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Intentional Craft

Community
kemiljk
intentional-craft

Develop or refine design and engineering ideas through Apple's Intention film: connect a human outcome to focused scope, implementation choices, and evidence of craft. Use when this lens is requested or a substantial idea needs an experience-led direction.

Overview

Publisherkemiljk
Repositoryskills
Skill nameintentional-craft
Stars
64
Forks
3
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by kemiljk on GitHub. Read the source before you install it.

Installation

Install the Intentional Craft AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/kemiljk/skills/tree/main/skills/intentional-craft
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/kemiljk/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/intentional-craft .claude/skills/intentional-craft
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Intentional Craft in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Intentional Craft on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Intentional Craft is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Intentional Craft

Make the intended human experience a decision criterion from the first idea through the working result. This skill translates Apple's 2013 Intention film into contemporary design engineering practice. It does not prescribe an Apple visual style or claim to document Apple's internal process.

Read source and translation notes when discussing the film, its wording, or the origin of a principle. The working method below is an editorial interpretation, not instructions quoted from Apple.

Establish the intention

Infer the person, task, context, and constraints from the request and available work. Identify the practical outcome and the feeling it should support. Confidence, agency, relief, curiosity, or connection may fit; surprise is not desirable in every task.

Turn the feeling into an observable design promise. For example: someone finishing an important edit should know whether it is saved and how to recover if saving fails. Emotional intent without a behaviour that supports it is too vague to guide the work. Do not claim to have measured a feeling through a screenshot or a passing test.

Use the existing brief when it supplies this direction. Ask only when an unresolved choice would materially change the result. Scale the method to the task; a precise local edit does not require a new manifesto.

Choose what deserves attention

Evaluate an idea against the intended experience, its frequency or consequence, and the cost of building and maintaining it. Prefer a coherent experience that fulfils the task over a larger set of incomplete promises.

For material choices, explain what earns inclusion and what can be deferred. A useful rejection names the competing user benefit, attention cost, or complexity it avoids. Do not manufacture a quota of rejected ideas or remove a requirement to make the result look simpler.

Compare the strongest plausible approaches when the choice is genuinely open. Keep the user's platform, brand, useful information density, and established conventions. More options can help experts; fewer options can obstruct them. Judge the quality of choice by what people can understand and accomplish.

Carry the intention into construction

Trace the human promise into the design and engineering decisions that make it credible:

Intended experienceSupporting decisionsEvidence to seek
Confidence that work is safeAccurate saving states, preserved input, and recoverable failuresObserve a successful save and an interrupted or failed save
Agency during a long operationUnderstandable progress, cancellation where feasible, and an honest account of partial effectsInterrupt the operation and inspect the resulting state
Clarity when choosingMeaningful labels, comparable information, and a useful default where appropriateWalk through a representative decision using real content
Continuity across a taskStable identity, retained context, and consistent state meaningsLeave and return, resize, or change input method as relevant

These are examples, not mandatory features. For an API, workflow, or architecture idea, make the same connection through understandable contracts, predictable failure behaviour, and the effort imposed on its consumers. Internal elegance earns its place when it supports useful behaviour or maintainable delivery.

Inspect the seams: input to feedback, local state to persisted state, one screen to another, and a component to its other uses. Typography, layout, copy, motion, semantics, and runtime behaviour should support the same promise. Spend effort where a mismatch has a meaningful effect; invisible implementation complexity is acceptable when it makes use simpler and remains maintainable.

Refine with a purpose

Prototype the uncertain experience before investing in a large implementation. Exercise the central task with representative content and the conditions most likely to challenge the promise. Change the responsible design or implementation, then recheck that task.

Simplify actual effort, ambiguity, or fragility. Retain labels, affordances, confirmation, and recovery when people need them. Restart a direction when evidence shows its premise is wrong; do not discard sound work because a new aesthetic is more exciting.

Allocate refinement by consequence and uncertainty. Stop when the agreed outcome is supported, the relevant checks pass, and further changes offer little benefit within the task's constraints. Record consequential limits rather than implying perfection.

Deliver an accountable result

Match the deliverable to the request. An ideas request should receive concrete proposals, tradeoffs, and the cheapest useful experiment. A review should receive evidence and recommendations. An implementation request should receive scoped changes and verification.

Explain the intended experience, the consequential decisions, and the evidence that supports the result. Distinguish what was observed from what remains a hypothesis. For any reported bug, give a credible consumer use case, prerequisites and reproduction steps, expected and actual behaviour, and impact; do not claim reproduction without executing it.

Readiness means meeting the task's quality bar with honest evidence. It does not authorise publication or deployment.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Intentional Craft AI skill do?

Develop or refine design and engineering ideas through Apple's Intention film: connect a human outcome to focused scope, implementation choices, and evidence of craft. Use when this lens is requested or a substantial idea needs an experience-led direction.

Why use Intentional Craft on TypingMind?

Because you install it once and use it with any model. Intentional Craft is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Intentional Craft in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kemiljk/skills/tree/main/skills/intentional-craft. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Intentional Craft?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Intentional Craft?

As many as you like. As long as a model supports skills, you can use Intentional Craft with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Intentional Craft AI skill free?

Yes. It is published on GitHub by kemiljk under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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