{"slug": "claude-code-skills-worth-trying-from-vague-idea-to-finished-feature", "title": "Claude Code Skills Worth Trying: From Vague Idea to Finished Feature", "summary": "A developer outlined a practical workflow for combining several Claude Code skills to take a feature from a vague idea to a finished implementation, using an AI-generated bookmark collection feature as the running example. The writeup highlights skills such as grill-me and grill-with-docs, which use structured questioning and domain modeling to surface product decisions and preserve terminology before any code is written. The developer argues these skills help with the messy parts of coding, including clarifying requirements, avoiding unnecessary complexity, and maintaining design consistency.", "body_md": "I’ve tried out a lot of Claude Code skills, and a few of them are especially useful when you combine them into a workflow.\n\nNot because they magically make the model better at coding, but because they help with the annoying parts around coding: figuring out what you actually want, avoiding unnecessary complexity, keeping a design consistent, and remembering where you left off.\n\nSo instead of just listing the skills, I wanted to put together a practical example.\n\nImagine we’re adding AI-generated collections to a bookmark manager. The user pastes a link, the app figures out where it belongs, and maybe creates a collection if there isn’t a good one already.\n\nSimple idea. Plenty of decisions hiding underneath it.\n\nWe’ll use a few skills to work through those decisions, build the feature, and make it easier to come back to later.\n\nYou’ll need Claude Code and the skills you want to use installed. Each repository has its own installation instructions, so follow those rather than assuming they all use the same setup.\n\nThe skills covered here are:\n\nYou don’t need all of them. The point is to give each one a specific job.\n\n`grill-me`\nThe first thing I’d do is **not ask Claude to implement the feature**.\n\n“Add AI-generated collections” sounds specific enough until you start thinking about what should happen when the AI isn’t sure, when a collection already exists, or when the user has manually organized something.\n\nThat’s where `grill-me` comes in.\n\nIt uses the underlying `grilling` skill to turn the discussion into a decision tree. Instead of asking a giant list of unrelated questions, it asks the questions that can be answered now, then uses those answers to determine what needs to be asked next.\n\nStart with something like:\n\n```\n/grill-me\n\nI want to add AI-generated collections to my bookmark manager.\n\nThe user should be able to paste a URL and have the app suggest\nor choose an appropriate collection.\n\nHelp me work out the behavior before we implement anything.\nInspect the existing codebase for facts you can determine yourself.\nAsk me about product decisions, not things you can find in the code.\n```\n\nThe important part is that Claude should inspect the project rather than making you explain every existing function.\n\nIt might discover that collections already have a creation function, that bookmarks can belong to multiple collections, or that there’s an existing metadata-fetching pipeline.\n\nThose are **facts about the codebase**.\n\nThe questions for you are things like:\n\nThose are **product decisions**.\n\nSuppose you decide that the AI should prefer existing collections and only create a new one when there isn’t a reasonable match.\n\nNow there’s a new question: what counts as a reasonable match?\n\nMaybe you want a confidence threshold. Maybe you’d rather have the AI suggest a new collection and let you approve it. Maybe the user should be able to turn automatic creation off entirely.\n\nThe grilling workflow is useful because those questions depend on the earlier decision. There’s no reason to spend ten minutes discussing deletion behavior for automatically created collections before deciding whether automatic creation is even allowed.\n\nKeep going until the important branches are resolved.\n\nFor a tiny utility, this may be more process than you need. For a feature with several possible behaviors, it can save a lot of “well, that’s not what I meant” halfway through implementation.\n\n`grill-with-docs`\nIf the feature is going to be more than a quick experiment, I’d use `grill-with-docs` instead of a plain grilling session.\n\nThis combines the structured interview with `domain-modeling`, so the important terminology and decisions don’t just disappear into chat history.