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Guizang Material Illustration

CommunityPopular
op7418
guizang-material-illustration

Generate Guizang-style material illustrations, labeled explanatory visuals, material-styled chart illustrations, and data-first editorial images from articles, notes, product concepts, workplace reports, creator posts, tutorials, school materials, humanities topics, science explanations, screenshots, or chart data. Use when the user asks for 配图, 带字插图, 解释图, 图解插画, 概念拆解图, 图表美化, 数据图美化, 3D 图表, 汇报配图, 内容配图, 小学课文配图, 生物/化学/物理解释图, 人文类配图, process/loop/system diagrams, or wants GPT-Image / image generation to create supporting images that can sit inside social cards, docs, slides, PPTs, or posts.

Overview

Publisherop7418
Repositoryguizang-material-illustration
Skill nameguizang-material-illustration
Stars
1.2K
Forks
118
Bundled files
10
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.

  • 10 bundled files

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

  • Open source

    Published by op7418 on GitHub. Read the source before you install it.

Installation

Install the Guizang Material 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/op7418/guizang-material-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/op7418/guizang-material-illustration.git \
  .claude/skills/guizang-material-illustration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Guizang Material 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 Guizang Material 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 Guizang Material 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.

Guizang Material Illustration

Create supporting illustrations from source text, screenshots, or chart data. The output is an image-generation prompt plan plus generated raster images when image generation is available.

This skill focuses on the illustration layer, not the full social-card layout. If the user also needs Xiaohongshu/WeChat card composition, pair this skill with the social-card skill: use this skill for the central illustrations, then place the generated images in the card template.

This skill does not replace the user's PPT skill or the Guizang social-card text-layout skill. Use those skills for slide structure and 3:4 text/card layout. Use this skill for the visual asset that goes into those layouts.

Workflow

  1. Read the user's source text, screenshot, or data and identify the concepts or charts that deserve supporting images.
  2. Decide the working mode yourself from context. Do not ask the user to choose a mode unless the missing choice would materially change the result. If clarification is needed, ask naturally in one short sentence and offer a recommended default.
  3. If the source is a chart screenshot, extract only chart type, title, data, axis labels, axis range, tick labels, units, category order, and error bars. Do not carry over screenshot colors, typography, spacing, shadows, or background.
  4. If the concept, entity, object, historical/cultural context, scientific mechanism, species, material, brand, model, or place is likely unfamiliar or visually specific, look up reference information and/or reference images before prompting. Use references to understand and extract stable visual cues, then translate those cues into the Guizang visual system.
  5. Compress each non-chart concept into one plain-language explanation and 3-5 visible diagram labels when labels help. Some editorial or humanities illustrations may need fewer labels.
  6. Choose a visual structure for each concept:
    • Cycle: repeated work, feedback, loops, iteration.
    • Pipeline: ordered steps, routing, transformation, workflow.
    • Hub-and-spoke: one center coordinating several branches.
    • Before/after: state change, upgrade, migration, comparison.
    • Layer stack: architecture, hierarchy, dependencies.
    • Data-first scene: chart or metric panel embedded in a topical scene.
    • Scientific mechanism: object, parts, forces, reactions, or biological process.
    • Text scene: a literary, historical, or everyday scene that anchors an abstract idea.
  7. Write one image prompt per illustration. Make the prompt describe exact label text or chart data, aspect ratio, safe margins, references used, and shared Guizang visual style.
  8. Generate the images with the imagegen skill or built-in image generation tool when available.
  9. Inspect each image. If labels are wrong, chart data is wrong, reference cues are misleading, unreadable, or clipped, regenerate with stricter constraints.
  10. Save prompts and final image paths in the task folder so the image set can be reproduced.

Text Rules

  • Use everyday Chinese. Avoid literal field labels like Trigger, Stop, Use unless the user explicitly wants bilingual UI.
  • Keep labels short: 2-5 Chinese characters is ideal; 6 characters is usually the upper limit.
  • Put explanatory sentences outside the illustration when the final artifact has a surrounding layout. Inside the illustration, only label objects and flows.
  • Prefer concrete labels over abstract nouns. Use 用户提示, AI 执行, 结果检查, 下一轮; not 输入阶段, 执行阶段, 验证阶段.
  • If the model struggles with Chinese text, shorten labels further and use distinct positions: top-left, top-center, top-right, bottom-center.
  • Do not add a dense legend inside the image. If a viewer needs a paragraph, it belongs in HTML/CSS, Markdown, or slide text.

Visual System

Read references/visual-style.md before generating a new set. It defines the default 3D Swiss editorial style, aspect ratios, safe-area rules, and supported accent colors.

Read references/prompt-patterns.md when drafting prompts. It contains reusable prompt shells for cycle, pipeline, hub, before/after, and layer-stack diagrams.

Read references/chart-beautify.md when the input is a chart screenshot, table, metric list, benchmark result, or the user asks to beautify a chart. It covers how to preserve exact values while giving the chart the same material illustration style.

Read references/use-cases-and-routing.md when deciding what kind of image to create from vague user input, education materials, humanities topics, or mixed article/data sources.

Read references/reference-gathering.md when the topic contains unfamiliar concepts, specific entities, scientific objects, cultural artifacts, historical scenes, brand/model names, or anything where visual accuracy matters.

Read references/qa-checklist.md before delivering final images.

File Handling

Create a task folder instead of writing loose assets next to this skill. Default to:

text
local-tests/<slug>/
├── assets/
│   └── generated illustration images
└── PROMPTS.md

When pairing with another project, use that project's task folder if the user specifies one.

For each final image, record:

  • Concept name.
  • Final prompt.
  • Output image path.
  • Any rejected attempt and the reason if it matters for reuse.

Quality Check

Before delivery, visually inspect each image and confirm:

  • The whole diagram fits the requested image well; no important object or label is cropped.
  • Chinese labels are legible and match the requested text.
  • Label positions point to the right objects.
  • Accent color is consistent across the set.
  • The image contains no accidental logos, watermarks, UI chrome, or unrelated English text.
  • The illustration can still be understood at social-card size.

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 Guizang Material Illustration AI skill do?

Generate Guizang-style material illustrations, labeled explanatory visuals, material-styled chart illustrations, and data-first editorial images from articles, notes, product concepts, workplace reports, creator posts, tutorials, school materials, humanities topics, science explanations, screenshots, or chart data. Use when the user asks for 配图, 带字插图, 解释图, 图解插画, 概念拆解图, 图表美化, 数据图美化, 3D 图表, 汇报配图, 内容配图, 小学课文配图, 生物/化学/物理解释图, 人文类配图, process/loop/system diagrams, or wants GPT-Image / image generation to create supporting images that can sit inside social cards, docs, slides, PPTs, or posts.

Why use Guizang Material Illustration on TypingMind?

Because you install it once and use it with any model. Guizang Material 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 Guizang Material Illustration in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/op7418/guizang-material-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 Guizang Material 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 Guizang Material Illustration?

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

Is the Guizang Material Illustration AI skill free?

It is published on GitHub by op7418. 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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