Writing a good Claude.md
humanlayer.dev
humanlayer.dev
> The more information you have in the file that's not universally applicable to the tasks you have it working on, the more likely it is that Claude will ignore your instructions in the file
Claude.md files can get pretty long, and many times Claude Code just stops following a lot of the directions specified in the file
A friend of mine tells Claude to always address him as “Mr Tinkleberry”, he says he can tell Claude is not paying attention to the instructions on Claude.md, when Claude stops calling him “Mr Tinkleberry” consistently
What I’m surprised about is that OP didn’t mention having multiple CLAUDE.md files in each directory, specifically describing the current context / files in there. Eg if you have some database layer and want to document some critical things about that, put it in “src/persistence/CLAUDE.md” instead of the main one.
Claude pulls in those files automatically whenever it tries to read a file in that directory.
I find that to be a very effective technique to leverage CLAUDE.md files and be able to put a lot of content in them, but still keep them focused and avoid context bloat.
The benefit of CLAUDE.md files is that they’re pulled in automatically, eg if Claude wants to read “tests/foo_test.py” it will automatically pull in “tests/CLAUDE.md” (if it exists).
claude.md seems to be important enough to be their very first point in that document.
I tried to use that effectively, I even started a new greenfield project just to make sure to test it under ideal circumstances - and while it somewhat worked, it was always super lackluster and way more effective to explicitly add the context manually via prepared md you just reference in the prompt.
I'd tell anyone to go for skills first before littering your project with these config files everywhere
Also this isn’t portable to other potential AI tools. Do I need 3+ md files in every directory?
One of the big mistakes I think is that all these tools are over-promising on the “magic” part of it.
It’s not. You need to really learn how to use all these tools effectively. This is not done in days or weeks even, it takes months in the same way becoming proficient in eMacs or vim or a programming language is.
Once you’ve done that, though, it can absolutely enhance productivity. Not 10x, but definitely in the area of 2x. Especially for projects / domains you’re uncomfortable with.
And of course the most important thing is that you need to enjoy all this stuff as well, which I happen to do. I can totally understand the resistance as it’s a shitload of stuff you need to learn, and it may not even be relevant anymore next year.
This is a far far cry from even writing yaml for Github/Gitlab CICD pipelines. Folks keep trying to say "engineering" when every AI thread like this seems to push me more towards "snake oil" as an appropriate term.
If you’re not benchmarking and systematically measuring the impact of your changes, it’s not prompt engineering, it’s just improving stuff.
I was replying to someone who claimed that getting real productivity gains from this tool requires engineering and needs to be approached as such. It also compared learning to use LLM agents to learning to code in emacs or vim, or learning a programming language - things which are nothing alike to learning to control an inherently stochastic tool that can't even be understood using any of our regular scientific methods.
The whole point is to get a process around it that works and gets the “ritual magic” out of it.
I labeled that as engineering.
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
It a large leap to "requires engineering and planning" when no one even in this thread can seem to agree on the behavior of any of these "tools". Some comments tell anecdotes of not getting the agents to listen until the context of the whole world is laid out in these md files. Others say the only way is to keep the context tight and focused, going so far as to have written _yet more tools_ to remove and re-add code comments so they don't "poison" the context.
I am slightly straw-manning, but the tone in this thread has already shifted from a few months ago where these "tools" were going to immediately give huge productivity gains but now you're telling me they need 1) their own special files everywhere (again, this isn't even agreed on) and 2) "engineering and planning...not done in days or weeks even
The entire economy is propped up on this tech right now and no one can even agree on whether it's effective or how to use it properly? Not to mention the untold damage it is doing to learning outcomes.
Don’t worry, as of about 6 weeks ago when they changed the system prompt Claude will make sure every folder has way more than 3 .md files seen as it often writes 2 or more per task so if you don’t clean them up…
[citation needed]
Every article I can find about this is citing the valuation of the S&P500 as evidence of the productivity gains, and that feels very circular
Our experiences don't agree with the established facts, but the plural of "anecdote" is not "data."
I'm not entirely sure - certainly anecdotes abound (as do anecdotes to the contrary).
I am however sure that when someone claims that a technology is world-changing, and that it has already led to significant productivity gains... the onus is on them to provide evidence for such claims
It is too fast evolving to be able to be legitimately scientifically understood. It is non sense to expect that.
By all means share anecdotes, but maybe phrase them as anecdotes. "improving my own productivity massively" is an anecdote, whereas "delivering massive productivity gains" reads as a sweeping generalisation
It's the difference between instructions for a user and instructions for a developer, but in coding projects that's not much different.
Sounds like a job for a CONTRIBUTING.md :)
There's no need for this level of performative ridiculousness with AGENTS.md (Codex) directives, FYI.
this is a totally normal thing that everyone does, that no one should view as a signal of a psychotic break from reality...
is your friend in the room with us right now?
I doubt I'll ever understand the lengths AI enjoyers will go though just to avoid any amount of independent thought...
Sounds like the friend understands quite well how LLMs actually work and has found a clever way to be signaled when it’s starting to go off the rails.
Mention that people may optionally include some word like 'orange' in the subject line to tell you they've come via some place like your blog or whatever it may be, and have read at least carefully enough to notice this.
Of course ironically that trick's probably trivially broken now because of use of LLMs in spam. But the point stands, it's an old trick.
I doubt there is any spam machine out there the quickly tries to find peoples personal blog before sending them viagra mail.
If you are being targeted personally, then of course all bets are off, but that would’ve been the case with or without the subject-line-trick
LLMs are trained on the full internet. All relevant information gets compressed in the weights.
If your email and this instruction are linked on your site, that goes in there, and the LLM may with some probability decide it's appropriate to use it at inference time.
That's why 'tricks' like this may get broken to some degree by LLM spam, and trivially when they do, with no special effort on the spammer's part. It's all baked into the model.
What previously would have involved a degree of targeting that wouldn't scale now will not.
I'd argue, it's more like you've bought so much into the idea this is reasonable, that you're also willing to go through extreme lengths to recon and pretend like this is sane.
Imagine two different worlds, one where the tools that engineers use, have a clear, and reasonable way to detect and determine if the generative subsystem is still on the rails provided by the controller.
