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Agent skill

Imagegen

by theowenyoung in theowenyoung/home

Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts.

Apache-2.0Auto-check passedMedia & Creative

Install Imagegen

skills CLI
$ npx skills add theowenyoung/home --skill imagegen -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install theowenyoung/home imagegen --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/theowenyoung/home.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.codex/skills/.system/imagegen .claude/skills/imagegen && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
imagegen
GitHub stars
115
Used in
4 other repos
Token cost
~4.8k tokens
SKILL.md length
2,335 words
Files
12 (incl. scripts, references, assets)
Skills in repo
7
Repo updated
First seen
Licence
Apache-2.0

At a glance

Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts.

  • Works in 3 steps: If the user names a destination, move or… → If the image is meant for the current… → If the image is only for preview or…
  • Codex should create a brand-new image
  • SKILL.md covers Top-level modes and rules, When to use, When not to use and Decision tree, plus 11 more sections
  • Runs Python scripts from its folder; calls uv; needs OPENAI_API_KEY

What it does

Imagegen is an agent skill from theowenyoung/home. Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system…

Its SKILL.md is about 4.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 15 other files, including scripts, reference files and assets (for example `agents/openai.yaml`, `references/cli.md` and `references/codex-network.md`).

It sits in Media & Creative, covering Image generation. It works with OpenAI. The licence is Apache-2.0.

When your agent uses it

  • Codex should create a brand-new image
  • Transform an existing image
  • Derive visual variants from references
  • The output should be a bitmap asset rather than repo-native code

Example prompts

  • “/imagegen”

Requirements

  • Python 3
  • A credential in OPENAI_API_KEY

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. If the user names a destination, move or copy the selected output there.
  2. If the image is meant for the current project, move or copy the final selected image into the workspace before finishing.
  3. If the image is only for preview or brainstorming, render it inline; the underlying file can remain at the default $CODEX_HOME/* path.

What it can do on your machine

Read from SKILL.md and the folder at commit 9bf3ea9. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • platform.openai.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • OPENAI_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Imagegen loads about 4.8k tokens when it runs, and up to ~16k if it reads all its reference files. Until then it costs about 145 tokens; SKILL.md has 2,335 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~145
When it runs · the whole SKILL.md, loaded when a task matches
~4.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~16k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from theowenyoung/home at commit 9bf3ea9, republished under its Apache-2.0 licence (© theowenyoung). 2,335 words, ~4,791 tokens.

Download SKILL.mdSave it as .claude/skills/imagegen/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.
name
imagegen
description
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas.

Image Generation Skill

Generates or edits images for the current project (for example website assets, game assets, UI mockups, product mockups, wireframes, logo design, photorealistic images, or infographics).

Top-level modes and rules

This skill has exactly two top-level modes:

  • Default built-in tool mode (preferred): built-in image_gen tool for image generation, editing, and transparent-image requests. Does not require OPENAI_API_KEY.
  • Fallback CLI mode: scripts/image_gen.py CLI. Use when the user explicitly asks for or confirms the CLI/API/model path. Requires OPENAI_API_KEY.

Within CLI fallback, the CLI exposes three subcommands:

  • generate
  • edit
  • generate-batch

Rules:

  • Use the built-in image_gen tool by default for normal image generation and editing requests.
  • Do not switch to CLI fallback for ordinary quality, size, or file-path control.
  • For transparent images, ask built-in image_gen for a transparent background and preserve the generated alpha.
  • Never silently switch from built-in image_gen or CLI gpt-image-2 to CLI gpt-image-1.5; ask the user first unless they explicitly requested gpt-image-1.5.
  • The word batch by itself does not mean CLI fallback. If the user asks for many assets or says to batch-generate assets without explicitly asking for CLI/API/model controls, stay on the built-in path and issue one built-in call per requested asset or variant.
  • If the built-in tool fails or is unavailable, tell the user the CLI fallback exists and that it requires OPENAI_API_KEY. Proceed only if the user explicitly asks for that fallback.
  • If the user explicitly asks for CLI mode, use the bundled scripts/image_gen.py workflow. Do not create one-off SDK runners.
  • Never modify scripts/image_gen.py. If something is missing, ask the user before doing anything else.

