Anatomy of the .claude/ folder
blog.dailydoseofds.com
blog.dailydoseofds.com
Plain Claude, ask it to write a plan, review plan, then tell it to execute still works the best in my experience.
The reality is that if you actually know what you want, and can communicate it well (where the productivity app can be helpful), then you can do a lot with AI.
My experience is that most people don't actually know what they want. Or they don't understand what goes into what they want. Asking for a plan is a shortcut to gaining that understanding.
So, to really create something new that I care about, LLMs don't help much.
They are still useful for plenty of other tasks.
This particular skill is not great.
And i drawn a tarot card and the card refuted your gut feeling.
Joking asside, there is no reason to suspect that an LLM is telling you correct information about how best to use an LLM. The way to confirm your thesis is by running experiments, or finding someone who has already done them. It is a valid move to use an LLM to find primary sources, but in itself the LLM is not an authority you could or should trust.
I can’t tell you how many times I have a CS student in my office for advising and they tell me they only want to take technical courses, because anything reading or writing or psychology or history based is “soft”, unrelated to their major, and a waste of their time.
I’ve spent years telling them critical reading and expressive writing skills are very important to being a functioning adult, but they insist what they need to know can only be found in the Engineering college.
Engineers who lack soft skills cannot be effective in team environments.
To be fair, LLMs can be quite useful for quickly finding the correct place in TFM to look when you don't necessarily have a function or feature name to go on.
1000%. This is why people whose job it was to figure out how to make a thing are thriving with AI tools, and those who operate in the conceptual/abstract are flailing and frustrated with it. (it really is a mirror in this case, and the frustration they have is unknowingly directed at themselves)
Working on an unspecified codebase of unknown size using unconfigured tooling with unstated goals found that less configuration worked better than more.
But for some projects there will be things Claude doesn’t know about, or things that you repeatedly want done a specific way and don’t want to type it in every prompt.
1. I have many and sometimes contradictory workflows: exploration, prototyping, bug fixing debugging, feature work, pr management, etc. When I'm prototyping, I want reward hacking, I don't care about tests or lint's, and it's the exact opposite when I manage prs.
2. I see hard to explain and quantify problems with over configuration. The quality goes down, it loses track faster, it gets caught in loops. This is totally anecdotal, but I've seen it across a number of projects. My hypothesis is that is related to attention, specifically since these get added to the system prompt, they pull the distribution by constantly being attended to.
3. The models keep getting better. Similar to 2, sometime model gains are canceled out by previously necessary instructions. I hear the anthropic folks clear their claude.md every 30 days or so to alleviate this.
skills that teach the agent how to pipe data, build requests, trace them through a system and datasources, then update code based on those results are a step function improvement in development.
ai has fundamentally changed how productive i am working on a 10m line codebase, and i'd guess less than 5% of that is due to code gen thats intended to go to prod. Nearly all of it is the ability to rapidly build tools and toolchains to test and verify what i'm doing.
What sort of skills are you referring to?
Skills are crazy useful to tell Claude how to debug your particular project, especially when you have a library of useful scripts for doing so.
Claude is kind of decent at doing "when in Rome" sort of stuff with your codebase, but it's nice to reinforce, and remind it how to deploy, what testing should be done before a PR, etc.
I'd love to know how this skill was phased.
It's certainly an iterative process but it gets better every iteration.
And also we know why: effective context depends on inout and task complexity. Our best guess right now is that we are often between 100k to 200k effective context length for frontier, 1m NIHS type models
So I naturally felt the need to (tell Claude to) build a MCP for this accounting API, and now I ask it to do accounting tasks, and then it just does them. It's really ducking sweet.
Another thing I did was, after a particularly grueling accounting month close out, I've told Claude to extract the general tasks that we accomplished, and build a skill that does it at the end of the month, and now it's like having a junior accountant in at my disposal - it just DOES the things a professional would charge me thousands for.
So both custom project MCPs and skills are super useful in my experience.
