How to Use Claude ADHD Skill Better Than 99% of People 카테고리 없음2026. 8. 1. 13:07
How to Use Claude ADHD Skill Better Than 99% of People
A couple of days ago, I was working on a product pricing plan and asked Claude Code for some ideas.
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It gave me four directions. But after reading it, I realized that this was the same answer I could get by simply asking Google Search:
Tiered pricing based on usage (Free, Pro), pay-as-you-go billing, Token Plan, free advertising.
These answers were textbook-correct, but not the kind of insightful “You can do that? Then I suddenly understand” answer I was looking for.
Is it possible to make Claude Code’s output less boring?
I tried several things, such as modifying the Agent’s.md file, but the results were mediocre.
Until a few days ago, I suddenly found a helpful answer to this question:
I saw a post on Reddit titled “I gave Claude Code ADHD…and it thinks 2x better now”.
The author of the post mainly conducts research on medical AI safety. He used the Skill method to incorporate ADHD thinking into Claude Code and even wrote a paper on this Skill.
People with ADHD often experience a chaotic mix of information in their minds, making it difficult to decide “where to start,” and thus wasting time. This is a characteristic known as “weak executive function,” and it is completely different from a lack of effort.
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When you ask an AI tool, “I’ve made a list of things to do today. Tell me in what order I should do them,” the AI will suggest a logical order without emotion or bias. This “outsourcing of structuring work” significantly reduces the burden on execution functions.
One thing immediately came to mind: Could this skill help me turn Claude Code into ADHD? That would be awesome.
So I spent two hours reading through the paper and the code.
After watching it, I felt that this skill is really valuable in certain specific scenarios.
Truth often lies in the hands of a minority.
The biggest problem with large models right now is not illusions or incorrect answers, but rather that the answers are too “correct”.
The reason for this overly accurate prediction is the autoregressive generation mechanism of LLMs:
It ensures that each token is sampled based on the conditional probability distribution mentioned earlier. Although there are random adjustments such as temperature, the overall distribution is still strongly constrained by the training distribution.
This means that when the large model is written up to the third sentence, the first two sentences have already anchored the direction for the rest of the model.
This is ChatGPT
Therefore, the final output of a large model is always the conventional “thinking” that conforms to the training distribution, rather than truly valuable “unique” ideas (unless your prompts are very unique, but this does not align with most conversational styles).
This works perfectly for questions with standard answers: if you ask it how to write quicksort, it will write it for you quickly and correctly.
However, most questions are open-ended, and large models can easily become “mediocre” in this context.
For example, when you’re making architectural decisions or thinking about solutions, the standard answer is often the “mediocre” one.
But truly valuable solutions are often “unconventional thinking” that is “in the hands of a few”.
And this is precisely the comfort zone of ADHD thinking.
Skipping the first three obvious answers and continuing to detour where others have stopped is what ADHD thinking excels at.
Three Designs of ADHD Skill
I summarize the core design of the ADHD skill into three points.
First, five independent agents.
Instead of having an AI think five times, it makes five completely independent LLM calls, with no shared context between each call.
The ideas in Agent A are completely invisible in Agent B. The context is physically isolated, so it’s not about using a prompt to “ignore previous ideas” or compressing the context to “reduce context”; rather, there is no such thing as “previous ideas” at all.
This is similar to the approach recommended by Anthropic in the Harness project: they believe that long tasks should have their context reset directly to avoid context compression affecting task performance.
《harness-design-long-running-apps》
Second, cognitive framework-driven.
Each agent is assigned a completely different “thinking role”.
For example, “You are a regulator, auditing the system’s compliance and failure modes,” or “You are a ten-year-old child who has never seen software before, rethinking this problem in the most naive way.”
The paper contains 15 such frameworks, and ADHD will select five each time it runs: the goal is not to make them each “correct”, but to make them each “different”.
This forces each agent to think from only one specific perspective, simulating ADHD-like multi-threaded cognition.
Third, the processes of generation and critique are separated.
This is the most ingenious design in my opinion:
During the divergence phase, the system clearly states, “You are a generator, not a critic. Evaluation, sorting, and hedging are prohibited.”
During the convergence phase, switching to a completely different LLM call results in the system prompting “You are now a critic, engaging in adversarial reading, scoring, and finding traps.”
These two roles use different calls, different system prompts, and different output formats.
Why is this important? Because when creativity and judgment are working together, creativity cannot be fully utilised.
Just like someone who is writing the first draft of an article while simultaneously revising it, the result is that they can’t write anything at all; the same applies to an LLM.
Therefore, through this phased design, ADHD Skills can be achieved:
During the divergent thinking phase, consider the problem from five independent perspectives using “multi-threaded thinking”.
During the convergence phase, a thread is used to “integrate thinking”, and finally, an “unconventional” answer is given.
What did the ADHD paper discuss?
Of course, these two cases are not rigorous enough. Is ADHD really effective enough?
The authors themselves conducted more systematic verification and even wrote a paper titled “ADHD: Parallel Divergent Ideation for Coding Agents”.
The paper used six open-ended engineering questions for evaluation: ADHD was compared with a single response of the same model, with 5 wins and 1 loss.
His evaluation results showed that ADHD improved the novelty of the entire solution by 5.17 and the breadth by 4.17.
From the perspective of traditional prompt word engineering, this idea can be understood as follows:
ADHD alters the thinking logic of AI, allowing us to directly change the structure of the model’s output when calling the model.
You might say, doesn’t CoT (Mind Chain) allow AI to think more deeply? Doesn’t ToT (Mind Tree) allow it to search more broadly?
