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Qiaomu Codex Imagegen

Community
joeseesun
qiaomu-codex-imagegen

Generate or edit images with Codex's built-in image generation from any agent, through an MCP tool or a plain CLI. For every image request it first proposes at least four divergent style directions (24 distilled templates, 48 presets, 20 Mondo poster artists), lets the user pick, expands the prompt, generates, and checks the result. Scenario craft for 视频封面, 视频海报, 小红书配图, 公众号封面, X 封面, 朋友圈海报, 书籍封面, 专辑封面, 产品海报, 人物写真, 节气海报, 展览海报, PPT 页面, 图生图. Use for 生图, 配图, 海报, 封面, "给我几个风格", "用 Codex 画一张". Excludes: editing photos with local tools, charts or diagrams from data, UI mockups, video generation, or anything that needs an API key-based image service.

Overview

Publisherjoeseesun
Repositoryqiaomu-codex-imagegen
Skill nameqiaomu-codex-imagegen
Stars
135
Forks
7
Bundled files
75
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.

  • 75 bundled files

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

  • Open source

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

Installation

Install the Qiaomu Codex Imagegen 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/joeseesun/qiaomu-codex-imagegen/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/joeseesun/qiaomu-codex-imagegen.git \
  .claude/skills/qiaomu-codex-imagegen
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Qiaomu Codex Imagegen 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 Qiaomu Codex Imagegen 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 Qiaomu Codex Imagegen 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.

Qiaomu Codex ImageGen

Codex ships an image generator that other agents cannot call. This package relays to it (codex app-server) and adds the craft that makes the pictures usable: a library of 24 templates (48 presets) distilled from 689 reference prompts, Mondo poster techniques, scenario presets, and a rule set for facts, text and identity.

How to call it

  1. MCP tools present (suggest_directions, compose_prompt, generate_image, search_prompts, get_prompt, build_prompt, list_catalog; in Claude Code mcp__qiaomu-codex-imagegen__*): use them.
  2. No MCP: run the CLI from this skill's folder: node scripts/cli.mjs suggest|compose|generate|search|prompt|list (see --help).
  3. Neither available: tell the user to install (README). Requires the Codex CLI, logged in. Never fake an image with code.

The workflow: suggest, choose, compose, generate, check

1. Intake (no tool call yet)

Extract: topic, deliverable (what it is for), ratio, text mode, facts that must be exact, references and the role of each (identity person, product real packaging, style, layout), must-keep, must-avoid.

  • Text mode: none (no text), exact_short (only the short text you list), typeset_later (text-free base with room for the title).
  • Posters, covers, cards and slides need a typographic layer. A finished poster has a hierarchy: headline, subtitle, one or two info lines, a small tag. With only a headline, or none, the result is concept art, not a poster. So for these deliverables use exact_short and give a complete short copy set: headline plus copy as lines separated by |. Take the lines from the user's facts. If they gave none, ask, or write clearly fictional sample lines and say so; never invent real prices, dates of real events, names, statistics.
  • none fits portraits and photos (T09–T11), product macros that carry no information, and base images the user will typeset. Keep every string short and read the result: models misspell, especially long Chinese.
  • Ask only blocking questions, at most three: a missing subject, a real person's identity reference, or facts that must appear. Fill low-risk gaps with a stated default.

2. Suggest at least four divergent directions

Call suggest_directions with the topic, deliverable and any headline/text mode. It returns four or more directions with different visual mechanisms (typography-led, graphic structure, photographic, product macro, material/space, plus a Mondo artist option for poster-like work). Never mix two mechanisms into one image.

Present them to the user briefly, one numbered item each:

  • name and template id,
  • one concrete sentence of what this topic would look like in that mechanism (write it yourself from mechanism and the topic, not a copy of the lock text),
  • what you still need from them (subject, data, reference photo),
  • look at one references[].image thumbnail per direction when available, so your sentence matches the real look.

Then ask which to make: one, several, "all", or "你定". Offer to exclude these and propose more (exclude).

Skip the question when the user already fixed the style ("用 Mondo 风格", "T03", "就这个风格") or said to go ahead ("直接出", "你定"). For "你定", pick the two best-fitting directions from different families and say which and why.