(I'm genuinely asking)
To give you a small taste: you need to issue an electronic invoice for each unique customer, and submit on the fly the tax authority - but these need to correlated monthly with the money in your business bank account. The paid invoices don't just go into your bank account, they are disbursed from time to time by the payment processor, on random dates that don't sync with the accounting month, so at end of month you have to have correlate precisely what invoice is paid or not. But wait, the card processor won't just send you the money in a lump sum, it will deduct from each payment some random fee that is determined by their internal formula, then, at the end of each month, add all those deducted fees (even for payments that have not been paid to you) and issue another invoice to you, which you need to account for in you books as being partially paid each month (from the fees deducted from payments already disbursed). You also have other payment channels, each with their fees etc. So I need to balance this whole overlapping intervals mess with all sort of edge cases, chargebacks and manual interventions I refuse to think about again.
This is one example, but there are also issues with wages and their taxation, random tax law changes in the middle of the month etc. The accountant can of course solve all this for you, but once you go a few hundred invoices per month (if you sell relatively cheap services) you are considered a "medium" business, so instead of paying for basic accounting services less than 100€ per month (have the certified accountant look over your books and sign them, as required by law), you will need more expensive packages which definitely add up to thousands in a few months.
Go be an entrepreneur, they said.
Claude and an mcp and skill is plain to me. Writing your own agent connecting to LLMs to try to be better than Claude code, using Ralph loops and so on is the rabbit hole.
Though, you get such a huge bang from customizing your config that I can easily see how you could go down that slippery slope.
I’m still new to this, but the first obvious inefficiency I see is that I’m repeating context between sessions, copying .md files around, and generally not gaining any efficiency between each interaction. My only priority right now is to eliminate this repetition so I can free up buffer space for the next repetition to be eliminated. And I don’t want to put any effort into this.
How are you guys organizing this sort of compounding context bank? I’m talking about basic information like “this is my job, these are the products I own, here’s the most recent docs about them, here’s how you use them, etc.” I would love to point it to a few public docs sites and be done, but that’s not the reality of PM work on relatively new/instable products. I’ve got all sorts of docs, some duplicated, some outdated, some seemingly important but actually totally wrong… I can’t just point the agent at my whole Drive and ask it to understand me.
Should I tell my agent to create or update a Skill file every time I find myself repeating the same context more than twice? Should I put the effort into gathering all the best quality docs into a single Drive folder and point it there? Should I make some hooks to update these files when new context appears?
- A well-structured folder of markdown files that I constantly garden. Every sub-folder has a README. Every files has metadata in front-matter. I point new sessions at the entry point to this documentation. Constantly run agents that clean up dead references, update out of date information, etc. Build scripts that deterministically find broken links. It's an ongoing battle.
- A "continuation prompt" skill, that prompts the agent to collect all relevant context for another agent to continue
- Judicious usage of "memory"
- Structured systems made out of skills like GSD (Get Shit Done)
- Systems of "quality gate" hooks and test harnesses
For all of these, I have the agent set them up and manage them, but I've yet to find a context-management system that just works. I don't think we understand the "physics" of context management yet.
Great docs help you, your agents, your team and your customers.
If you’re confused and the agent can’t figure it out reliably how can anyone?
Easier said than done of course. And harder now than ever if the products are rapidly changing from agentic coding too.
One of my only universal AGENTS.md rules is:
> Write the pull request title and description as customer facing release notes.
One quick win I’ve thought could bridge this is updating our docs site to respond to `Accept: text/markdown` requests with the markdown version of the docs.
We used to have the very difficult task of producing working scalable maintainable code describing complex systems which do what we need them to do.
Now on top of it we have the difficult task of producing this code using constantly mutating complex nondeterministic systems.
We are the circus bear riding a bicycle on a high wire now being asked to also spin plates and juggle chainsaws.
Maybe singularity means that time sunk into managing LLMs is equal to time needed to manually code similar output in assembly or punch cards.
e.g. spend time creating a skill about how to query production logs
All the fancy frameworks are vibe coded, so why could they do better than something you do by yourself?
At most get playwright MCP in so the agent can see the rendered output
my only machine-specific config is overriding haiku usage with sonnet in claude code. i outline what i want in linear, have claude synthesize into a plan and we iterate until we're both happy, then i let it rip. works great.
then one of my juniors goes and loads up things like "superpowers" and all sorts of stuff that's started littering his PRs. i'm just not convinced this ricing of agents materially improves anything.
But beyond that, I just ask it what I want it to ask, and that's it. I'm not convinced that putting more time into building the "toolbox" will actually give me significant returns on that time.
