# qiaomu-mondo-poster-design: a prompt skill that picks your poster style

> A Claude skill that takes one sentence about your book, film or event and turns it into a poster prompt, choosing the art direction and colour scheme before the image model ever runs.

**joeseesun/qiaomu-mondo-poster-design** — 一句话生成大师级海报、书籍封面、专辑封面和各类设计作品。无需懂PS、配色或艺术史，AI自动选择最佳风格（基于20位传奇海报设计师）。支持电影海报、读书笔记、公众号封面、小红书配图等。默认9:16竖版，完美适配社交媒体。包含AI提示词优化、风格对比、图生图转换功能。触发词："Mondo风格"、"书籍封

- Repository: https://github.com/joeseesun/qiaomu-mondo-poster-design
- Stars: 1,247 · Forks: 126
- Language: Python
- License: MIT
- Published: 2026-10-07 · Updated: 2026-10-07 · Language: en
- Canonical page: https://hysenlabs.com/projects/joeseesun-qiaomu-mondo-poster-design

## Installing it as a skill rather than running a program

There is no pip install and no command line entry point in the README. Installation is one line that installs the skill into an assistant's skill directory:

```bash
npx skills add joeseesun/qiaomu-mondo-poster-design
```

After that, the interaction is a sentence typed to the assistant. The README's own examples are the interface: asking for a Mondo style book cover for a specific title, asking for an album cover, asking for a jazz festival poster, or asking for a 21:9 header image for a WeChat article. The trigger words listed in the repository description are the Mondo style name and a phrase for book covers, so the assistant knows when to load the instructions rather than answering as a general purpose chatbot.

This is the structural decision that shapes everything else. The repository tree is short and tells the story: `SKILL.md` at the root, a `references/` directory, a `scripts/` directory, a `requirements.txt`, and an `examples/` folder holding twenty PNG files. There is no package manifest for an application, no Dockerfile, no web server. The project is a prompt library with scripts attached.

## The table that maps a brief to a named style

The core of the project is a lookup from content type to art direction, and the README lays it out as a table. A WeChat reading note goes to a literary watercolour treatment with soft negative space. A Xiaohongshu film note goes to a Japanese film look with warm grain. A Xiaohongshu book share goes to a Korean pastel gradient. A literary book cover goes to the Penguin Clothbound pattern style, while a science fiction cover goes to a Chip Kidd conceptual treatment built on a visual metaphor. Album covers go to Peter Saville or Reid Miles. Science fiction film posters go to Kilian Eng's geometric futurism, literary film posters to Alphonse Mucha's art nouveau, mystery posters to Olly Moss's negative space, and event posters to Saul Bass's minimal geometry.

Colour is chosen the same way, one step down from the style. Science fiction gets cyber blue with neon pink, an art film gets beige with deep red, a mystery gets near black with blood orange, and something warm gets yellow with a soft blue.

The composition rules are a short list underneath: negative space, visual double meaning, minimalism, and dramatic contrast. What is notable is that all of this lives in the README rather than in Python, which means the style selection is something the assistant reads and applies when composing a prompt, not something a function computes.

## Aspect ratios are the one setting you pass yourself

The README gives a table mapping each publishing surface to a ratio and a command line argument. WeChat article headers are 21:9, Xiaohongshu images are 3:4, article illustrations are 16:9, book covers and event and film posters are 9:16, album covers are 1:1, and desktop wallpaper is 16:9. The default is 9:16.

The table is worth reading as an argument about defaults rather than a list. A poster tool that defaulted to 1:1 would be assuming an album cover, and one that defaulted to 16:9 would assume a blog header. Choosing 9:16 as the floor means the skill is built for phone screens, which fits the stated use cases of WeChat and Xiaohongshu.

One detail worth flagging: the README presents these as arguments, so somewhere below the documentation there is a script that accepts them. The repository tree confirms a `scripts/` directory and a `requirements.txt`, but the README excerpt you get does not show the invocation, so the exact flag names beyond `--aspect-ratio` are not something the project spells out on its front page.

## What the Python dependencies actually pull in

The dependency list is short enough to read in full, and it tells you how little of this project is code:

```text
requests>=2.31.0
Pillow>=10.0.0
```

`requests` is described as core, needed by both scripts. `Pillow` is marked optional, needed only by the enhanced script, and the comment next to it names the two features it serves: image to image conversion and side by side style comparison. That is the entire runtime surface. There is no client library for any image model in this list, no prompt templating engine, no vector graphics package.

The split between a basic script and an enhanced script explains the description's mention of prompt optimisation, style comparison and image to image conversion. The basic path is prompt construction, the enhanced path adds the ability to feed an existing image in and to render two styles next to each other for comparison. Image to image is the feature that matters most for anyone who already has a layout they like and wants the same composition in a Mondo treatment.