3. Compose the prompt

For each chosen template direction call compose_prompt with concrete variables:

  • Map the topic to one visible silhouette, action or relation, and say how it connects to the topic. At most one main symbol and one cross-over action.
  • Colours are roles (background, primary, accent, ink), not fixed hex values. Material belongs where the preset puts it; do not overlay noise on everything.
  • No invented facts: prices, statistics, titles, names, dates, weather, quotes, QR codes, logos.
  • missing_required non-empty → ask for exactly that. weak_variables → they still hold generic defaults, fill them first.
  • Preset with structural_override → rewrite the prompt by hand: delete the base sentences the override replaces, keep no conflicting structure, then send the result as raw_prompt with the 避免:… line.
  • Typographic templates with no text: tell the user the base image only satisfies the style after the title is typeset.
  • Give copy as |-separated lines; when strings include dates or numbers, list them all so the whitelist matches exactly (or pass a full text_rule).

Mondo directions skip this step: generate_image { prompt, style: <artist>, preset: <video-cover|poster|…> }.

Full rules and the canonical agent instruction: references/design-system/agent-guide.md.

4. Generate

Default one image per chosen direction, up to four, each its own generate_image call (they can run in parallel). Use count only for variants of the same direction. Say that it takes 30–180 s and spends Codex quota. out_dir goes inside the user's project when they name one; reference_images take absolute paths (identity photo, product shot, style reference, or the image to edit).

Series: lock template, type hierarchy, colour roles, texture placement and cross-over action; change one or two of scene, narrative focus, hue, module span. Pass the first image as reference_images for the rest.

5. Check and deliver

Read each saved image and test it against acceptance_checks (the tool prints them): first-glance focus, relations that must hold, materials only where intended, exact text and identity, nothing invented. State pass or the specific failed relation.

With the local corpus pack installed, compare against the references: put the result next to one or two references[].image of the same template (suggest_directions lists them). If yours is plainly less designed (bare subject, no typographic hierarchy, no secondary layer), that is a failure too: complete the copy set or the missing layer and regenerate once.

On failure change only the sentence that carries that relation and regenerate once; do not stack "more premium" adjectives. Transparent regions in a result are flattened onto white automatically (transparent_background: true keeps them). Deliver absolute paths, the direction used, and the sidecar .json (exact prompt) next to each image. Only say "generated" for images you actually produced and looked at.

Rules

  • Each generation spends the user's quota: no test generations, count above 3 only if asked.
  • People: prefer silhouettes, back views, hands or symbols unless a face is asked for. Identity consistency needs an identity reference photo; without one say the person is fictional. Never make deceptive material with a real person's likeness.
  • Facts and numbers come only from the user; complex charts and long copy are typeset afterwards, not drawn by the model.
  • Reference corpus: search_prompts / get_prompt read the local pack of 689 reference prompts (installed on the user's machine only). Use them to study how a mechanism is described; never send a corpus prompt unchanged as the user's prompt.
  • Simple route: prompt + preset + style (66 styles, 20 Mondo artists) when the user wants a quick image without the direction step.

Output contract

Reply with: absolute path(s), pixel size, direction (template/preset or Mondo artist) per image, assumptions made, acceptance result, and where the prompt sidecar is. On failure give the tool's error and the one fix to try (Codex login, shorten the prompt, check the reference path).

Resources

Copyright (c) 向阳乔木. X https://x.com/vista8, GitHub https://github.com/joeseesun/. Template library distilled from public-area samples of https://vip.xiaoxiaodong.ai/open-source; Mondo material from qiaomu-mondo-poster-design (MIT).

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

and 15 more files.

Frequently asked questions

What does the Qiaomu Codex Imagegen AI skill do?

Generate or edit images with Codex's built-in image generation from any agent, through an MCP tool or a plain CLI. For every image request it first proposes at least four divergent style directions (24 distilled templates, 48 presets, 20 Mondo poster artists), lets the user pick, expands the prompt, generates, and checks the result. Scenario craft for 视频封面, 视频海报, 小红书配图, 公众号封面, X 封面, 朋友圈海报, 书籍封面, 专辑封面, 产品海报, 人物写真, 节气海报, 展览海报, PPT 页面, 图生图. Use for 生图, 配图, 海报, 封面, "给我几个风格", "用 Codex 画一张". Excludes: editing photos with local tools, charts or diagrams from data, UI mockups, video generation, or...

Why use Qiaomu Codex Imagegen on TypingMind?

Because you install it once and use it with any model. Qiaomu Codex Imagegen 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 Qiaomu Codex Imagegen in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/joeseesun/qiaomu-codex-imagegen/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 Qiaomu Codex Imagegen?

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 Qiaomu Codex Imagegen?

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

Is the Qiaomu Codex Imagegen AI skill free?

Yes. It is published on GitHub by joeseesun 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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