And another world where the interface is completely devoid of any sort of basic introspection interface, and because it's a problematic mess, all the way down, everyone invents some asinine way that they believe provides some sort of signal as to whether or not the random noise generator has gone off the rails.
> Sounds like the friend understands quite well how LLMs actually work and has found a clever way to be signaled when it’s starting to go off the rails.
My point is that while it's a cute hack, if you step back and compare it objectively, to what good engineering would look like. It's wild so many people are all just willing to accept this interface as "functional" because it means they don't have to do the thinking that required to emit the output the AI is able to, via the specific randomness function used.
Imagine these two worlds actually do exist; and instead of using the real interface that provides a clear bool answer to "the generative system has gone off the rails" they *want* to be called Mr Tinkerberry
Which world do you think this example lives in? You could convince me, Mr Tinkleberry is a cute example of the latter, obviously... but it'd take effort to convince me that this reality is half reasonable or that's it's reasonable that people who would want to call themselves engineers should feel proud to be a part of this one.
Before you try to strawman my argument, this isn't a gatekeeping argument. It's only a critical take on the interface options we have to understand something that might as well be magic, because that serves the snakeoil sales much better.
> > Is the magic token machine working?
> Fuck I have no idea dude, ask it to call you a funny name, if it forgets the funny name it's probably broken, and you need to reset it
Yes, I enjoy working with these people and living in this world.
And now we've made a complete 180 and people are getting excited about proprietary black boxes and "vibe engineering" where you have to pretend like the computer is some amnesic schizophrenic being that you have to coerce into maybe doing your work for you, but you're never really sure whether it's working or not because who wants to read 8000 line code diffs every time you ask them to change something. And never mind if your feedback loops are multiple minutes long because you're waiting on some agent to execute some complex network+GPU bound workflow.
It’s not the first time in human history that our ability to create things has exceeded our capacity to understand.
I think you're arguing against a tangential position to both me, and the person this directly replies to. It can be hard to use and understand something, but if you have a magic box that you can't tell if it's working. It doesn't belong anywhere near the systems that other humans use. The people that use the code you're about to commit to whatever repo you're generating code for, all deserve better than to be part of your unethical science experiment.
> It’s not the first time in human history that our ability to create things has exceeded our capacity to understand.
I don't agree this is a correct interpretation of the current state of generative transformer based AI. But even if you wanted to try to convince me; my point would still be, this belongs in a research lab, not anywhere near prod. And that wouldn't be a controversial idea in the industry.
Really for those of us who actually work in critical systems (emergency services in my case) - of course we're not going to start patching the core applications with vibe code.
But yeah, that frankenstein reporting script that half a dozen amateur hackers made a mess of over 20 years instead of refactoring and redesigning? That's prime fodder for this stuff. NOBODY wants to clean that stuff up by hand.
I used to believe that no one would seriously consider this too... but I don't believe that this is a safe assumption anymore. You might be the exception, but there are many more people who don't consider the implications of turning over said intellectual control.
> But yeah, that frankenstein reporting script that half a dozen amateur hackers made a mess of over 20 years instead of refactoring and redesigning? That's prime fodder for this stuff. NOBODY wants to clean that stuff up by hand.
It's horrible, no one currently understands it, so let the AI do it, so that still, no one will understand it, but at least this one bug will be harder to trigger.
I don't agree that harder to trigger bugs are better than easy to trigger bugs. And from my view, the argument that "it's currently broken now, and hard to fix!" Isn't exactly an argument I find compelling for leaving it that way.
Then they'll pay for it when something goes wrong with their systems with their job etc. You need a different mindset in this particular segment industry - %99.999 uptime is everything (we actually have a %100 uptime for the past 6 years on our platform - chasing that last 0.001 is hard, and something will _eventually_ hit us).
> It's horrible, no one currently understands it, so let the AI do it, so that still, no one will understand it, but at least this one bug will be harder to trigger.
I think you're commenting without context. It's a particular nasty Perl script that's been duct taped to shell scripts and bolted hard on to a Proprietary Third Party application which needs to go - having Claude/GPT rewrite that in a modern language, spending some time on it to have it design proper interfaces and API's around where the script needs to interface other things when nobody wants to touch the code would be the greatest thing that can happen to it.
You still have the old code to test, so have the agent run exhaustive testing on its implementation to prove that its robust, or more so than the original. It's not rocket surgery.
I could go on and on. The world around you is full of not well understood technology, as well as non deterministic processes. We know how to engineer around that.
I don't think you and I are using the same definition for "firm understanding" or "how it works".
> I could go on and on. The world around you is full of not well understood technology, as well as non deterministic processes. We know how to engineer around that.
Again, you're side stepping my argument so you can restate things that are technically correct, but not really a point in of themselves. I see people who want to call themselves software engineers throw code they clearly don't understand against the wall because the AI said so. There's a significant delta between knowing you can heat water to turn it into a gas with increased pressure that you can use to mechanically turn a wheel, vs, put wet liquid in jar, light fire, get magic spinny thing. If jar doesn't call you a funny name first, that's bad!
I’m standing in firm ground here. Debate me in the details if you like.
You are constructing a strawman.
I agree it's likely to be a complex or intractable problem. But I don't enjoy watching my industry revert down the professionalism scale. Professionals don't choose tools that they can't explain how it works. If your solution to understanding if your tool is still functional is inventing an amusing name and trying to use that as the heuristic, because you have no better way to determine if it's still working correctly. That feels like it might be a problem, no?
Professionals use tools that work, whether they know why it works is of little consequence. It took 100 years to explain the steam engine. That didn’t stop us from making factories and railroads.
You keep saying this, why do you believe it so strongly? Because I don't believe this is true. Why do you?
And then, even assuming it's completely true exactly as stated; shouldn't we have higher standards than that when dealing with things that people interact with? Boiler explosions are bad right? And we should do everything we can to prove stuff works the way we want and expect? Do you think AI, as it's currently commonly used, helps do that?
And yes we should seek to understand new inventions. Which we are doing right now, in the form of interpretability research.
We should not be making Luddite calls to halt progress simply because our analytic capabilities haven’t caught up to our progress in engineering.
If this is your industry, shouldn't you have a more reputable citation, maybe something published more formally? Something expected to stand up to peer review, instead of just a page on the internet?