I do think that some of this (commands, skills, breaking up CLAUDE.md into separate rules files) can be useful, but it's highly context-dependent, and I think YAGNI applies here: don't front-load this work. Only set those up if you run into specific problems or situations where you think doing this work will make Claude work better.
* Claude trying to install packages into my Python system interpreter - (always use uv and venvs)
* Claude pushing to main - (don't push to main ever)
* When creating a PR, completely ignoring how to contribute (always read CONTRIBUTING.md when creating a PR)
* Yellow ANSI text in console output - (Color choices must be visible on both dark and light backgrounds)
Because I got sick of repeating myself about the basics.
For all of my projects task build runs linters, tests and builds the project with as little output as possible on a happy path.
This catches a bunch of "it's a pre-existing issue" stuff from Claude. Sometimes I ask it to run build first, then start implementing just so that it can prove to itself that no, it wasn't a pre-existing issue, you broke something.
Context still matters and I'll never stop implementing things in small slices instead of trying to one-shot.
The proper way to do this is find a personal pain point, figure out how to fix it, fix it, and then continue.
That's how I built my own system, zero skills, just a git submodule with shared guides how to do stuff the way _I_ like it. I can just refer any agent to read that directory and they'll usually get it on the first go.
Working out how to work on code on your own with agentic support is one thing. Working out how to work on it as a team where each developer is employing agentic tools is a whole different ballgame.
Is this a hangover from when the tools were not as good?
1. Provision of optional tools: I may use an ai agent differently to all other devs on a team, but it seems useful for me to have access to the same set of project-specific commands, skills & MCP configs that my colleagues do. I amn't forced to use them but I can choose to on a case by case basis.
2. Guardrails: it seems sensible to define a small subset of things you want to dissuade everyone's agents from doing to your code. This is like the agentic extension of coding standards.
Most people do, most people don’t have wildly different setups do they? I’d bet there’s a lot in common between how you write code and how your coworkers do.
Isn't this article just another one in that same drawer?
> What actually belongs in CLAUDE.md - Write: - Import conventions, naming patterns, error handling styles
Then just a few lines below:
> Don’t write: - Anything that belongs in a linter or formatter config
The article overall seems filled with internal inconsistencies, so I'm not sure this article is adding much beyond "This is what an LLM generated after I put the article title with some edits".
This is important no matter how experienced you are, but arguable the most important when you don't know what you're doing.
0: or if you don't want to learn about that, you can use Claude Code Web
Better isolation than running it in a container.
https://github.com/anthropics/claude-code
You can download the devcontainer CLI and use it to start a Docker container with a working Claude Code install, simple firewall, etc. out of the box. (I believe this is how the VSCode extension works: It uses this repo to bootstrap the devcontainer).
Basic instructions:
- Install the devcontainer CLI: `https://github.com/devcontainers/cli#install-script`
- Clone the Claude Code repo: `https://github.com/anthropics/claude-code`
- Navigate to the top-level repo directory and bring up the container: `devcontainer --workspace-folder . up`
- Start Claude in the container: `devcontainer exec --workspace-folder . bash -c "exec claude"`
P.S. It's all just Docker containers under the hood.
There many decent options (cloud VMs, local VMs, Docker, the built-in sandboxing). My point is just that folks should research and set up at least one of them before running an agent.
I know the deny list is only for automatically denying, and that non-explicitly allowed command will pause, waiting for user input confirmation. But still it reminds me of the rationale the author of the Pi harness [1] gave to explain why there will be no permission feature built-in in Pi (emphasis mine):
> If you look at the security measures in other coding agents, *they're mostly security theater*. As soon as your agent can write code and run code, it's pretty much game over. [...] If you're uncomfortable with full access, run pi inside a container or use a different tool if you need (faux) guardrails.
As you mentioned, this is a big feature of Claude Code Web (or Codex/Antigravity or whatever equivalent of other companies): they handle the sand-boxing.
[0] https://blog.dailydoseofds.com/i/191853914/settingsjson-perm...
[1] https://mariozechner.at/posts/2025-11-30-pi-coding-agent/#to...
I never said "permissions", I said "sandboxing". You can configure that in settings.json.
https://code.claude.com/docs/en/sandboxing#configure-sandbox...