That’s right.
CoT allows a brain to think more slowly and carefully along a single path; it remains a linear path. ToT allows the same brain to try different next steps at forks, but the branches share context, so when writing the fourth step, the context of the first three steps is still there.
Therefore, no matter how deep the big model is thought out or how wide the search is, it cannot solve the problem that “the angle itself has not changed”.
Having understood this difference, we can now consider a larger question:
Once model capabilities and frameworks become similar, what will be the next dimension of differentiation?
I believe it’s about inference-time engineering: how you organize the call, isolate the context, assign roles, and schedule the generation and evaluation at the moment the model is invoked.
One example is Claude Code’s recently launched Agent Teams mode, which enables multiple AIs to collaborate in parallel, supporting up to 20 sub-agents running simultaneously.
ADHD Skill is also a concrete implementation of this direction:
It does not change the model weights or perform fine-tuning; it simply changes the diversity and quality of the agent’s output by calling orchestration during inference.
Installation and management with Cloud Code
First, add the plugin marketplace provided by the official warehouse, and then install the plugins.
claude plugin marketplace add ayghri/i-have-adhd
claude plugin install i-have-adhd@i-have-adhd
Check the installation results.
claude plugin list
This will run if an update is needed.
claude plugin marketplace update i-have-adhd
If you just want to turn off the plugin temporarily instead of removing it, you can:
claude plugin disable i-have-adhd
To completely uninstall it, do the following:
claude plugin uninstall i-have-adhd
claude plugin marketplace remove i-have-adhd
Once the installation is complete, type the following into the session where you need this output method /i-have-adhd: If the command does not appear in the autocomplete list, restart Claude Code first, as the plugin index is usually read at startup.
Installation and management via Codex
The installation command for Codex is slightly different from that for Claude Code.
codex plugin marketplace add ayghri/i-have-adhd --ref main
codex plugin add i-have-adhd@i-have-adhd
Check if it is installed.
codex plugin list
If you need to get a new version, you can update the market index and reinstall the plugin.
codex plugin marketplace upgrade i-have-adhd
codex plugin remove i-have-adhd
codex plugin add i-have-adhd@i-have-adhd
The uninstall command is as follows:
codex plugin remove i-have-adhd
codex plugin marketplace remove i-have-adhd
Typing “ “ into the conversation $i-have-adhdwill activate this skill. If you want to restore the normal output, you can tell the agent stop adhd modeor normal modedirectly. If you are still using the old style after switching, it is usually safer to create a new session rather than repeatedly modifying the current context.
How to set the default output style
If you need to manually enable it for each session, you can write the persistence rules to a global directive file.
Use Claude Codex ~/.claude/CLAUDE.mdand Codex ~/.codex/AGENTS.md. Add the following:
## Output style
Always follow the rules in the `i-have-adhd` skill: action-first, numbered steps, no preamble, no closers, state restated each turn.
Global activation affects all projects. A safer approach is to manually invoke it in multiple sessions to see if this rhythm fits your work style before deciding whether to write a global configuration.
For tasks requiring didactic explanations, design discussions, or lengthy reasoning, you can also temporarily ask an assistant to provide a full explanation.
The recommended starting point is “One move on the first day → After one week → Once you’re comfortable.”
It’s definitely worth trying, provided you understand the side effects. However, for people with ADHD traits, the “5 steps all at once” approach is too much. I certainly felt that way myself.
We will divide it into stages.
Day 1: Ask Claude about “what you’re currently struggling with.”
That’s all you need.
Either the browser version of Claude.ai or Claude Code is fine. The free version is sufficient.
“I can’t start work in the morning,” “I can’t review the tasks I wrote in Notion,” “Writing meeting minutes is a pain.” Anything is fine.
What’s important isn’t whether Claude’s answers are useful, but the experience of “putting your problem into words and receiving some kind of response.”
One week later: Create one file called MEMORY.md.
Here are some things Claude thought were good to remember, written one line at a time.
These are the kinds of things that are worth saving:
My own decision-making criteria and work rules
Past failures (things I don’t want to repeat)
Recurring phrases and instructions
Project-specific prerequisites
Items not to include: sensitive information such as account numbers, credit card information, addresses, authentication information, family personal information, and health information. Do not store this information in persistent memory that is readable by AI. You need to decide to manage it in a separate location.
Once you’re comfortable: Turn repetitive tasks into a single skill.
Choose one thing you do every week. It could be anything: formatting meeting minutes, organizing tasks, writing weekly reports, etc.
Create a skill that will act when you say “Do this.” Once you’ve created one, you’ll understand how to apply it to others.
Once you’re more comfortable: Add adversarial reviews.
I will have my output reviewed by AI other than Claude, such as Codex, ChatGPT, and Gemini.
Claude has a strong tendency towards conformity bias. He tends to say things like, “That’s great.” To stop the cognitive tunnelling that occurs during hyperfocus from the outside, a different part of the brain is needed.
Before making it a habit: Set aside half a day each week for Claude bans.
To measure the degree of dependence. For example, only handwritten notes on Sunday mornings, and only a paper notebook on Wednesday evenings.
If you find yourself unable to do anything at this point, you’ll realize you were “dependent.” It’s best to realize this sooner rather than later.
Addiction itself isn’t bad. It’s only unmeasured addiction that’s dangerous.
🧙♂️ I am an AI Generative expert! If you want to collaborate on a project, drop an inquiry here or book a 1-on-1 Consulting Call With Me.
GitHub: https://github.com/uditakhourii/adhd
Paper: https://divergent.sh/
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