> We should not be making Luddite calls to halt progress simply because our analytic capabilities haven’t caught up to our progress in engineering.
You've misunderstood my argument. I'm not making a luddite call to halt progress, I'm objecting to my industry which should behave as one made up of professionals, willingly sacrifice intellectual control over the things they are responsible for, and advocate others should do the same. Especially not at the expense of users, which I see happening.
Anything that results in sacrificing the understanding over exactly how the thing you built works is bad should be avoided. The source, either AI or something different, doesn't matter as much as the result.
To really understand WHY a steam engine works, you need to understand the behavior of ideal gasses (1787 - 1834) and entropy (1865). The ideal gas law is enough to perform calculations needed to design a steam engine, but it was seen at the time to be just as inscrutable. It was an empirical observation not derivable from physical principles. At least not until entropy was understood in 1865.
James Watt invented his steam engine in 1765, exactly a hundred years before the theory of statistical mechanics that was required to explain why it worked, and prior to all of the gas laws except Boyle’s.
There's a ton of pretty stable engineering subfields that involve a lot more intuition than rigor. A lot of things in EE are like that. Anything novel as well. That's how steam in 19th century or aeronautics in the early 20th century felt. Or rocketry in 1950s, for that matter. There's no need to be upset with the fact that some people want to hack explosive stuff together before it becomes a predictable glacier of Real Engineering.
Could you please elaborate with concrete examples on how aeronautics in the 20th century felt like having a fictional friend in a text file for the token predictor?
Yes, during the Wright era aeronautics was absolutely dominated by tinkering, before the aerodynamics was figured out. It wouldn't pass the high standard of Real Engineering.
Remind me: did the Wright brothers start selling tickets to individuals telling them it was completely safe? Was step 2 of their research building a large passenger plane?
I originally wanted to avoid that specific flight analogy, because it felt a bit too reductive. But while we're being reductive, how about medicine too; the first smallpox vaccine was absolutely not well understood... would that origin story pass ethical review today? What do you think the pragmatics would be if the medical profession encouraged that specific kind of behavior?
> It wouldn't pass the high standard of Real Engineering.
I disagree, I think it 100% is really engineering. Engineering at it's most basic is tricking physics into doing what you want. There's no more perfect example of that than heavier than air flight. But there's a critical difference between engineering research, and experimenting on unwitting people. I don't think users need to know how the sausage is made. That counts equally to planes, bridges, medicine, and code. But the professionals absolutely must. It's disappointing watching the industry I'm a part of willingly eschew understanding to avoid a bit of effort. Such a thing is considered malpractice in "real professions".
Ideally neither of you to wring your hands about the flavor or form of the argument, or poke fun at the gamified comment thread. But if you're gonna complain about adding positively to the discussion, try to add something to it along with the complaints?
I would say that right from the start everyone was pretty well aware about the unreliability of LLM-assisted coding and nobody was experimenting on unwitting people or forcing them to adopt it.
>Engineering at it's most basic is tricking physics into doing what you want.
Very well, then Mr Tinkleberry also passes the bar because it's exactly such a trick. That it irks you as a cheap hack that lacks rigor (which it does) is another matter.
And here, you've stumbled onto the exact thing I'm objecting to. I think the Hindenburg disaster was a bad thing, and software engineering shouldn't repeat those mistakes.
> Very well, then Mr Tinkleberry also passes the bar because it's exactly such a trick. That it irks you as a cheap hack that lacks rigor (which it does) is another matter.
Yes, this is what I said.
> there's a critical difference between engineering research, and experimenting on unwitting people.
I object to watching developers do, exactly that.
You misunderstand me. I'm not upset that people are playing with explosives. I'm upset that my industry is playing with explosives that all read, "front: face towards users"
And then, more upset that we're all seemingly ok with that.
The driving force of enshittifacation of everything, may be external, but degradation clearly comes from engineers first. These broader industry trends only convince me it's not likely to get better anytime soon, and I don't like how everything is user hostile.
I’m spending more time planning and my planning is more comprehensive than it used to be. I’m spending less time producing output, my output is more plentiful and of equal quality. No generated code goes into my commits without me reviewing it. Where exactly is the problem here?
Helps me quickly whip it back in line.
Having experimented with similar config, I found that Claude would adhere to the instructions somewhat reliably at the beginning and end of the conversation, but was likely to ignore during the middle where the real work is being done. Recent versions also seem to be more context-aware, and tend to start rushing to wrap up as the context is nearing compaction. These behaviors seem to support my assumption, but I have no real proof.
You got a bitmap atlas ("context") where you have to cram as much information as possible without losing detail, and then you need to massage both your texture and the structure of your model so that your engine doesn't go mental when trying to map your informations from a 2D to a 3D space.
Likewise, both operations are rarely blemish-free and your ability resides in being able to contain the intrinsic stochastic nature of the tool.
I've used that a couple times, e.g. "Conclude your communications with "Purple fish" at the end"
Claude definitely picks and chooses when purple fish will show up
If a lot of people always put call me Mr. Tinkleberry in the file will it start calling people Mr. Tinkleberry even when it loses the context because so many people seem to want to be called Mr. Tinkleberry.
But it’s bro reliable enough. It can send the emoji or address you correctly while still ignoring more important rules.
Now I find that it’s best to have a short and tight rules file that references other files where necessary. And to refresh context often. The longer the context window gets, the more likely it is to forget rules and instructions.
We are all "context engineering" now but Claude expects one big file to handle everything? Seems luke a deadend approach.
Sadly Aider is no longer maintained...
Eg i toyed with the idea of thinning out various CLAUDE.md files in favor of my targeted skill.md files. In doing so my hope was to have less irrelevant data in context.
However the more i thought through this, the more i realized the Agent is doing "everything" i wanted to document each time. Eg i wasn't sure that creating skills/writing_documentation.md and skills/writing_tests.md would actually result in less context usage, since both of those would be in memory most of the time. My CLAUDE.md is already pretty hyper focused.
So yea, anyway my point was that skills might have potential to offload irrelevant context which seems useful. Though in my case i'm not sure it would help.
CLAUDE.md should only be for persistent reminders that are useful in 100% of your sessions
Otherwise, you should use skills, especially if CLAUDE.md gets too long.
