪ.ɡɹeɪt/ | intégrer |
| agnostic nom | /aɡˈnɒstɪk/ | agnostique |
| constraint nom | /kənˈstɹeɪnt/ | contrainte |
| markdown nom | démarque | |
| relentlessly adverbe | /ɹɪˈlɛntləsli/ | inexorablement, sans relâche |
| spawn verbe | /spɔn/ | frayer |
| tracer nom | /ˈtɹeɪsɚ/ | marqueur |
| fetch verbe | /fɛt͡ʃ/ | ramener, rapporter |
Les verbes à particule que vous entendrez
| Mot | Prononciation | Sens |
|---|---|---|
| break down verbe | tomber en panne | |
| end up verbe | se terminer, se finir | |
| figure out verbe | cerner | |
| go through verbe | traverser | |
| work out verbe | /ˌwɝk ˈaʊt/ | calculer |
| check out verbe | mater, regarder | |
| go up verbe | monter, s'élever | |
| pick up verbe | soulever |
Des phrases à répéter
Des phrases courtes et complètes de la vidéo, à réutiliser dans la conversation de tous les jours :
- We've got a problem statement.
- We've got an improved codebase architecture skill.
- We're trying to find confusions.
- What would you you like me to cover next?
La grammaire de cette vidéo
Les structures que le locuteur utilise le plus, avec les mots exacts de la vidéo :
| Structure | Dans la vidéo |
|---|---|
| Phrases conditionnelles if + proposition, will/would + verbe — une condition et son résultat | if elements from different designs would combine |
| Present perfect have/has + participe passé — une action passée qui compte encore maintenant | I've been · they've done · has resulted |
| Voix passive be + participe passé — l’accent est mis sur ce qui arrive, pas sur qui le fait | is called · can be answered · will be invoked |
| Propositions relatives who / which + proposition — une précision sur une personne ou une chose | one which is · Brooks, which is · codebase, which is |
Prononciation à surveiller
Le locuteur utilise 67 contractions et formes réduites, comme I've, we're, don't. Prononcez-les sous leur forme courte, telles que vous les entendez.
- Les sons « sh » et « zh »: crucially /ˈkɹuːʃəli/, flush /ˈflʌʃ/, intuition /ˌɪn.tjuːˈɪʃ.ən/, assertion /əˈsɜɹʃən/, unusually /ʌnˈjuːʒuəli/
- Mots longs — placez bien l’accent: dependency /dɪˈpɛndənsi/, relentlessly /ɹɪˈlɛntləsli/, analogy /əˈnæləd͡ʒi/, independently /ˌɪndɪˈpɛndəntli/, criterion /kɹaɪˈtɪɹ.i.ən/
Les sons difficiles pour les francophones :
- /h/ — il se souffle, il n’est pas muet: cohort /ˈkoʊ̯ˌhɔɹt/, horizontal /ˌhoɹəˈzɑn.təl/
- /tʃ/ et /dʒ/ — à ne pas adoucir en « ch » et « j »: module /ˈmɑd͡ʒul/, fetch /fɛt͡ʃ/, analogy /əˈnæləd͡ʒi/, surge /sɝd͡ʒ/, methodology /ˌmɛθ.əˈdɑ.lə.d͡ʒi/
- /r/ anglais — langue recourbée, sans frotter la gorge: grill /ɡɹɪl/, integrate /ˈɪn.tɪ.ɡɹeɪt/, constraint /kənˈstɹeɪnt/, crucially /ˈkɹuːʃəli/, relentlessly /ɹɪˈlɛntləsli/
Comment s’entraîner avec cette vidéo
- Écoutez la vidéo en entier une fois sans parler et notez les mots que vous ne connaissez pas.
- Commencez à la vitesse 0,75×, répétez phrase par phrase, puis revenez à la vitesse normale quand cela devient facile.
- Enregistrez-vous et comparez avec l’original, en faisant attention à des mots comme codebase, module, refactor.
Qu'est-ce que la technique du Shadowing ?
Le Shadowing est une technique d'apprentissage des langues fondée sur la science, développée à l'origine pour la formation des interprètes professionnels. Le principe est simple mais puissant : vous écoutez de l'anglais natif et le répétez immédiatement à voix haute — comme une ombre suivant le locuteur avec un décalage de 1 à 2 secondes. Les recherches montrent une amélioration significative de la précision de la prononciation, de l'intonation, du rythme, des liaisons, de la compréhension orale et de la fluidité.