'Great Question!' No More: The Claude Skill Cutting Through LLM Fluff

The before-and-after in the README is almost violent in its contrast. On one side, a wall of text: an LLM response that opens with enthusiasm, wanders through context about middleware and cookie handling, gestures vaguely at a file, offers a few optional thoughts, and closes with a shrug. On the other side, a single imperative sentence, three numbered steps, and a concrete next action. The repository is named i-have-adhd. It deploys as a Claude Code skill and Codex plugin. Its entire purpose is to stop the model from burying the answer under social fluff.

That fluff is not an accident. Large language models are fine-tuned on datasets that reward engagement markers, rapport-building, and hedging. The result is a default personality that performs helpfulness rather than delivering it. For users who struggle with executive function, this performance is expensive. The National Institute of Mental Health notes that adults with ADHD frequently face disorganization, procrastination, poor time management, and difficulty following instructions or completing large projects. A meandering response does not just waste time; it adds cognitive load to a brain already struggling to filter signal from noise.

A Clinical Framework, Repackaged for a Machine

The skill is built around ten rules. Lead with the next action. Number multi-step tasks. End with one concrete next step. Suppress tangents. Restate state every turn. Give specific time estimates in minutes, not vague qualifiers. Make wins visible. Deliver matter-of-fact errors. Cap lists at five items. No preamble, no recap, no closers. These rules are adapted from The Adult ADHD Tool Kit by J. Russell Ramsay and Anthony L. Rostain, though the README notes the adaptation is for how an LLM should respond, not how a human should organize their day. The result is a system prompt that treats the model less like a conversational partner and more like a task-oriented cognitive prosthetic.

Read as a protocol, the rules map neatly onto clinical ADHD challenges. Numbered steps reduce initiation paralysis by converting an amorphous blob of work into a sequence with a clear entry point. Restating state every turn functions like a conversational stack trace, compensating for working memory gaps so the user never has to ask what were we doing again. Specific time estimates address time blindness, replacing the anxiety of a vague duration with the certainty of a concrete number. Matter-of-fact errors sidestep the shame spirals that can derail focus—a theme explored in the I Have ADHD podcast, where host Kristen Carder discusses how ADHDers often pile judgment onto themselves when tasks go wrong. By stripping emotional language from failure, the skill keeps the user moving. Even the five-item list cap mirrors the working memory constraints that make long menus overwhelming.

The Claude Code Skill Economy

Technically, the repository is almost entirely a single skill manifest. It is a behavioral constraint layer, not a domain expert. This places it within the emerging Claude Code skill ecosystem—one aggregated library lists hundreds of skills covering security, DevOps, and research operations, each packaged as structured instructions and optional Python tools. Most of those skills add knowledge; this one subtracts verbosity. In a landscape where context windows are finite and attention is the scarcest resource, stripping away preamble and recap is a form of compression. The Figma resource library notes that Claude Code is designed for engineering teams working across large, complex codebases. In that environment, every token spent on performative thinking is a token not spent on the actual edit.

The skill also blurs categories in that ecosystem. The skills library distinguishes between skills, agents, and personas—defining skills as neutral instructions for how to execute a task. The i-have-adhd skill is closer to a persona or a behavioral modifier, yet it is packaged as a skill. That blurring suggests the market for agent modularity is still immature. Users are experimenting with whether to treat LLM temperament as a feature, a plugin, or a platform setting.

When the Tool Matches the Brain

The skill arrives at a cultural inflection point. The I Have ADHD podcast recently devoted an episode to ADHD, AI, Money & the Things We Avoid, explicitly discussing the use of AI as an ADHD support tool and the difference between asking for help and outsourcing your thinking. Meanwhile, the developer blog Software As Craft has framed ADHD as a different operating system rather than a flaw, advocating techniques like test-driven development and discovery trees to manage cognitive load. The i-have-adhd skill fits neatly into this narrative: it is an admission that the default LLM personality—deferential, meandering, eager to provide context—creates friction for brains that need structure.

The Software As Craft author specifically mentions discovery trees as a way to avoid being overwhelmed by massive, undefined tasks. The i-have-adhd skill’s numbered steps and concrete next actions are essentially automated discovery trees, generated by the AI rather than drawn by the developer. This inverts the typical assistive technology model: instead of the human using a tool to break down a task, the tool breaks down the task for the human. The README’s tagline, No ADHD diagnosis needed!, acknowledges that the formatting is useful for anyone tired of scrolling past pleasantries, but it also hints at the podcast’s tension around outsourcing your thinking. There is a fine line between support and dependency when the machine organizes your cognition for you.

The Bluntness Trade-Off

Yet the project has clear limits. It is, fundamentally, a well-crafted prompt file wrapped in a plugin manifest. It runs only inside Claude Code or OpenAI Codex, which means users of other AI coding tools or the standard ChatGPT web interface cannot invoke it. The ADHD-friendly label is also broad. ADHD is not a monolithic condition; the blunt, action-first tone that helps one user initiate a task might feel abrupt or anxiety-inducing to another, particularly in complex architectural discussions where suppressed tangents might strip away legitimately useful nuance. And because the skill operates at the instruction layer, it cannot fix underlying model hallucinations or errors—it simply instructs the model to state them flatly when they occur.

There is also a risk of aestheticization. The skill’s rules are derived from a clinical framework, yet its marketing wink risks reducing cognitive accessibility to a productivity life hack. In a tech culture that already conflates long hours with virtue, a focus mode can quickly become another standard to fail. The skill works best when understood as a prosthetic for a specific interaction style, not a moral upgrade over normal communication. The before-and-after example in the README still shows the model outputting a shell command to install a package and edit a file. The skill does not validate these commands; it only formats them. A user in a distracted state might act without reading, amplifying risk rather than reducing it.

Outlook

What the skill reveals is a design failure in the default LLM interface. The enthusiasm that makes chatbots feel friendly in a consumer setting becomes cognitive noise in a professional one. The success of a ten-rule prompt suggests that users are hungry for modality: the ability to flip an LLM from conversational mode into instrument mode. The open question is whether platforms will internalize these accessibility profiles—offering cognitive style settings natively—or whether users will be left curating personal libraries of personality patches, one manifest file at a time. The Instagram reel and community forum posts around Claude skills indicate demand, but the real test will be whether platform owners recognize that executive dysfunction is a user-interface problem, not just a user problem.