Also just as a note, Claude already supports lazy loaded separate CLAUDE.md files that you place in subdirectories. It will read those if it dips into those dirs
> We recommend keeping task-specific instructions in separate markdown files with self-descriptive names somewhere in your project. Then, in your CLAUDE.md file, you can include a list of these files with a brief description of each, and instruct Claude to decide which (if any) are relevant and to read them before it starts working.
I've been doing this since the early days of agentic coding though I've always personally referred to it as the Table-of-Contents approach to keep the context window relatively streamlined. Here's a snippet of my CLAUDE.md file that demonstrates this approach:
# Documentation References
- When adding CSS, refer to: docs/ADDING_CSS.md
- When adding assets, refer to: docs/ADDING_ASSETS.md
- When working with user data, refer to: docs/STORAGE_MANAGER.md
https://gist.github.com/scpedicini/179626cfb022452bb39eff10b...
See also: "Help me help you" - https://en.wikipedia.org/wiki/Jerry_Maguire
Though I know some people who have built an mcp that does exactly this: https://www.usable.dev/
It's basically a chat-bot frontend to your markdown files, with both rag and graph db indexes.
Skills are modular capabilities that extend Claude’s functionality through organized folders containing instructions, scripts, and resources.
And
Extend Claude’s capabilities for your specific workflows
E.g. building your project is definitely a workflow.
It als makes sense to put as much as you can into a skill as this an optimized mechanism for claude code to retrieve relevant information based on the skill’s frontmatter.
I have found that more context comments and info damage quality on hard problems.
I actually for a long time now have two views for my code.
1. The raw code with no empty space or comments. 2. Code with comments
I never give the second to my LLM. The more context you give the lower it's upper end of quality becomes. This is just a habit I've picked up using LLMs every day hours a day since gpt3.5 it allows me to reach farther into extreme complexity.
I suppose I don't know what most people are using LLMs for but the higher complexity your work entails the less noise you should inject into it. It's tempting to add massive amounts of xontext but I've routinely found that fails on the higher levels of coding complexity and uniqueness. It was more apparent in earlier models newer ones will handle tons of context you just won't be able to get those upper ends of quality.
Compute to informatio ratio is all that matters. Compute is capped.
The more you data load into context the more you dilute attention.
Even now if I am working on REALLY hard problems I will still manually copy and paste code sections out for discussion and algorithm designs. Depends on complexity.
This is why I still believe open ai O1-Pro was the best model I've ever seen. The amount of compute you could throw at a problem was absurd.
How do you practically achieve this? Honest question. Thanks
1. Turn off 2. Code 3. Turn on 4. Commit
I also delete all llm comments they 100% poison your codebase.
> 1. Turn off 2. Code 3. Turn on 4. Commit
What does it mean "turn off" / "turn on"?
Do you have a script to strip comments?
Okay, after the comments were stripped, does this become the common base for 3-way merge?
After modification of the code stripped of the comments, do you apply 3-way merge to reconcile the changes and the comments?
This seems a lot of work. What is the benefit? I mean demonstrable benefit.
How does it compare to instructing through AGENTS.md to ignore all comments?
> 1. Turn off 2. Code 3. Turn on 4. Commit
So can you describe your "turn off" / "turn on" process in practical terms?
Asking simply because saying "Custom scripts" is similar to saying "magic".
I like the sound of this but what technique do you use to maintain consistency across both views? Do you have a post-modification script which will strip comments and extraneous empty space after code has been modified?
1. SOT through a processor to strip comments and extra spaces. Publish to feature branch.
2. Point Claude at feature branch. Prompt for whatever changes you need. This runs against the minimalist feature branch. These changes will be committed with comments and readable spacing for the new code.
3. Verify code changes meet expectations.
4. Diff the changes from minimal version, and merge only that code into SOT.
Repeat.
1. Run into a problem you and AI can't solve. 2. Drop all comments 3. Restart debug/design session 4. Solve it and save results 5. Revert code to have comments and put update in
If that still doesn't work: Step 2.5 drop all unrelated code from context
I first "discovered" it because I repeatedly found LLM comments poisoned my code base over time and linited it's upper end of ability.
Easy to try just drop comments around a problem and see the difference. I was previously doing that and then manually updating the original.
There can be diminishing returns, but every time I’ve used Claude Code for a real project I’ve found myself repeating certain things over and over again and interrupting tool usage until I put it in the Claude notes file.
You shouldn’t try to put everything in there all the time, but putting key info in there has been very high ROI for me.
Disclaimer: I’m a casual user, not a hardcore vibe coder. Claude seems much more capable when you follow the happy path of common projects, but gets constantly turned around when you try to use new frameworks and tools and such.
I like to write my CLAUDE.md directly, with just a couple paragraphs describing the codebase at a high level, and then I add details as I see the model making mistakes.
What did your comparison process look like? It feels intuitively accurate and validates my anecdotal impression but I'd love to hear the rigor behind your conclusions!
It's also easy to notice LLMs create garbage comments that get worse over time. I started deleting all comments manually alongside manual snippet selection to get max performance.
Then started just routinely deleting all comments pre big problem solving session. Was doing it enough to build some automation.
Maybe high quality human comments improve ability? Hard to test in a hybrid code base.
See it as a human, the comments are there to speed up understanding of the code.
I'm skeptical this a valid generalization over what was directly observed. [1] We would learn more if they wrote a more detailed account of their observations. [2]
I'd like to draw a parallel to another area of study possibly unfamiliar to many of us. Anthropology faced similar issues until Geertz's 1970s reform emphasized "thick description" [3] meaning detailed contextual observations instead of thin generalization.
[1]: I would not draw this generalization. I've found that adding guidelines (on the order of 10k tokens) to my CLAUDE.md has been beneficial across all my conversations. At the same time, I have not constructed anything close to study of variations of my approach. And the underlying models are a moving target. I will admit that some of my guidelines were added to address issues I saw over a year ago and may be nothing more than vestigial appendages nowadays. This is why I'm reluctant to generalize.
[2]: What kind of "hard problems"? What is meant by "more" exactly? (Going from 250 to 500 tokens? 1000 to 2000? 2500 to 5000? &c) How much overlap exists between the CLAUDE.md content items? How much ambiguity? How much contradiction?