It's also not targeted at first-timers getting their first taste of AI coding. It's a guide for how to use these tools to deal with frustrations you will inevitably encounter with AI coding.
Though really, many of the complaints about AI coding on HN are written by beginners who would also benefit from a simple .claude configuration that includes their preferences and some guidelines. A frequent complaint from people who do drive-by tests of AI coding tools before giving up is that the tools aren't reading their mind or the tools keep doing things the user doesn't want. Putting a couple lines into AGENTS.md or the .claude folder can fix many of those problems quickly.
For example, I have a rule [^0] that instructs Claude to never start work until some pre-conditions are met. This works well, as it always seems to check these conditions before doing anything, every turn.
I can see security teams wanting to use this approach to feel more comfortable about devs doing things with agentic tools without worrying _as much_ about them wreaking havoc (or what they consider "havoc").
As well, as someone who's just _really_ getting started with agentic dev, spending time dumping how I work into rules helped Claude not do things I disapprove of, like not signing off commits with my GPG key.
That said, these rules will never be set in stone, at least not at first.
[^0]: https://github.com/carlosonunez/bash-dotfiles/blob/main/ai/c...
If you wanted to be more “corporate” about it, then assuming that devs are using some enterprise wrapper around Claude or whatever, I would bake an instruction into the system prompt that ensures that AGENTS is only read from the main branch to force this convention.
This is harder to guarantee since these tools are non-deterministic.
cute that you think cluade gives a rat ass about this.
Everyone's workflow is different and nobody knows which workflow is the right one. If you turn your harness into a junk drawer of random skills that get auto updated, you introduce yet another layer of nondeterminism into it, and also blow up your context window.
The only skill you should probably install instead of maintaining it yourself is playwright-cli, but that's pretty much it.
In practice, I also find it more useful that the Chrome MCP uses my current profile since I might want Claude to look at some page I'm already logged in to.
I'm not very sophisticated here though. I mainly use use browser MCP to get around the fact that 30% of servers block agent traffic like Apple's documentation.
Chrome MCP is much slower and by default pretty much unusable because Claude seems to prefer to read state from screenshots. Also, no Firefox/Safari support means no cross-browser testing.
There appears to be https://github.com/sumyapp/playwright-parallel-mcp which may be worth trying.
You do not want to log in one day to find your favorite workflow has changed via updates.
Then again this is all personal preference as well.
Ignore original comment below, as the post is technical so is the parent comment: for techies
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That applies to tech users only.
Non-tech users starting to use Claude code and won't care to get the job done
Claude introduced skills is to bring more non-tech users to CLI as a good way to get your feet wet.
Not everyone will go for such minute tweaks.
I am an adminstrator of this stuff at my company and it’s an absolute effing nightmare devising policies that protect people from themselves. If I heard this come out of someone’s mouth underneath me I’d tell them to leave the room before I have a stroke.
And this is stuff like, if so and so’s machine is compromised, it could cost the company massive sums of money. for your personal use, fine, but hearing this cavalier attitude like it doesn’t matter is horrifying, because it absolutely does in a lot of contexts.
LLMs have completely killed my motivation to continue running it. None of standard practices apply anymore
I also import skills or groups of skills like Superpowers (https://github.com/obra/superpowers) when I want to try out someone else's approach to claude code for a while.
1. Had to consume context and turns by reading files, searching web, running several commands for what was otherwise a straightforward task
2. Whatever tool it used wasn't designed with agent usage in mind. Which most of the time will mean agent has to do tail, head, grep on the output by re-running the same command.
Then you create a skill that teaches how to do this in fewer turns, possibly even adding custom scripts it can use as part of that skill.
You almost never need a skill per se, most models will figure things out themselves eventually, skill is usually just an optimization technique.
Apart from this, you can also use it to teach your own protocols and conventions. For example, I have skills that teach Claude, Codex, Gemini how to communicate between themselves using tmux with some helper scripts. And then another skill that tell it to do a code review using two models from two providers, synthesize findings from both and flag anything that both reported.
Although, I have abandoned the built-in skill system completely, instead using my own tmux wrapper that injects them using predefined triggers, but this is stepping into more advanced territory. Built in skill system will serve you well initially, and since skills are nothing but markdown files + maybe some scripts, you can migrate them easily into whatever you want later.