\n\nFor example:\n\n```\n/grill-with-docs\n\nHelp me define the AI collection feature for this bookmark manager.\n\nInspect the existing code and documentation first.\nClarify the important domain concepts and behavior.\nKeep the documentation focused on decisions and terminology\nthat will matter when I return to this project later.\n```\n\nOne thing I like about the domain-modeling approach is that it challenges vague terminology.\n\nFor example, what does “collection” actually mean in this app?\n\nIs it a folder for bookmarks? A tag-like grouping? Can a bookmark belong to more than one? Is an AI-generated collection different from a manually created one, or is the only difference how it was created?\n\nThose distinctions matter because they affect the data model and the behavior.\n\nThe skill can maintain a `CONTEXT.md` glossary for domain terminology. That file is meant to explain **what things mean**, not become a giant implementation spec.\n\nIt can also create ADRs, or architecture decision records, for decisions that are genuinely worth preserving.\n\nFor example, if you decide:\n\nAI suggestions never modify manually organized bookmarks without approval.\n\nThat might be worth documenting if it affects several parts of the system and would be surprising to someone reading the code later.\n\nThe distinction is useful:\n\n**Glossary:** What does “collection” mean?\n\n**ADR:** Why did we decide AI should not reorganize manually sorted bookmarks automatically?\n\nNot every little choice needs an ADR. The idea is to preserve the reasoning that would otherwise be easy to lose.\n\nNow that the behavior is clearer, it’s tempting to tell Claude to build the whole thing.\n\nBefore doing that, I’d bring in [Ponytail](https://github.com/DietrichGebert/ponytail).\n\nIts general philosophy is to think like the laziest competent senior developer.\n\nNot lazy as in “do a shitty job.” Lazy as in:\n\nWhy are we creating six abstractions and a new service when the project already has most of what we need?\n\nAsk it to review the plan first:\n\n```\nUse Ponytail to review the implementation plan for this feature.\n\nLook for existing code, platform features, or dependencies we can reuse.\nIdentify unnecessary abstractions or infrastructure.\nRecommend the smallest implementation that satisfies the requirements.\n\nDo not simplify away validation, security, error handling, or\nthe behavior we agreed on.\n```\n\nFor our bookmark manager, maybe the app already has:\n\nIf so, we probably don’t need a separate “AI Collection Orchestration Service” with its own plugin architecture and twelve interfaces.\n\nMaybe we just need to add a classification step to the existing save flow.\n\nThat’s the kind of simplification I want.\n\nPonytail also includes related skills such as `ponytail-review`, `ponytail-audit`, and `ponytail-debt`.\n\nAfter implementing the feature, I’d use the review to look for unnecessary complexity in the diff:\n\n```\nUse ponytail-review on the changes for the AI collection feature.\n\nLook for unnecessary abstractions, duplicated logic, and code\nthat could be replaced with existing project functionality.\n\nPreserve the agreed behavior and correctness requirements.\n```\n\nThe debt skill is interesting for deliberate shortcuts.\n\nMaybe we decide that a simple classification call is enough for now, but we know we might need a more sophisticated system if the library grows to tens of thousands of bookmarks.\n\nThat doesn’t mean we need to build the sophisticated system today. It means we can record why the simple approach was chosen and what would make us revisit it.\n\nOnce the behavior is settled, I’d work on the interface.\n\n[UI/UX Pro Max](https://github.com/nextlevelbuilder/ui-ux-pro-max-skill) gives Claude a searchable design knowledge base covering styles, palettes, typography, UX guidance, chart choices, and stack-specific implementation recommendations.\n\nFor our example, I’d ask it to establish the design direction before touching the UI:\n\n```\nUse ui-ux-pro-max to establish a design direction for\nthe AI collection feature in this bookmark manager.\n\nThe app uses React and Tailwind and supports dark mode.\n\nI want the feature to feel like a natural part of the existing app,\nnot a separate AI dashboard.\n\nRecommend the interaction pattern, typography, colors, spacing,\nand component guidance. Do not implement anything yet.\n```\n\nThe part I find especially useful is the ability to persist a design system.\n\nUI/UX Pro Max can generate a project-level `MASTER.md` containing the overall design direction, with page-specific overrides where needed.