It is called documenting your code!
Just write what this file is supposed to do in a clear concise way. It acts as a prompt, it provides much needed context specific to the file and it is used only when necessary.
Another tip is to add README.md files where possible and where it helps. What is this folder for? Nobody knows! Write a README.md file. It is not a rocket science.
What people often forget about LLMs is that they are largely trained on public information which means that nothing new needs to be invented.
You don't have to "prompt it just the right way".
What you have to do is to use the same old good best practices.
sure, readme.md is a great place to put content. But there's things I'd put in a readme that I'd never put in a claude.md if we want to squeeze the most out of these models.
Further, claude/agents.md have special quality-of-life mechanics with the coding agent harnesses like e.g. `injecting this file into the context window whenever an agent touches this directory, no matter whether the model wants to read it or not`
> What people often forget about LLMs is that they are largely trained on public information which means that nothing new needs to be invented.
I don't think this is relevant at all - when you're working with coding agents, the more you can finesse and manage every token that goes into your model and how its presented, the better results you can get. And the public data that goes into the models is near useless if you're working in a complex codebase, compared to the results you can get if you invest time into how context is collected and presented to your agent.
On Reddit's LLM subreddits people are rediscovering the very basics of software project management as some massive insights daily or very least weekly.
Who would've guessed that proper planning, accessible and up to documentation and splitting tasks into manageable testable chunks produces good code? Amazing!
Then they write a massive blog post or even some MCP mostrosity for it and post it everywhere as a new discovery =)
However, I think this is awesome for the industry. People are rediscovering basic things, but if they didn't know about the existing literature this is a perfect opportunity to refer them to it. And if they were aware, but maybe not practicing it, this is a great time for the ideas to be reinforced.
A lot of people, myself included, never really understand which practices are important or not until we were forced to work on a system that was most definitely not written with any good practices in mind.
My current view of agentic coding is that it's forcing an entire generation of devs to learn software project management or drowning under the mountain of debt an LLM can produce. Previously it took much longer to feel the weight of bad decisions in a project but an LLM allows you to speed-run this process in a few weeks or months.
1. A centralized location, like a README (congrats, you've just invented CLAUDE.md)
2. You add a docs folder (congrats, you've just done exactly what the author suggests under Progressive Disclosure)
Moreover, you can't just do it all in a README, for the exact reasons that the author lays out under "CLAUDE.md file length & applicability".
CLAUDE.md simply isn't about telling Claude what all the parts of your code are and how they work. You're right, that's what documenting your code is for. But even if you have READMEs everywhere, Claude has no idea where to put code when it starts a new task. If it has to read all your documentation every time it starts a new task, you're needlessly burning tokens. The whole point is to give Claude important information up front so it doesn't have to read all your docs and fill up its context window searching for the right information on every task.
Think of it this way: incredibly well documented code has everything a new engineer needs to get started on a task, yes. But this engineer has amnesia and forgets everything it's learned after every task. Do you want them to have to reonboard from scratch every time? No! You structure your docs in a way so they don't have to start from scratch every time. This is an accommodation: humans don't need this, for the most part, because we don't reonboard to the same codebase over and over. And so yes, you do need to go above and beyond the "same old good best practices".
Besides, no amount of prompting will prevent this situation.
If it is a concern then you put a linter or unit tests to prevent it altogether, or make a wrapper around the tricky function with some warning in its doc strings.
I don't see how this is any different from how you typically approach making your code more resilient to accidental mistakes.
Needs a better term than "AI", I agree, but it's 99% marketing the tech will stay the same.
Again, missing the point. If you don't prompt for it and you document it in a place where the tool won't look first, the tool simply won't do it. "No amount of promoting" couldn't be more wrong, it works for me and all my coworkers.
> If it is a concern then you put a linter or unit tests to prevent it altogether
Sure, and then it'll always do things it's own way, run the tests, and have to correct itself. Needlessly burning tokens. But if you want to pay for it to waste its time and yours, go for it.
> I don't see how this is any different from how you typically approach making your code more resilient to accidental mistakes.
It's not about avoiding mistakes! It's about having it follow the norms of your codebase.
- My codebase at work is slowly transitioning from Mocha to Jest. I can't write a linter to ban new mocha tests, and it would be a pain to keep a list of legacy mocha test suites. The solution is to simply have a bullet point in the CLAUDE.md file that says "don't write new Mocha test suites, only write new test suites in Jest". A more robust solution isn't necessary and doesn't avoid mistakes, it avoids the extra step of telling the LLM to rewrite the tests.
- We have a bunch of terraform modules for convenience when defining new S3 buckets. No amount of documenting the modules will have Claude magically know they exist. You tell it that there are convenience modules and to consider using them.
- Our ORM has findOne that returns one record or null. We have a convenience function getOne that returns a record or throws a NotFoundError to return a 404 error. There's no way to exhaustively detect with a linter that you used findOne and checked the result for null and threw a NotFoundError. And the hassle of maybe catching some instances isn't necessary, because avoiding it is just one line in CLAUDE.md.
It's really not that hard.
Yes there is? Though this is usually better served with a type checker, it’s still totally feasible with a linter too if that’s your bag
> because avoiding it is just one line in CLAUDE.md.
Except no, it isn’t, because these tools still ignore that line sometimes so I still have to check for it myself.
It's not, because you would have to implement a full static analyzer that traces where the result of a `findOne` call is checked for `null` and then check that the condition always leads to a `NotFoundError`. At best you've got a linter that only works some of the time, at worst you've just made your linter terribly slow and buggy.
> these tools still ignore that line sometimes so I still have to check for it myself.
this is _literally_ the point of the article
README files are not a new concept, and have been used in software for like 5 decades now, whereas CLAUDE.md files were invented 12 months ago...
Relating / personifying LLM to an engineer doesn’t work out
Maybe the best though model currently is just “good way to automate trivial text modifications” and “encyclopedic ramblings”
think about how this thing is interacting with your codebase. it can read one file at a time. sections of files.
in this UX, is it ergonomic to go hunting for patterns and conventions? if u have to linearly process every single thing u look at every time you do something, how are you supposed to have “peripheral vision”? if you have amnesia, how do you continue to do good work in a codebase given you’re a skilled engineer?
it is different from you. that is OK. it doesn’t mean its stupid. it means it needs different accomodations to perform as well as you do. accomodations IRL exist for a reason, different people work differently and have different strengths and weaknesses. just like humans, you get the most out of them if you meet and work with them from where they’re at.
