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Design Art Direction

Community
Sahil-SS9
design-art-direction

Form a content-specific visual thesis and maintain committed art direction from brief through implementation.

Overview

PublisherSahil-SS9
Repositoryagent-personas
Skill namedesign-art-direction
Stars
67
Forks
4
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 Sahil-SS9 on GitHub. Read the source before you install it.

Installation

Install the Design Art Direction 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/Sahil-SS9/agent-personas/tree/main/skills/design-art-direction
  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/Sahil-SS9/agent-personas.git /tmp/agent-personas
mkdir -p .claude/skills
cp -r /tmp/agent-personas/skills/design-art-direction .claude/skills/design-art-direction
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Design Art Direction 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 Design Art Direction 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 Design Art Direction 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.

Design art direction

Adapted in original words from Apache-2.0 sources, notably Impeccable (Paul Bakaus) new-work and bolder playbooks; see THIRD-PARTY-NOTICES.md.

Every ambitious surface gets a visual thesis: one sentence about what this brief looks like and why. Direction is chosen before build; build honours the thesis.

When to use

Any new surface, redesign, or work flagged as ambitious/expressive. Not required for a one-off component inside an established world.

Procedure

  1. Write the thesis before pixels: name the subject's unique mechanism in a sentence, the audience's real scene, and the cultural world the brief summons (its places, rituals, artefacts). The thesis predicts what a stranger should feel in the first five seconds.
  2. Derive composition candidates from the content, not the template shelf: at least three materially different structures, each joining a distinct composition to the same thesis. Recolouring one layout is not a candidate. Candidates are ranked by fit to the brief, and the ranking is shown, not hidden.
  3. For whole new visual worlds, go wider: list concrete visual systems the audience knows from its world (publications, notation, interfaces, physical artefacts), spanning at least three material families; convert two or three into full directions with ground colours, type voices and one signature device. Name the category's predictable default and exclude it unless the content specifically earns it.
    • Draw the directions from different families, never two from the same one. The families are a menu, not a ladder, and none is the default. Examples: editorial print (magazine grid, type-led); archival or specimen (catalogue, notation, index cards); utilitarian interface (tables, terminals, timetables); poster or Swiss typographic; brutalist or raw web; illustrated or hand-made; product-precision (Stripe- or Linear-like restraint); playful or toy-like; data-atlas; and cinematic one-world, described below. Pick by brief, audience, content and the user's media access, and record why the others lost.
    • Cinematic one-world is ONE option, for briefs about a place, an object or an experience that rewards immersion. It is wrong for dense information, tools, docs and accessibility-critical flows. Recipe: a fixed full-viewport WebGL scene behind the content that scroll moves through time, not a gallery of photos. It is built from a few gated plates derived from one master image. Relight, water, grain and a pointer-driven light are done in the shader. It uses pinned transitional acts, colour that bleeds between acts and a camera change per act. It has a still-image fallback for no WebGL and for a lost context, and a reduced-motion path. Prove it with a scroll recording, because stills cannot show it. One owner-approved build (a hotel on an islet, dusk to dawn) used it; that approval earns it a place on the menu, not priority.
  4. Write the media plan with generative-asset-production: every image, illustration, lettering piece and video the direction needs, its role, and the tool class it routes to given the user's access. A direction whose signature depends on media it cannot get is revised now, not discovered at build.
  5. Set the system before devices: a layout grid, a type scale with at most two families plus one utility face, spacing steps, and one signature interaction for the page. Further devices must reuse the signature's vocabulary; a new gimmick per section is noise, not ambition.
  6. Lock direction with a preview: a mockup or wireframe the owner approves before implementation (owner gate). Never implement first and justify later.
  7. Commit: the chosen thesis owns every surface decision until replaced. Within an established world, extension inherits; only a redesign replaces the world — a refinement never splits the difference between two looks.
  8. Implementation compares itself to the thesis at review: render-fidelity checks against the approved direction, plus the floor checks (contrast, states, browser-default theming, real content at real breakpoints).
  9. Render gate before anyone sees it. Render in a real browser at 1440, 1024 and 390 wide, after load and after scrolling each section into view, and inspect every section at full size. Fail on any of: clipped or cut-off elements, unintended overlaps, labels off their targets, broken kerning or letter-spacing in display type, awkward wraps (orphaned arrows, one-word button lines), dead empty bands, AI artefacts or fake lettering, inconsistent subjects between images, copy contradictions, and placeholders (dummy numbers, example domains, draft notes). Write the list with a pass or fix for each item. Self-ratings and model or vision-tool scores are not evidence; if no full-size inspection happened, the work is not ready to show. The builder's own pass is not the gate: a reviewer in a fresh context that did not build the page reads the same full-size renders and must return a show verdict. Builders reliably pass their own work on defects a fresh eye sees at once (letter gaps in display italics, half-applied interaction states, a second render of the same subject, layout off its grid).
  10. "Bolder" requests amplify the existing system's strongest moves in the system's own vocabulary — reuse its motif and scale at full strength, increase conviction, quiet what competes. If everything got louder, nothing did.

Decision rules

  • The brief wins over personal taste; redirecting a clear brief toward one's own preferred look is failure.
  • An ambitious directive ("really bold", "expressive") expands the candidate range, never the template adherence.
  • If the skeleton (structure minus copy) does not communicate, the boldness is decoration, not design.
  • One world per page: borrowed disciplines transfer as ambition and rigour, never as pasted costumes.

Pitfalls

  • One approved build becoming the house style: every later brief gets the same look. Past approvals widen the menu, they do not pick from it; each brief still gets directions from different families.
  • Seven variations of one grid presented as seven directions.
  • Hand-drawing in CSS what should be a photograph, painting or crafted object.
  • Treating ambition as adding effects rather than committing to a premise.
  • Mockup approved, implementation quietly drifted (re-theming, swapped fonts, dropped signature) — drift is a defect.
  • Expression that breaks core interaction paradigms without cause.
  • Judging the page from shrunken full-page screenshots or one width; defects live at full size and on mobile.
  • Shipping draft copy or placeholders marked as drafts instead of holding the page back.

Done

An approved thesis and locked direction, implemented output that passes render-fidelity comparison against it, a render-gate list with every item passed at all three widths, and the candidate list and ranking retained as part of the record.

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 Design Art Direction AI skill do?

Form a content-specific visual thesis and maintain committed art direction from brief through implementation.

Why use Design Art Direction on TypingMind?

Because you install it once and use it with any model. Design Art Direction 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 Design Art Direction in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Sahil-SS9/agent-personas/tree/main/skills/design-art-direction. 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 Design Art Direction?

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 Design Art Direction?

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

Is the Design Art Direction AI skill free?

Yes. It is published on GitHub by Sahil-SS9 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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