Create .claude/agents/ when you have a narrow scoped thing that you want to happen, but not pollute the main context and have a well formed output. Also handy for reducing costs by using a different model for a class of actions you repeat often. For example, the "lookup related issues" from `/triage` is a Sonnet agent that finds Trac issues for a bug reports. Main context only needs the small list of matches and not the dozens of not relevant ones.
Create .claude/skills/ for job description like behaviors you want. For example, code reviewer or security researcher.
For any of these, install the `/plugin` skill-creator. Run `/init` if you don't have a claude.md and then ask the skill creator to help you improve the claude.md and create useful commands and skills.
IMHO most of this “customize your config to be more productive” stuff will go away within a year, obsoleted by improved models and harnesses.
Just like how all the lessons for how to use LLMs in code from 1-2 years ago are already long forgotten.
Perhaps your blanket statement could be wrong, and I would encourage you to let your mind be a bit more open. The landscape here is not what it was 6 months ago. This is an undeniable fact that people are going to have to come to terms with pretty soon. I did not want to be in this spot, I was forced to out of necessity, because the stuff does work.
The Vercel team had some interesting findings[1]:
> In 56% of eval cases, the skill was never invoked. The agent had access to the documentation but didn't use it.
Others had different findings for commonly accepted practices[2], some you may have adopted from reading documentation, which surely didn't come from influencers.
And yet others swear by magical Markdown documents[3].
So... who is the ultimate authority on what actually works, and who is just cargo culting the trendy practice of the week? And how is any of this different from what was being done a few years ago?
[1]: https://vercel.com/blog/agents-md-outperforms-skills-in-our-...
[2]: https://arxiv.org/abs/2602.11988
[3]: https://soul.md/
I measure the tooling success with a suite of small prompt tests performing repeatable tasks, measuring the success rate over time, educating the broader team, and providing my own tried and tested in the field skills that I’ve shared to similar successes to the broader teams. We’ve seen a huge increase in velocity and lower bug rate, which are also very easily measurable (and long evaluated stats) enough to put me in the position I am, which was not a reluctant one. You’re perfectly free to view my long history on this topic on this forum to see I am a complete skeptic on this topic, and wouldn’t be here unless I had to.
everyone is figuring this out still. There is no authority, I am my own authority on what I have seen work and what hasn’t. Feel free to take of that what you will. I just wanted to provide a counterpoint to your initial claim. I’m certainly not going to expose to a fine degree what has worked for my org and what hasn’t due to obvious reasons.
have a good day!
https://theonion.com/area-man-constantly-mentioning-he-doesn...
which is basically every setup because claude sucks at calling skills and forget everything in claude.md with a few seconds.
>If you tell Claude to always write tests before implementation, it will. If you say “never use console.log for error handling, always use the custom logger module,” it will respect that every time.
It just isn't true lol
My point is, these custom things are often short lived band-aids, and may not be needed with better default harnesses or smarter future models.
I’ve been developing and working on dev tools for more than 15 years. I’ve never seen things evolve so rapidly.
Experiment, have fun and get things done, but don’t get too sure or attached to your patches.
It’s very likely the models and harnesses will keep improving around the gaps you see.
I’ve seen most of my AGENTS.md directives and custom tools fade away too, as the agents get better and better at reading the code and running the tests and feeding back on themselves.
Always separate plan from implementation and clear context between, its the build up of context that makes it bad ime.
Don't skills sit in context while custom slash commands are only manually invoked?
The difference isn't clear to me, especially since, upon googling it right now, I see that skills can also be invoked with a /slash.
It's not against the rules to post AI slop here, and I don't necessarily think it should be. But I do wonder how we value written content going forward. There's value to taste and style and editing and all the other human things... there's very little value in the actual words themselves. We'll figure it out.
I don't think people realize exactly how important the specific prompts are, with the same prompt you'd get wildly different results for different models, and when you're iterating on a prompt (say for some processing), you'd do different changes depending on what model is being used.
Would also be interested in examples of a CLAUDE.md file that works well in Claude, but works poorly with Codex.