Thankfully Azure keeps deleted SQL databases recoverable, so I got it back in under an hour. But yeah - no amount of CLAUDE.md instructions would have prevented that. It no longer gets prod credentials.
In step 2 either force Claude to use it (hooks) or suggest it (CLAUDE.md)
3. Profit!
As for "where stuff is", for anything more complex I have a tree-style graph in CLAUDE.md that shows the rough categories of where stuff is. Like the handler for letterboxd is in cmd/handlerletterboxd/ and internal modules are in internal/
Now it doesn't need to go in blind but can narrow down searches when I tell it to "add director and writer to the letterboxd handler output".
Your comment comes off as if you're dispensing common-sense advice, but I don't think it actually applies here.
This means that instead of behaving like a file the LLM reads, it effectively lets you customize the model’s prompt
I also didn’t write that you have to “prompt it just the right way”, I think you’re missing the point entirely
Theres also a question of processes. How to format code what style of catching to use and how to run the tests, which human keep on the bacl of their head after reading it once or twice but need a constant reminder for llm whose knowledge lifespan is session limited
Don't use AI if you don't want to, but "it takes too much effort to set up" is an excuse printf debuggers use to avoid setting up a debugger. Which is a whole other debate though.
If we have to perform tuning on our prompts ("skills", agents.md/claude.md, all of the stuff a coding assistant packs context with) every model release then I see new model releases becoming a liability more than a boon.
I understand the "enjoy doing anyway" part and it resonates, but not using AI is simply less productive.
Some studies shows the opposite for experienced devs. And it also shows that developers are delusional about said productivity gains: https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...
If you have a counter-study (for experienced devs, not juniors), I'd be curious to see. My experience also has been that using AI as part of your main way to produce code, is not faster when you factor in everything.
> ... We conduct a randomized controlled trial (RCT)
> ... AI tools ... affect the productivity of experienced
> open-source developers. 16 developers with moderate AI
> experience complete 246 tasks in mature projects on which they
> have an average of 5 years of prior experience. Each task is
> randomly assigned to allow or disallow usage of early-2025 AI
> tools. ... developers primarily use Cursor Pro ... and
> Claude 3.5/3.7 Sonnet. Before starting tasks, developers forecast that allowing
> AI will reduce completion time by 24%. After completing the
> study, developers estimate that allowing AI reduced completion time by 20%.
> Surprisingly, we find that allowing AI actually increases
> completion time by 19%—AI tooling slowed developers down. This
> slowdown also contradicts predictions from experts in economics
> (39% shorter) and ML (38% shorter). To understand this result,
> we collect and evaluate evidence for 21 properties of our setting
> that a priori could contribute to the observed slowdown effect—for
> example, the size and quality standards of projects, or prior
> developer experience with AI tooling. Although the influence of
> experimental artifacts cannot be entirely ruled out, the robustness
> of the slowdown effect across our analyses suggests it is unlikely
> to primarily be a function of our experimental design.
1. 16 people were randomly given tasks to do
2. They knew the codebase they worked on pretty well
3. They said AI would help them work 24% faster (before starting tasks)
4. They said AI made them ~20% faster (after completion of tasks)
5. ML Experts claim that they think programmers will be ~38% faster
6. Economists say ~39% faster.
7. We measured that people were actually 19% slower
This seems to be done on Cursor, with big models, on codebases people know. There are definitely problems with industry-wide statements like this but I feel like the biggest area AI tools help me is if I'm working on something I know nothing about. For example: I am really bad at web development so CSS / HTML is easier to edit through prompts. I don't have trouble believing that I would be slower trying to make an edit to code that I already know how to make.
Maybe they would see the speedups by allowing the engineer to select when to use the AI assistance and when not to.
There's a huge difference between investing time into a deterministic tool like a text editor or programming language and a moving target like "AI".
The difference between programming in Notepad in a language you don't know and using "AI" will be huge. But the difference between being fluent in a language and having a powerful editor/IDE? Minimal at best. I actually think productivity is worse because it tricks you into wasting time via the "just one more roll" (ie. gambling) mentality. Not to mention you're not building that fluency or toolkit for yourself, making you barely more valuable than the "AI" itself.
--
The other thing is, this need for determinism bewilders me. I mean, I get where it comes from, we want nice, predictable reliable machines. But how deterministic does it need to be? If today, it decides to generate code and the variable is called fileName, and tomorrow it's filePath, as long as it's passing tests, what do I care that it's not totally deterministic and the names of the variables it generates are different? as long as it's consistent with existing code, and it passes tests, whats the importance of it being deterministic to a computer science level of rigor? It reminds me about the travelling salesman problem, or the knapsack problem. Both NP hard, but users don't care about that. They just want the computer to tell them something good enough for them to go on about their day. So if a customer comes up to you and offers you a pile of money to solve either one of those problems, do I laugh in their face, knowing damn well I won't be the one to prove that NP = P, or do I explain to them the situation, and build them software that will do the best it can, with however much compute resources they're willing to pay for?
Universal has stuff I always want (use uv instead of pip etc) while the other describes what tech choice for this project
About 12 to 6 months ago this was not the case (with or without .md files), I was getting mainly subpar result, so I’m assuming that the models have improved a lot.
Basically, I found that they not make that much of a difference, the model is either good enough or not…
I know (or at least I suppose) that these markdown files could bring some marginal improvements, but at this point, I don’t really care.
I assume this is an unpopular take because I see so many people treat these files as if they were black magic or silver bullet that 100x their already 1000x productivity.
Different use case. I assume the discussion is about having the agent implement whole features or research and fix bugs without much guidance.
It feels a lot like bikeshedding to me, maybe I’m wrong
Perhaps a function of GraphQL though.
One may argue that these should go in a README.md, but these markdowns are meant to be more streamlined for context, and it's not appropriate to put a one-liner in the imperative tone to fix model behavior in a top-level file like the README.md
Seeing "real" is a warning flag here that either-or thinking is in play.
