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Orange Line Illustration

Community
orange2ai
orange-line-illustration

Generate New Yorker-style minimalist editorial illustrations — thin black ink lines on white, vast negative space, a single orange accent (#F97316) — for articles, principles, covers, concept visuals, and illustrated HTML slide decks. Use when the user asks for 配图, 插图, 封面图, editorial illustrations, concept/metaphor illustrations, article covers, 公众号配图, principle/maxim illustrations, or mentions "橙线风格" / "orange line style" / "纽约客风格插图". Also use for "橙线 PPT" / "orange line PPT" / "线条插画PPT" / "帮我做成PPT配插图" / "做成演讲稿". Works with any image generation tool that accepts text prompts.

Overview

Publisherorange2ai
Repositoryorange-line-illustration
Skill nameorange-line-illustration
Stars
449
Forks
28
Bundled files
23
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.

  • 23 bundled files

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

  • Open source

    Published by orange2ai on GitHub. Read the source before you install it.

Installation

Install the Orange Line Illustration 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/orange2ai/orange-line-illustration/tree/main
  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/orange2ai/orange-line-illustration.git \
  .claude/skills/orange-line-illustration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Orange Line Illustration 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 Orange Line Illustration 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 Orange Line Illustration 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.

Orange Line Illustration · 橙线插画

A complete style system for generating elegant, conceptual editorial illustrations. Born from a real workflow: illustrating "六条与 AI 一起做产品 的原则" (Six Principles for Building Products with AI). The style's soul: one idea, one accent, lots of silence.

The Style (non-negotiable core)

Every image follows these rules. They are what make a set of images feel like one voice — consistency is the whole point.

  • Line: thin black ink lines, hand-drawn editorial feel (think New Yorker single-panel cartoons). No shading, no gradients, no fills except the accent.
  • Background: pure white. Generous negative space — emptiness is part of the composition, not wasted space.
  • Accent: exactly ONE color — warm orange #F97316. Use it on the single most meaningful element (the thing the viewer should understand). If everything is orange, nothing is.
  • Figures: TINY. The human is always small — dwarfed by the object, the wall, the staircase, the line. Enlarge the thing, shrink the person: this scale contrast is the signature drama of the style (a tiny figure facing an enormous wall of buttons says more than a medium figure beside a medium wall). If a figure fills more than roughly a tenth of the frame, it is too big — say "tiny" explicitly in the prompt, image models love to inflate people.
  • Mood: witty, restrained, intelligent. The image should make the viewer think for one second, then smile.

Designing the Metaphor (do this before any generation)

The metaphor is the product; the rendering is just execution. Spend the thinking here.

  1. Extract the tension. Good concept illustrations express a pair of opposites: many vs. one, hollow vs. warm, generated vs. judged. Find the tension in the content first.
  2. Prefer action scenes over abstract symbols. A person handing a cup of warm tea beats floating speech bubbles. A figure tripping on one inconsistent stair beats a row of doors with one odd handle. Field-tested: abstract juxtapositions confuse readers; small human actions land instantly. If the metaphor needs explaining, it failed.
  3. One idea per image. If the sketch needs two sentences to describe its point, cut one.
  4. Place the orange deliberately. The orange element answers: "what is this image about?" — the kept button, the warm steam, the inconsistent step, the refined line.
  5. Exaggerate the scale gap. Make the object monumental and the person tiny. The disproportion is what makes the viewer feel the idea — a small human against a vast generated world is, after all, the subject of the whole genre.

Prompt Template

Compose prompts from this skeleton (adjust the scene, keep everything else):

New Yorker magazine style editorial illustration, single-panel conceptual
metaphor. Minimalist thin black ink line drawing on pure white background,
generous negative space, no shading, no gradients, only one accent color:
warm orange #F97316. Scene: [YOUR SCENE — describe the action, the
characters, what is orange and why]. Witty, restrained, intelligent.
[CAPTION BLOCK — see below]. No other text anywhere.

Scene-writing tips learned the hard way:

  • Always write "a TINY human figure" / "a tiny figure dwarfed by ..." — and describe the object as large ("an enormous wall", "a vast circle"). State the scale contrast twice (once on the figure, once on the object) or the model will even them out.
  • Describe who does what to whom with spatial directions (left/right/ middle). Vague spatial relations produce split, disconnected scenes.
  • Add explicit negative constraints when geometry matters: "plain side view, NOT Escher-like, no impossible geometry" — image models love to get creative with stairs and architecture.
  • For continuous lines: "one single CONTINUOUS unbroken smooth line, never breaking" — otherwise lines fracture.

Caption & Title System

Interior images (3:2 landscape) carry a quiet signature in the bottom-left corner:

In the bottom-left corner: a short horizontal orange line, and below it
one small light-gray Chinese caption in a clean sans-serif font:
"[THE PRINCIPLE / KEY SENTENCE]" — small, quiet, unobtrusive, perfectly
legible.

Same corner, same style, every image — the signature position itself demonstrates consistency.

Covers (21:9 ultra-wide) make the title the anchor:

The Chinese title "[TITLE]" in clean dark-gray sans-serif typography,
placed in the [upper-left / centered upper] area, medium size, elegant,
perfectly legible.

