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Real Content Design

Community
kemiljk
real-content-design

Shape interface structure using representative content before layout decisions settle. Use when placeholders, uniform examples, incomplete records, or mixed content types may hide problems in hierarchy, comparison, navigation, or component design.

Overview

Publisherkemiljk
Repositoryskills
Skill namereal-content-design
Stars
64
Forks
3
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Real Content Design 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/real-content-design
  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/real-content-design .claude/skills/real-content-design
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Real Content Design 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 Real Content Design 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 Real Content Design 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.

Real Content Design

Let the material people actually need to read, compare, or act on shape the interface. This is a design method, not just a long-string stress test.

Establish the content and task

Inspect the named surface, its content model, and available examples. Identify the audience's task: scan, compare, choose, understand, edit, or find something. Record which fields are essential to that task and which are optional, derived, unavailable, or conditional.

Use supplied or authorised project material first. When examples are insufficient, create clearly labelled synthetic fixtures that reproduce useful structural variation. Do not invent ownership, testimonials, results, or other factual claims for production copy. Keep fixtures separate from publishable content and avoid writing them into live records.

Ask for missing facts only when they prevent a meaningful decision. Otherwise work with explicit unknowns and propose the content requirement. A request to explore layouts authorises exploration; production content or code changes require the corresponding scope.

Choose representative variation

Select examples that challenge a real decision rather than exhaustively combining every field:

VariationDesign question
Uneven titles and summariesDoes hierarchy survive different amounts of explanation?
Missing images or optional fieldsIs the remaining record understandable without fake filler?
Mixed status, type, or ownershipCan people distinguish what they are comparing?
Similar names or overlapping descriptionsIs enough distinguishing context visible?
Complex or consequential informationDoes simplification remove something needed to decide?
Few, many, or no recordsDoes organisation still fit the task, and is the next action clear?

Include long text, locale, units, or writing-direction variation when the intended audience and product support them. Avoid using arbitrary extremes as a reason to redesign an otherwise sound pattern. State which examples are real, synthetic, or still missing.

Design from the material

Arrange the representative content in the existing system before inventing a new structure. Inspect what readers need together, which distinctions deserve emphasis, and what information is required at each decision.

Do not force equal copy lengths to rescue a rigid card grid. Try content-driven height, grouping, a list, or a different summary structure when the task supports it. Conversely, consistent comparison may need stable columns and explicit missing values. Choose based on use, not an automatic preference for fluid layouts.

Treat absence deliberately. Omit optional decoration, distinguish unavailable from not applicable where meaningful, and explain required next steps. Missing content, loading, and failure are different states; do not present all three as an empty placeholder.

Check truncation against the task. A detail view can justify a concise preview if the identifying information remains visible and access is clear. A tooltip does not adequately restore essential content for every input method. Keep headings, actions, and reading order coherent as the content changes.

Turn discoveries into bounded changes

Distinguish three remedies: improve the content itself, change the content model, or change the presentation. A new layout cannot supply missing project ownership; a better summary cannot fix a component that clips supported text.

For example, a portfolio containing a shipped product, a team contribution, and an unfinished experiment needs honest status and contribution context. Unequal screenshots and summary lengths should inform the layout. Do not manufacture identical case studies or claim the experiment shipped. This is a hypothetical application.

For implementation work, place representative fixtures in the project's existing preview or development mechanism when useful. Avoid adding a new fixture framework for a small design decision. Check changed shared components in their relevant other uses.

Verify and deliver

Revisit the task with the chosen content at relevant layout sizes and inputs. Inspect actual rendering for wrapping, reading order, image treatment, and discoverability. Exercise behaviour when the change affects it. A fixture demonstrates a scenario, not how common it is; a screenshot does not prove accessibility or task comprehension.

Report the sample, design decisions it changed, any factual content dependencies, and checks performed. Keep hypotheses separate from findings. For a bug, state the concrete consumer use case, prerequisites and reproduction steps, expected and actual behaviour, and impact; do not claim reproduction from imagined content alone.

Deliver the requested exploration, review, or scoped implementation. Preserve representative examples when they materially help future work, but do not replace real product content with demonstration data.

Frequently asked questions

What does the Real Content Design AI skill do?

Shape interface structure using representative content before layout decisions settle. Use when placeholders, uniform examples, incomplete records, or mixed content types may hide problems in hierarchy, comparison, navigation, or component design.

Why use Real Content Design on TypingMind?

Because you install it once and use it with any model. Real Content Design 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 Real Content Design in TypingMind?

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

Which AI models can use Real Content Design?

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 Real Content Design?

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

Is the Real Content Design 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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