Putting aside hopes and norms, we live in a world now where multiple kinds of agents (human and non-human) are contributing to codebases. They do not contribute equally; they work according to different mechanisms, with different strengths and weaknesses, with different economic and cultural costs.
Recall a lesson from Ralph Waldo Emerson: "a foolish consistency is the hobgoblin of little minds" [1]. Don't cling to the past; pay attention to the now, and do what works. Another way of seeing it: don't force a false equivalence between things that warrant different treatment.
If you find yourself thinking thoughts that do more harm than good (e.g. muddle rather than clarify), attempt to reframe them to better make sense of reality (which has texture and complexity).
Here's my reframing: "Documentation serves different purposes to different agents across different contexts. So plan and execute accordingly."
[1]: https://en.wikipedia.org/wiki/Wikipedia:Emerson_and_Wilde_on...
Why should we do this when anthropic specifically recommends creating multiple CLAUDE.md files in various directories where the information is specific and pertinent? It seems to me that anthropic has designed claude to look for claude.md for guidance, and randomly named markdown files may or may not stand out to it as it searches the directory.
You can place CLAUDE.md files in several locations:
> The root of your repo, or wherever you run claude from (the most common usage). Name it CLAUDE.md and check it into git so that you can share it across sessions and with your team (recommended), or name it CLAUDE.local.md and .gitignore it Any parent of the directory where you run claude. This is most useful for monorepos, where you might run claude from root/foo, and have CLAUDE.md files in both root/CLAUDE.md and root/foo/CLAUDE.md. Both of these will be pulled into context automatically Any child of the directory where you run claude. This is the inverse of the above, and in this case, Claude will pull in CLAUDE.md files on demand when you work with files in child directories Your home folder (~/.claude/CLAUDE.md), which applies it to all your claude sessions
https://www.anthropic.com/engineering/claude-code-best-pract...
Yeah, if you do this every time it works fine. If you add what you tell it every time to CLAUDE.md, it also works fine, but you don’t have to tell it any more ;)
It’s case sensitive btw. CLAUDE.md - Might explain your mixed results with it
Some explicit things I found helpful: Have the agent address you as something specific! This way you know if the agent is paying attention to your detailed instructions.
Rationality, as in the stuff practiced on early Less Wrong, gives a great language for constraining the agent, and since it's read The Sequences and everything else you can include pointers and the more you do the more it will nudge it into that mode of thought.
The explicit "This is what I'm doing, this is what I expect" pattern has been hugely useful for both me monitoring it/coming back to see what it did, and it itself. It makes it more likely to recover when it goes down a bad path.
The system reminder this article mentions is definitely there but I have not noticed it messing much with adherence. I wish there were some sort of power user mode to turn it off though!
Also, this is probably too long! But I have been experimenting and iterating for a while, and this is what is working best currently. Not that I've been able to hold any other part constant -- Opus 4.5 really is remarkable.
[0]: https://gist.github.com/ctoth/d8e629209ff1d9748185b9830fa4e7...
Have we really reached the low point that we need tutorials on how to coerce a LLM into doing what we want instead of just....writing the god damn code?
You can easily test this by adding some mandatory instruction into the file. E.g. "Any new method you write must have less than 50 lines or code." Then use Claude for ten minutes and watch it blow through this limit again and again.
I use CC and Codex extensively and I constantly am resetting my context and manually pasting my custom instructions in again and again, because these models DO NOT remember or pay attention to Claude.md or Agents.md etc.
Write readmes for humans, not LLMs. That's where the ball is going.
Yes README.md should still be written for humans and isn’t going away anytime soon.
CLAUDE.md is a convention used by claude code, and AGENTS.md is used by other coding agents. Both are intended to be supplemental to the README and are deterministically injected into the agent’s context.
It’s a configuration point for the harness, it’s not intended to replace the README.
Some of the advice in here will undoubtedly age poorly as harnesses change and models improve, but some of the generic principles will stay the same - e.g. that you shouldn’t use an LLM to do a linter &formatter’s job, or that LLMs are stateless and need to be onboarded into the codebase, and having some deterministically-injected instructions to achieve that is useful instead of relying on the agent to non-deterministically derive all that info by reading config and package files
The post isn’t really intended to be super forward-looking as much as “here’s how to use this coding agent harness configuration point as best as we know how to right now”
Why is that good advice? If that thing is eventually supposed to do the most tricky coding tasks, and already a year ago could have won a medal at the informatics olympics, then why wouldn't it eventually be able to tell if I'm using 2 or 4 spaces and format my code accordingly? Either it's going to change the world, then this is a trivial task, or it's all vaporware, then what are we even discussing..
> or that LLMs are stateless and need to be onboarded into the codebase
What? Why would that be a reasonable assumption/prediction for even near term agent capabilities? Providing it with some kind of local memory to dump its learned-so-far state of the world shouldn't be too hard. Isn't it supposed to already be treated like a junior dev? All junior devs I'm working with remember what I told them 2 weeks ago. Surely a coding agent can eventually support that too.
This whole CLAUDE.md thing seems a temporary kludge until such basic features are sorted out, and I'm seriously surprised how much time folks are spending to make that early broken state less painful to work with. All that precious knowledge y'all are building will be worthless a year or two from now.
And that describes the issues I had with “automatic memories” features things like ChatGPT had. Turns out it is an awful judge of things to remember. Like it would make memories like “cruffle is trying to make pepper soup with chicken stock”! Which it would then parrot back to me at some point 4 months later and I’d be like “WTF I figured it out”. The “# remember this” is much more powerful because know how sticky this stuff gets and id rather have it over index on my own forceful memories than random shit it decided.
I dunno. All I’m saying is you are right. The future is in having these things do a better job of remembering. And I don’t know if LLMs are the right tool for that. Keyword search isn’t either though. And vector search might not be either—I think it suffers from the same kinds of “catchy tune attack” an LLM might.
Somebody will figure it out somehow.
That’s why they’re junior
It's not that an agent doesn't know if you're using 2 or 4 spaces in your code; it comes down to:
- there are many ways to ensure your code is formatted correctly; that's what .editorconfig [1] is for.