Keep the illustration in the lower portion, breathing room around the title.

小橙 IP (default character)

The default recurring character is 小橙 — a tiny line-drawn figure with a single orange dot on its chest. Read references/xiao-orange-ip.md for the full character definition and references/xiao-orange-prompt-template.md for the generation prompt template.

Key traits (quick reference):

  • Circle head (outline only, not filled), two black dot eyes, no mouth.
  • Narrow rectangular body (outline only), thin stick arms and legs.
  • ONE small solid orange dot (#F97316) on the chest — this IS the accent.
  • Tiny relative to scene objects. Geometric, hand-drawn, not cute.
  • Must participate in the core action, not decorate.

When using 小橙, the orange accent rule changes slightly: the accent is always the chest dot (the character itself), not a separate scene element. All other objects remain black line art on white.

If the user requests a different figure style for a specific set, fall back to the generic character consistency flow below.

Character Consistency (for multi-image sets)

When producing a set of illustrations that will be viewed together (a PPT deck, an article series, a multi-panel explainer) and the user does NOT want 小橙, establish the human figure style before generating any scene illustrations.

Generate a single image with 4 candidate figures side by side — each with a distinct style (e.g. rounder head, angular torso, different arm/hand treatment, varying expressiveness). Label them A / B / C / D below each. Ask the user to pick one, or describe tweaks.

Once confirmed, write a concise character prompt block (20–40 words) and reuse it verbatim in every subsequent prompt for that set. The block should pin: head shape, eye style, mouth treatment, torso shape, arm/hand style, leg style, fill vs. outline.

For single standalone illustrations with 小橙, use the prompt template directly — no character selection step needed.

Workflow: 抽卡, not 检查 (gacha, not QA)

Generation is cheap; AI self-inspection is expensive and adds little. The human's taste is the quality gate.

  1. Design metaphor candidates — for covers, prepare ~5 distinct directions in one batch.
  2. Generate all candidates in parallel. One generation per metaphor.
  3. Present everything to the human. Let them pick. Do not pre-filter with image analysis unless the human explicitly asks.
  4. When the human says "this metaphor but not quite" — regenerate with a sharpened prompt (add the missing spatial/negative constraints), don't abandon the metaphor.
  5. Rename outputs descriptively (e.g. 01-放大器.png, 封面E-走过草稿.png) so the set stays navigable.

Reference: a field-tested set

The style was proven on six principle illustrations + one cover. The chosen cover metaphor ("walking through the draft": a figure walks along a line — ahead of them messy black scribble, behind them the same line refined into clean orange) captures the spirit of the whole style: human judgment walking through AI's rough output, leaving refinement behind.

Metaphors that worked: erasing a wall of buttons down to one; a conveyor of identical bottles with one hand-painted; completing the last arc of a nearly-finished circle; tripping on the one inconsistent stair.

Metaphors that failed (and why): ornate empty speech bubbles (too abstract), a row of doors with one odd handle (no action, no consequence), Escher staircases (model over-creativity — constrain geometry explicitly).

Renderings that failed (and why): figures drawn at half the frame height — metaphor fine, drama gone. The style lives on tiny people in big scenes; oversized figures turn a poster into a comic panel.

PPT Workflow (illustrated slide decks)

When the user wants to turn an article or talk into an HTML slide deck with orange-line illustrations, read references/ppt-workflow.md for the complete step-by-step process.

Key differences from standalone illustrations:

  • PPT illustrations depict actual story scenes, not abstract metaphors.
  • Use 小橙 as the default recurring character across all slides.
  • Confirm outline before generating; rename files before building HTML.
  • Output is a self-contained HTML file with navigation + progress bar.

References

All reference files live in references/:

  • xiao-orange-ip.md — 小橙 IP definition (default character)
  • xiao-orange-prompt-template.md — 小橙 generation prompt template
  • xianren-ip.md — 线人 (abstract human figure)
  • xianmao-ip.md — 线猫 (minimal cat character)
  • ppt-workflow.md — Full PPT slide deck workflow

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 Orange Line Illustration AI skill do?

Generate New Yorker-style minimalist editorial illustrations — thin black ink lines on white, vast negative space, a single orange accent (#F97316) — for articles, principles, covers, concept visuals, and illustrated HTML slide decks. Use when the user asks for 配图, 插图, 封面图, editorial illustrations, concept/metaphor illustrations, article covers, 公众号配图, principle/maxim illustrations, or mentions "橙线风格" / "orange line style" / "纽约客风格插图". Also use for "橙线 PPT" / "orange line PPT" / "线条插画PPT" / "帮我做成PPT配插图" / "做成演讲稿". Works with any image generation tool that accepts text prompts.

Why use Orange Line Illustration on TypingMind?

Because you install it once and use it with any model. Orange Line Illustration 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 Orange Line Illustration in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/orange2ai/orange-line-illustration/tree/main. 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 Orange Line Illustration?

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 Orange Line Illustration?

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

Is the Orange Line Illustration AI skill free?

It is published on GitHub by orange2ai. Check the repository for licensing terms. 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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