I-have-ADHD: A skill to stop coding agents from burying the answer

Meanwhile Fable consistently ignores all my requests to write this exact way. I mean, the bare minimum I ask it for it to itemize lists and not write in single long passages using comas, semicolons and 'and's. Still ignores them.

I honestly think it's time to call Astra the SOTA. It may not lead all the benchmarks but it genuinely feels much superior of a model. Not to mention the ¢20 Codex plan with frequent resets (https://codex-resets.com/) gives me roughly as much allowance as the ¢90 Claude plan, especially with recent limit cuts on Anthropic plans.

## Writing guidelines

These apply to documentation, code comments, commit and PR messages, and replies to the user.

- Write precisely in clear, complete sentences; keep text concise and proportional to task complexity.

- Stay focused: avoid filler, repetition, over-the-top detail, and tangents the user did not ask for. Once a fact is stated, do not restate it for effect ("so the commit landed on a branch nobody was going to merge"). Avoid editorialising.

- Always prefer ISO 24495-1:2023 conformant plain language over dense technical jargon: short sentences, one idea per sentence, define terms on first use.

- When reporting your own mistake, give the cause and the fix in one sentence each; no apology, no framing ("the mistake was mine"), no post-mortem.

- Never use em dashes or cataphoric teasers such as "Here's the thing" or "But there's a catch".

Like how theme parks generally don't do much to keep queues short (or Disney charges you a premium to skip the queue)

> Perfection is achieved, not when there is nothing more to add, but when there is nothing left to take away.

Long story short, I ended up looking at other providers and models like Kimi K3 and GLM 5.3 and eventually just stuck with OpenAI (more limits, despite smaller context), none of them have such pronounced issues with the tone and writing like Claude does - seems like they were working on it with 5.1 but I'd almost classify it as a form of model collapse.

I wince whenever I catch Claudisms on websites and elsewhere. Same as with that pulsating circle that indicates nothing.

https://code.claude.com/docs/en/output-styles

We'll see if they can do it, I originally got into Claude Code because it, at the time, felt more accessible/conversational than Codex/Gemini. Now it's shifted to say the least

It made it write more like a dev than a marketing agent.

It's fascinating, Sonnet 4 is still available via API and it's so much less moronic than the current model. All of the em-dashes and nonsense are a result of the repeated rounds of reinforcement learning using slop data.

I noticed the overuse of the word "sharper" or "sharp" in a science paper on ArXiV and my first reaction was "Ewww... AI slop!", but then I checked the date and it was 2020.

It looks like at least some AI-isms stem from the particular style of language commonly used in science papers. Several frontier labs have mentioned heavily weighting those during pre-training because higher quality inputs result in a higher quality model.

If you're using Claude Code, then it's in the harness. At the close of many sessions, I would start a meta conversation over why the LLM would consistently break certain rules. What it found when debugging itself is that some of the "contradicting" rules that I had were in fact, not from my rules. Instead, the instructions from its own harness had phrases telling it to do things like that. When something contradicts, its own instructions would outweigh any custom ones you write. Every rule variant I had tested (including the one that says it overrides the harness instructions - and yes, I've actually tested all the ideas in your comment too) has ultimately been unsuccessful due to this according to the LLM.

Firstly when you've instructed it ( possibly through skills ) not to do something. It'll keep reminding you that it didn't do that. So I might say, "Check out and review this PR, do not make comments on it", and then it'll be keen to point out it hasn't posted comments to the PR.

But more often it happens when it tries one approach, gets itself messed up, and then has to back out that approach, clean up its mess and do something else.

It'll often then spend more time explaining the wrong approach than the right one, which can be frustrating, especially if all its working is buried in the detailed transcripts.

You can also ask why did he mentioned something that wasn't done or why he thought this was important.

In my AGENTS.md file I have an instruction telling the agent to never commit any changes unless I explicitly ask for it, and this leads to messages similar to what you just described.

    Concise: Claude leads with the result, skips preamble and narration, and
    keeps responses short by default, while doing the engineering work as
    thoroughly as in the Default style. When you ask for an explanation or
    more detail, Claude answers in full. Claude always keeps the complete
    content of error reports, security warnings, and confirmations for
    destructive actions. Requires Claude Code v2.1.237 or later.

So if it gets bad I simply tell it "I ain't reading all that, feed it through the STE Gate" and it will tame the results. I haven't bothered to set it up as a hook yet.

Which sounds more like Claude has ADHD than the user does.

DeepSeek v4 Flash isn’t much better (unsurprising- it’s an extremely stubborn model).

Weirdly, GPT Luna excels at following this type of instruction from AGENTS.md, and never forgetting it, even 400k+ tokens into the context window.

Even MCPs are not safe. For example Notion injected ads [1] to its official MCP connector to advertise products mid-task.

[1]: https://old.reddit.com/r/ClaudeAI/comments/1w9dluw/notions_o...

Instead of describing to the user how to setup their dev environment, section 3 basically instructs the agent to install all developer tools required for the application to operate in development mode.

The skill simply demands concise and well-formatted responses from an agent. It is something demanded by anybody daily-driving agents for their actual job since >75% of the text output from agents is fluff. THis would better be named `/i-wont-read-that-heap-of-garbage`

I usually follow up with an "I'm not reading all that" and make it summarize.

Also, somehow over the weekend it responded with these sections all clearly laid out - What landed, Decisions I made and recorded, Two findings, and What you need to do. Not sure why it can't do that all the time.

I also don't know how much to trust the model, but I've had the model tell me specifically that certain aspects of ASD-STE100 are unactionable and will just create more noise.

The OP's own skill even leads with something in a very similar vein:

> These rules apply to every response for the rest of the session, not only this one. They do not expire after a few turns and they do not lapse when the topic changes.

My understanding is that phrases like this are at best a _very_ weak signal to the model. It's simply contradictory to how the model works at a level that can't be overridden by injecting tokens.

https://news.ycombinator.com/item?id=46871173

Anyways the most layman way I’ve seen it explained is this: skills help save token usage for the right context. Not every request needs all instructions all the time - running tests is different than reviewing a PR, so why should the context window have instructions for both on every request?

So now you split instructions into “skill” files, which are basically opinionated markdown files. And you invoke those with something like /grill-me in the prompt depending on what you’re doing.

There are some steps to have the agent automatically know what to invoke for you but in my experience this automation is hit or miss.

It is also challenging to keep track of a growing library of skills and keeping those up to date.

So YMMV regarding skills. I typically keep things in a single markdown file even if the context window gets a bit bloated.

Thousands of lines of text just to add one sentence to the prompt.

"- I'm not always going to read every word, so end each summary message with a TLDR of what you found, what you recommend, and what you need from me."

It works really well.

Sol doesn't need it at all.

However, I don't think this specific project is intending to glorify ADHD or help people claim they have it - it's just piggybacking on the idea that telling current-gen LLM models that you have ADHD (allegedly) produces better results for everyone.

So, no, not everyone can or should be diagnosed as ADHD. But the tools are (mostly) universally applicable. I don't see the downside in popularizing those. (Since you posted a top-level comment instead of a reply to someone claiming to have ADHD, I have to assume that's your complaint, at any rate.)

So when someone says they feel like they have ADHD, they are probably not inaccurate.