\n\nThat gives Claude something to refer back to instead of reinventing the visual style every time you work on another page.\n\nFor example, the bookmark manager’s main page, settings page, and collection editor should all feel like the same product.\n\nThe goal is to avoid the classic AI frontend situation where one page looks great and the next looks like it came from a completely different app.\n\nThis is where I’d bring in [Hallmark](https://github.com/Nutlope/hallmark).\n\nHallmark focuses on structural variety, not just changing colors and fonts. Its goal is to avoid the same generic AI-generated page composition being reused for everything.\n\nIt has workflows for building, auditing, redesigning, and studying designs.\n\nFor our bookmark manager, I’d give it a different job from UI/UX Pro Max:\n\n```\nUse Hallmark to design the AI collection interaction using\nthe design direction we just established.\n\nKeep the agreed palette and typography.\n\nFocus on a clear, distinctive layout and interaction flow\nthat fits the existing bookmark manager.\n\nDo not replace the design system with a different theme.\nDo not redesign unrelated parts of the app.\n```\n\n**UI/UX Pro Max** establishes the practical design foundation.\n\n**Hallmark** works on the actual composition and structure.\n\nWe don't want both skills independently deciding what the app should look like and fighting over the design system.\n\n`study` when you have a reference\nHallmark’s `study` mode is also interesting if you have a screenshot or URL of an interface you like.\n\nYou can ask it to analyze the design “DNA”: the layout structure, typography, color direction, and visual hierarchy.\n\n```\nUse Hallmark study mode on this screenshot.\n\nExplain the layout structure, spacing, typography, and visual hierarchy\nthat make it work.\n\nI want to use those principles for my bookmark manager,\nnot copy the original design.\n```\n\nThat is a useful way to learn from a reference without just asking Claude to recreate it.\n\nHallmark also has a component-specific workflow, so you can ask it to improve a modal or button without turning the request into a full-page redesign.\n\nAt this point, the feature is being implemented, and this is where a couple of smaller skills could make the day-to-day experience nicer.\n\n`i-have-adhd` to keep the next action obvious\n[i-have-adhd](https://github.com/ayghri/i-have-adhd) is an output-style skill that changes how Claude presents work.\n\nIt tries to lead with the next action, break multi-step work into smaller tasks, keep the current state visible, and suppress unrelated tangents.\n\n```\n/i-have-adhd\n\nHelp me finish this feature.\n\nShow the current task, what is already complete, and the\nsingle next action. Keep unrelated ideas in a later section.\n```\n\nInstead of getting a giant explanation about everything that could possibly happen next, you get something closer to:\n\n**Step 3 of 5 complete:** Collection classification is implemented. Next: test what happens when the AI returns an invalid collection ID. Dependency cleanup can wait.\n\nThat’s the kind of thing I find useful when I’m bouncing between projects and need to know what to do right now.\n\nIt still allows fuller explanations when you ask for them, so it isn’t supposed to make every answer tiny at the expense of being useful.\n\n[Caveman](https://github.com/JuliusBrussee/caveman) is for a slightly different problem: reducing unnecessary prose.\n\nIts basic skill compresses Claude’s explanations while preserving important technical details like code, commands, error messages, exact names, numbers, and words that change the meaning of instructions.\n\n```\nUse Caveman lite for this implementation session.\n\nKeep explanations concise, but preserve exact commands,\nerror messages, and important technical details.\n```\n\nSo instead of an essay explaining a small React issue, you might get:\n\nNew object ref each render. Prop identity changes. Memoize stable value.\n\nThe project has different modes, including lite, full, and ultra, and can switch back to clearer prose when something is ambiguous, safety-critical, or needs a proper explanation.\n\nIt also has related skills for concise commits, reviews, memory-file compression, and token statistics.\n\nOne thing worth keeping in mind: **fewer output tokens doesn’t automatically mean the same percentage reduction in total session cost**. The instructions have overhead, and input and reasoning tokens still count.