Actually, I can just read the skill with my own eyes and then I can also learn. So, thank you for sharing. It's interesting to read through what it suggests for different models - it fits for the ones I work with regularly, but there are many I don't know the strengths and weaknesses of.
I get the best results with the least number of skills and unnecessary configuration in place.
People are spending way too much time over-prescribing these documents, but AI is like a competent but nervous adult. The more you give it, the dumber it gets.
[0] https://claudefa.st/blog/guide/mechanics/claude-md-mastery
TLDR "CLAUDE.md isn't documentation for Claude to read - it's an operating system for Claude to run. Define behavior, delegate knowledge to skills, and build a system that improves itself over time."
They look to me like people actually want to build deterministic workflows, but blobs of text are the wrong approach for that. The right tool is code that controls the agent through specific states and validates the tool calls step by step.
Ultimately you can't force claude to solve any problem but you could make it so constraints are kept.
A simple way is a git hook thay runs all the deterministic things you care about.
Getting claude to follow your guidance files consistently is a bit maddening.
Then all I have to do is let the agents actually figure out how to accomplish what I ask of them, with the highly scoped set of tools and sub agents I give them.
I find this works phenomenally, because all the .agent.md file is, is a description of what the tools available are. Nothing more complex, no LARP instructions. Just a straightforward 'here's what you've got'.
And with agents able to delegate to sub agents, the workflow is self-directing.
Working with a specific build system? Vibe code an MCP server for it.
Making a tool of my own? MCP server for dev testing and later use by agents.
On the flipside, I find it very questionable what value skills and reusable prompts give. I would compare it to an architect playing a recording of themselves from weeks ago when talking to their developers. The models encode a lot of knowledge, they just need orientation, not badgering, at this point.
The best thing I’ve done so far is put GitHub behind an API proxy and reject pushes and pull requests that don’t meet a criteria, plus a descriptive error.
I find it forgets to read or follow skills a lot of the time, but it does always try to route around HTTP 400s when pushing up its work.
Not just CLAUDE.md but everything, the prompts you type in a session, skill descriptions, agent configs, all of it. More instructions does not mean better results. Claude actually performs noticeably better with brief focused input. The moment you start over-specifying things quality drops.
Just my observation from using it daily but it's been consistent enough that I figured it's worth sharing.
This sort of "prompt and pray" flow really works for people, as in they can make products and money, however, I do think the people that succeed today also would've reached for no-code tools 5 years ago and seen similar success. It's just faster and more comprehensive now. I think the general theme of the products remains the same though; not un-important or worthless, but it tends to be software that has effects that say INSIDE the realm of software. I feel like there's always been a market for that, as it IS important, it's just not WORTH the time and money to the right people to "engineer" those tools. A lot of SaaS products filled that niche for many years.
While it's not a way I want to work, I am also becoming comfortable with respecting that as a different profession for producing a certain brand of software that does have value, and that I wasn't making before. The intersection of that is opportunity I'm missing out on; no fault to anyone taking it!
The software engineer that writes the air traffic avoidance system for a plane better take their job seriously, understand every change they make, and be able to maintain software indefinitely. People might not care a ton about how their sales tracking software is engineered, but they really care about the engineering of the airplane software.
This is a brilliant reimagining of the old and trusted PnP acronym.
It shouldn’t be, but it’s going to take some catastrophic events to convince people that we have to work to make sure we understand the systems we’re building and keep everything from devolving into vibe coded slop.
I guess that's why I see it as a separate profession, as in we have to actually profess a standard for how a professional in our field acts and believes. I think it's OK for it to bifurcate into two different fields, but Software Engineering would need to specifically reject prompt-and-pray on a principled and rational basis.
Sadly yes, that might require real cost to life in order to find out the "why" side of that rational basis. If you meet anyone that went to an engineering school in Québec, ask them about the ceremony they did and the ring they received. [0] It's not like that ceremony fixes anything, but it's a solemn declaration of responsibility which to me at least, sets a contract with society that says "we won't make things that harm you".
[0] https://ironring.ca/home-en/
> [The] history of the 1907 failure of the Quebec City bridge, which was the inspiration for the Calling of an Engineer ceremony.>Claude Code users typically treat the .claude folder like a black box. They know it exists. They’ve seen it appear in their project root. But they’ve never opened it, let alone understood what every file inside it does.