## What the example images say about the output

The `examples/` directory holds twenty PNG files, and reading the file names is more informative than the surrounding marketing copy. There is a block of ten files named for the IMDb top ten films: `imdb-01-shawshank.png` through `imdb-10-good-bad-ugly.png`. The rest are grouped by use case, with names like `usecase-wechat-sapiens.png`, `usecase-xiaohongshu-prince.png`, `usecase-article-cafe.png`, `usecase-book-v2-penguin.png`, `usecase-album-pinkfloyd.png`, `usecase-event-poster.png` and `usecase-moments-poster.png`. Several carry a `v2` marker, which suggests a second pass at book covers and album art.

The albums are a deliberate set rather than a random pick: Pink Floyd, Radiohead and Joy Division, each paired with the designer the style table names, so Peter Saville's prism for the Dark Side of the Moon, Reid Miles' high contrast for OK Computer, and David Stone Martin's pulse waveform for Unknown Pleasures. Using real records with their real designers is the sharpest evidence in the repository that the style table is not decorative, since those three covers are among the most recognisable pieces of album design in existence and reproducing them is a hard test.

It also sets the honest limit. These are demonstration images produced by whatever model the author was using at the time, not a guarantee. There is no published version matrix, no changelog and no tagged release for this project, so nothing pins the images to a model or a prompt revision.

## Where the front page stops and the skill files begin

The README is written as marketing. It opens with a table of five frustrations, moves through a three second quick start, then spends most of its length on example galleries, and it repeats the claim that you do not need to learn Photoshop, understand negative space, know who Saul Bass or Olly Moss are, or pick colours. That framing is accurate about the convenience and uninformative about the mechanism.

The substance lives in `SKILL.md` and in `references/`, neither of which is quoted on the front page. A reader who wants to know how the style library is structured, what the trigger words match on, or what happens when a brief does not fit any row of the table has to open those files. The repository carries an MIT licence, sits on Python as its primary language, and has around 1,200 stars with about 120 forks, so it has been picked up enough to be worth reading rather than skimming.

The last thing to weigh is that this project's output inherits the limits of whatever model renders it. A prompt skill can choose negative space and negative space is still a phrase in a text prompt. It removes the need to know the vocabulary, not the need for the model to understand it.

## Conclusion

What this repository actually ships is a decision layer, not an image generator. It does not contain a model, and it does not contain the diffusion weights that produce the picture. It contains a mapping from the kind of thing you are making to a named art style, a colour scheme attached to that genre, and a set of composition rules, and it packages all of that as a skill an assistant can install with npx. That framing matters when you judge it, because the quality ceiling sits in whatever image model ends up executing the prompt, while the value here is that you no longer have to spell out negative space or pick between a Penguin clothbound look and a Chip Kidd concept at three in the afternoon. Start by reading `SKILL.md` and the files under `references/`, since those hold the style library, and treat the twenty example images in `examples/` as the honest answer to what the prompts produce.

## FAQ

### What is Mondo poster?

In this repository Mondo is the name of the poster design skill and its trigger phrase. Rather than being a single fixed look, the skill maps your brief onto one of several named art styles drawn from twentieth century poster and album design, including Penguin Clothbound, Chip Kidd, Saul Bass, Kilian Eng, Alphonse Mucha, Olly Moss, Peter Saville and Reid Miles.

### How do I choose the right aspect ratio for a poster?

Match the ratio to where the image will be published. The README recommends 21:9 for WeChat article headers, 3:4 for Xiaohongshu images, 16:9 for article illustrations and desktop wallpaper, 1:1 for album covers, and 9:16 for book covers, event posters and film posters. The default is 9:16, chosen for phone screens.

### Which Python packages does the skill need?

Two. `requests` is the core dependency required by both scripts. `Pillow` is optional and only needed by the enhanced script, which is the one that handles image to image conversion and style comparison.

### Does this project generate the image itself?

No. The repository ships prompts, a style selection table and Python scripts built on `requests`, not a diffusion model or any weights. It prepares a detailed prompt describing style, colour and composition so an assistant can hand it to whatever image model you have access to.

### What art styles does it know about?

The style table covers literary watercolour, Japanese film grain, Korean pastel gradient, Penguin Clothbound, Chip Kidd conceptual design, Peter Saville and Reid Miles album aesthetics, Kilian Eng geometric futurism, Alphonse Mucha art nouveau, Olly Moss negative space and Saul Bass minimal geometry. The repository description says the set is drawn from twenty legendary poster designers.

## Sources

- [Issues](https://github.com/joeseesun/qiaomu-mondo-poster-design/issues)
- [joeseesun/qiaomu-mondo-poster-design on GitHub](https://github.com/joeseesun/qiaomu-mondo-poster-design)
- [License: MIT](https://github.com/joeseesun/qiaomu-mondo-poster-design/blob/master/LICENSE)
- [README](https://github.com/joeseesun/qiaomu-mondo-poster-design/blob/master/README.md)

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Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/joeseesun-qiaomu-mondo-poster-design