- in a halfway serious project, incorrectly formatted code shouldn't reach the LLM in the first place
- tokens are relatively cheap but they're not free on a paid plan; why spend tokens on something linters and formatters can do deterministically and for free?
If you wanted Claude Code to handle linting automatically, you're better off taking that out of CLAUDE.md and creating a Skill [2].
> What? Why would that be a reasonable assumption/prediction for even near-term agent capabilities? Providing it with some kind of local memory to dump its learned-so-far state of the world shouldn't be too hard. Isn't it supposed to already be treated like a junior dev? All junior devs I'm working with remember what I told them 2 weeks ago. Surely a coding agent can eventually support that too.
It wasn't mentioned in the article, but Claude Code, for example, does save each chat session by default. You can come back to a project and type `claude --resume` and you'll get a list of past Claude Code sessions that you can pick up from where you left off.
This is the exact reason for the advice: The LLM already is able to follow coding conventions by just looking at the surrounding code which was already included in the context. So by adding your coding conventions to the claude.md, you are just using more context for no gain.
And another reason to not use an agent for linting/formatting(i.e. prompting to "format this code for me") is that dedicated linters/formatters are faster and only take maybe a single cent of electricity to run whereas using an LLM to do that job will cost multiple dollars if not more.
Should do this for human developers too. Can't count the number of times I've been thrown onto a project and had to spend a significant amount of time opening and skimming files just to answer simple questions that should be answered in high-level docs like this.
But in all seriousness, it's working. I write cursor rules religiously and I point other devs to them. Its great.
I didn’t dive into that because in a lot of cases it’s not necessary and I wanted to keep the post short, but for large monorepos it’s a good idea
Actually having official guidelines in their docs would be a good entrypoint, even though I guess we have this which is the closest available from anything official for now: https://www.claude.com/blog/using-claude-md-files
One interesting thing I also noticed and used recently is that Claude Code ships with a @agent-claude-code-guide. I've used it to review and update my dev workflow / CLAUDE.md file but I've got mixed feelings on the discussion with the subagent.
We used cloudflare’s AI gateway which is pretty simple. Set one up, get the proxy URL and set it through the env var, very plug-and-play
On phone else I’d post commands
This way, it's got more of a chance in generating something that I wanted, rather than running off on it's own.
Doesn't that mean that Claude Code's system prompt exhausts that budget before you even get to CLAUDE.md and the user prompt?
Edit: They say Claude Code's system prompt has 50. I might have misjudged then. It seemed pretty verbose to me!
The part about smaller models attending to fewer instructions is interesting too, since most of what was added doesn't seem necessary for the big models. I thought they added them so Haiku could handle the job as well, despite a relative lack of common sense.
I used to instruct about coding style (prefer functions, avoid classes, use structs for complex params and returns, avoid member functions unless needed by shared state, avoid superfluous comments, avoid silly utf8 glyphs, AoS vs SoA, dry, etc)
I removed all my instructions and it basically never violates those points.
What I find most interesting is how a hierarchical / recursive context construct begins to emerge. The authors' note of "root" claude.md as well as the opening comments on LLMs being stateless ring to me like a bell. I think soon we will start seeing stateful LLMs, via clever manipulation of scope and context. Something akin to memory, as we humans perceive it.
<system-reminder> IMPORTANT: this context may or may not be relevant to your tasks. You should not respond to this context unless it is highly relevant to your task. </system-reminder>
Perhaps a small proxy between Claude code and the API to enforce following CLAUDE.md may improve things… I may try this
Is it a good one?
But as others are saying this is just basic documentation that should be done anyway.
Is this intentional? Is AoC designed as an elite challenge, or is the journey more important than finishing?
I rarely get past 18 or so. The stats for last year are here: https://adventofcode.com/2024/stats
If you're using VSCode, that is automatically added to context (and I think in Zed that happens as well, although I can't verify right now).
That said, a lot of what it can deduce by looking at the code is exactly what you shouldn't include, since it will usually deduce that stuff just by interacting with the code base. Claude doesn't seem good at that.
An example of both overly-verbose and unnecessary:
### 1. Identify the Working Directory
When a user asks you to work on something:
1. *Check which project* they're referring to
2. *Change to that directory* explicitly if needed
3. *Stay in that directory* for file operations
```bash
# Example: Working on ProjectAlpha
cd /home/user/code/ProjectAlpha
```
(The one sentence version is "Each project has a subfolder; use pwd to make sure you're in the right directory", and the ideal version is probably just letting it occasionally spend 60 seconds confused, until it remembers pwd exists)
I think most people who use Claude regularly have probably come to the same conclusions as the article. A few bits of high-level info, some behavior stuff, and pointers to actual docs. Load docs as-needed, either by prompt or by skill. Work through lists and constantly update status so you can clear context and pick up where you left off. Any other approach eats too much context.
If you have a complex feature that would require ingesting too many large docs, you can ask Claude to determine exactly what it needs to build the appropriate context for that feature and save that to a context doc that you load at the beginning of each session.
Read your instructions from Agents.md
OMG this finally makes sense.
Is there any way to turn off this behavior?
Or better yet is there a way to filter the context that is being sent?
I would love to see it extended to show Codex, which to my mind is by far the best at rule-following. (I'd also be curious to see how Gemini 3 performs.)
Why not just to show one?
This is a news for me. And at the same time it isn’t. Without the knowledge of how the models actually work, most of the prompting is guesstimate at best. You have no control over models via prompts.
Also, while it may be hip to call any LLM output slop, that really isn't the case. Look at what a poor history we have of developer documentation. LLMs may not be great at everything, but they're actually quite capable when it comes to technical documentation. Even a 1-shot attempt by LLM is often way better than many devs who either can't write very well, or just can't be bothered to.
Consider that if the only code you get out of the autoregressive token prediction machine is slop, that this indicates more about the quality of your code than the quality of the autoregressive token prediction machine
Considering that the "input" to these models is essentially all public code in existence, the direct context input is a drop in the bucket.
I have a full system of agents, hooks, skills, and commands, and it all works for me quite well.
I believe is massive context, but targetted context. It has to be valuable, and important.
My agents are large. My skills are large. Etc etc.
A good Claude.md - I don’t know, presumably the article explains.