I know we are living in a post-engineering world now, but you can't tell me that people don't look at PRs anymore, or their own diffs, at least until/if they decide to .gitignore .claude.
I'm a senior engineer who has been shipping code since before GitHub and PR reviews was a thing. Thankfully LLMs have freed me from being asked to read other people's shit code for hours every day.
I recently tried IntelliJ for Kotlin development and it wanted me to give a credit card for a 30 day trial. I just want something that scans my repo and I tell it the changes I want and it does it. If possible, it would also run the existing tests to make sure its changes don't break anything.
Opencoder is bring your own model.
You get what you pay for so good luck.
You log in with your Goggle account.
While the coding assistants are pretty much universally free, you still need to connect them to a model. The model tokens generally cost something once you've gone past a certain quota.
I'm not sure if this is still true, but if you have a Google account, Gemini Code Assist had a quite generous "free tier" that I used for a while and found it do be pretty decent.
It is fun to use.
It has a few issues with outdated advice (e.g. commands has been merged with skills), but overall I might use share it with co-workers who needs an introduction to the concept.
Coming soon, unit, behavioural and regression tests for your prompts and skills :P
You’ll have:
* Claude model version
* Claude Code prompts and tools
* Your own prompts and skills and whatnot
* Your repository’s source code (= the input)
All of those change constantly, it’s not like it’s some kind of SWE benchmark.
Just read the official Claude documentation:
Do people find the nano-banana cartoon infographics to be helpful, or distracting? Personally, I'm starting to tire seeing all the little cartoon people and the faux-hand-drawn images.
Wouldn't Tufte call this chartjunk?
But they had already lost me at all the links, and the fact there's not a red wire through the entire article.
The first thing my eyes skimmed was:
> CLAUDE.md: Claude’s instruction manual
> This is the most important file in the entire system. When you start a Claude Code session, the first thing it reads is CLAUDE.md. It loads it straight into the system prompt and keeps it in mind for the entire conversation.
No it's not. Claude does not read this until it is relevant. And if it does, it's not SOT. So no, it's argumentatively not the most important file.
Like mostly people who have confused luck and success, or business acumen for religion.
So I wouldn’t use LinkedIn as a positive data point of what’s hot.
https://code.claude.com/docs/en/memory
“CLAUDE.md files are loaded into the context window at the start of every session”
The simple truth we're about to realize is there is no free lunch: a tool cannot inject more intent into a piece than its author put in. It might smooth out some blemishes or highlight some alternative choices, but it can't transform the input "make me a video game" into something greater than a statistical mix-mash of the concept. And traditional tools of automation give you a much better, more precise interface for intent than natural language, which allows these vagaries.
Feels like generated AI art like this is modern clipart
I think the problem is that they're uninformative slop often enough that I've subconsciously determined they aren't worth risking attention time on.
So yes, it's chartjunk.
In this case, I'd say helpful because I didn't have to read the article at all to understand what was being communicated.
Some of the others, I don’t feel like added value, but I agree that these are some of the best of a practice that I agreed does not add a ton of value typically
Let’s say lose those and using emojis as bullet points. It’s going to be a lot harder to detect.
> Clutter is the disease of American writing. We are a society strangling in unnecessary words, circular constructions, pompous frills and meaningless jargon.
> Look for the clutter in your writing and prune it ruthlessly. Be grateful for everything you can throw away. Reexamine each sentence you put on paper. Is every word doing new work? Can any thought be expressed with more economy?
On Writing Well (Zinsser)
When you have this performative folder of skills the AI wastes a bunch of tool calls, gets confused, doesn't get to the meat of the problem.
beware!
No.
CLAUDE.md is just prompt text. Compaction rewrites prompt text.
If it matters, enforce it in other ways.
In the end it will still produce slop you need to review line by line.
The question is: Do you want to write code you know and verified that works, or review code written by AI that is of junior dev quality that is not verified.
> "The project-level folder holds team configuration. You commit it to git. Everyone on the team gets the same rules, the same custom commands, the same permission policies."
> "Most people either write too much or too little. Here’s what works."
It feels like I've been teleported into a recent LinkedIn feed. Do real people actually already write like AI or is it AI generated?
> Two folders, not one
Why post AI slop here?