\n\nThe basic concise-output skill is also separate from the repo’s larger optional proxy/engine and Cloud-related tooling. I’d distinguish those before installing the whole stack.\n\nNow imagine the feature is mostly done, but the integration tests are failing and you’re done for the day.\n\nThis is where Matt Pocock’s `handoff` skill comes in.\n\nIt creates a Markdown handoff document in the OS temporary directory so another session or agent can pick up the important context.\n\nI’d use something like:\n\n```\n/handoff\n\nThe AI collection feature is mostly implemented.\n\nPreserve:\n- The current task and completed work.\n- The decisions we made about collection behavior.\n- The data model and why we chose it.\n- The failing integration tests.\n- The single next action.\n\nReference existing plans, specs, ADRs, or commits where useful\ninstead of copying everything into the handoff.\n```\n\nThe idea is to preserve the important state without dumping the entire chat into the next session.\n\nIt’s **context portability**, not magic context-window compression.\n\nA handoff can still lose details, so if the reasoning behind a rejected approach matters, I’d explicitly ask it to preserve that. I’d also check the file before sharing it elsewhere if the conversation contained anything sensitive.\n\n`handoff` + `handoff` preserves the important context.\n\n`i-have-adhd` makes that context easier to act on when you come back.\n\nIn the next session, I’d point Claude at the generated handoff file and say:\n\n```\n/i-have-adhd\n\nRead the handoff file and help me resume.\n\nShow the current state, then give me the first concrete action.\nKeep unrelated ideas in a later section.\n```\n\nThat should make it easier to reopen a project and avoid spending the first part of the session reconstructing what happened yesterday.\n\nFor someone who has several projects going at once, that sounds pretty damn useful.\n\nHere’s the order I’d try:\n\n| Stage | Skill | Job | \n|---|---|---|\n| Clarify the idea | `grill-me` | Work out the behavior before coding. | \n| Preserve decisions | `grill-with-docs` | Document terminology and important trade-offs. | \n| Simplify the plan | Ponytail | Find the smallest implementation that satisfies the requirements. | \n| Establish design | UI/UX Pro Max | Create a consistent visual and UX foundation. | \n| Design the interface | Hallmark | Improve structure and composition without replacing the design system. | \n| Implement | `i-have-adhd` / Caveman | Keep the current task clear and the conversation manageable. | \n| Review | `ponytail-review` | Look for unnecessary complexity in the changes. | \n| End the session | `handoff` | Preserve the important context for next time. | \n| Resume | `handoff` +`i-have-adhd` | Reconstruct the state and identify the next action. | \n\nYou don’t have to use every stage every time.\n\nIf I’m making a tiny utility, I might just use Ponytail and a concise output style.\n\nIf I’m building a feature with complicated behavior, I’d spend more time grilling and documenting the decisions.\n\nIf I’m working on a frontend, I’d use the design skills, but I wouldn’t ask both of them to independently invent the entire visual direction.\n\nThe useful part is giving each skill a clear responsibility.\n\nAvoid treating the workflow as a mandatory process. The whole point is to make building things easier, not turn a small feature into a ceremony involving six agents and a stack of documents.\n\nThe combination: **grilling → documentation → simple implementation → handoff**.\n\nThe result is that you're getting from “I have an idea” to “I know what I’m building,” without losing the decisions or accidentally turning a small project into a giant architecture exercise.", "url": "https://wpnews.pro/news/claude-code-skills-worth-trying-from-vague-idea-to-finished-feature", "canonical_source": "https://dev.to/sizzlebop/claude-code-skills-worth-trying-from-vague-idea-to-finished-feature-1nhe", "published_at": "2026-09-14 22:36:57+00:00", "updated_at": "2026-09-14 23:02:41.716374+00:00", "lang": "en", "topics": ["ai-tools", "ai-agents", "developer-tools", "ai-products"], "entities": ["Claude Code", "Anthropic"], "alternates": {"html": "https://wpnews.pro/news/claude-code-skills-worth-trying-from-vague-idea-to-finished-feature", "markdown": "https://wpnews.pro/news/claude-code-skills-worth-trying-from-vague-idea-to-finished-feature.md", "text": "https://wpnews.pro/news/claude-code-skills-worth-trying-from-vague-idea-to-finished-feature.txt", "jsonld": "https://wpnews.pro/news/claude-code-skills-worth-trying-from-vague-idea-to-finished-feature.jsonld"}}