Built-in save-path policy:

  • In built-in tool mode, Codex saves generated images under $CODEX_HOME/* by default.
  • Do not describe or rely on OS temp as the default built-in destination.
  • Do not describe or rely on a destination-path argument (if any) on the built-in image_gen tool. If a specific location is needed, generate first and then move or copy the selected output from $CODEX_HOME/generated_images/....
  • Save-path precedence in built-in mode:
    1. If the user names a destination, move or copy the selected output there.
    2. If the image is meant for the current project, move or copy the final selected image into the workspace before finishing.
    3. If the image is only for preview or brainstorming, render it inline; the underlying file can remain at the default $CODEX_HOME/* path.
  • Never leave a project-referenced asset only at the default $CODEX_HOME/* path.
  • Do not overwrite an existing asset unless the user explicitly asked for replacement; otherwise create a sibling versioned filename such as hero-v2.png or item-icon-edited.png.

Shared prompt guidance for both modes lives in references/prompting.md and references/sample-prompts.md.

Fallback-only docs/resources for CLI mode:

  • references/cli.md
  • references/image-api.md
  • references/codex-network.md
  • scripts/image_gen.py

When to use

  • Generate a new image (concept art, product shot, cover, website hero)
  • Generate a new image using one or more reference images for style, composition, or mood
  • Edit an existing image (inpainting, lighting or weather transformations, background replacement, object removal, compositing, transparent background)
  • Produce many assets or variants for one task

When not to use

  • Extending or matching an existing SVG/vector icon set, logo system, or illustration library inside the repo
  • Creating simple shapes, diagrams, wireframes, or icons that are better produced directly in SVG, HTML/CSS, or canvas
  • Making a small project-local asset edit when the source file already exists in an editable native format
  • Any task where the user clearly wants deterministic code-native output instead of a generated bitmap

Decision tree

Think about two separate questions:

  1. Intent: is this a new image or an edit of an existing image?
  2. Execution strategy: is this one asset or many assets/variants?

Intent:

  • If the user wants to modify an existing image while preserving parts of it, treat the request as edit.
  • If the user provides images only as references for style, composition, mood, or subject guidance, treat the request as generate.
  • If the user provides no images, treat the request as generate.

Built-in edit semantics:

  • Built-in edit mode is for images already visible in the conversation context, such as attached images or images generated earlier in the thread.
  • If the user wants to edit a local image file with the built-in tool, first load it with built-in view_image tool so the image is visible in the conversation context, then proceed with the built-in edit flow.
  • Do not promise arbitrary filesystem-path editing through the built-in tool.
  • If a local file still needs direct file-path control, masks, or other explicit CLI-only parameters, use the explicit CLI fallback only when the user asks for it.
  • For edits, preserve invariants aggressively and save non-destructively by default.

Execution strategy:

  • In the built-in default path, produce many assets or variants by issuing one image_gen call per requested asset or variant.
  • In the CLI fallback path, use the CLI generate-batch subcommand only when the user explicitly chose CLI mode and needs many prompts/assets.
  • For many distinct assets, do not use n as a substitute for separate prompts. n is for variants of one prompt; distinct assets need distinct built-in calls or distinct CLI generate-batch jobs.

Assume the user wants a new image unless they clearly ask to change an existing one.

Workflow

  1. Decide the top-level mode: built-in by default, including transparent-output requests; fallback CLI only if explicitly requested or confirmed.
  2. Decide the intent: generate or edit.
  3. Decide whether the output is preview-only or meant to be consumed by the current project.
  4. Decide the execution strategy: single asset vs repeated built-in calls vs CLI generate-batch.
  5. Collect inputs up front: prompt(s), exact text (verbatim), constraints/avoid list, and any input images.
  6. For every input image, label its role explicitly:
    • reference image
    • edit target
    • supporting insert/style/compositing input
  7. If the edit target is only on the local filesystem and you are staying on the built-in path, inspect it with view_image first so the image is available in conversation context.
  8. If the user asked for a photo, illustration, sprite, product image, banner, or other explicitly raster-style asset, use image_gen rather than substituting SVG/HTML/CSS placeholders. If the request is for an icon, logo, or UI graphic that should match existing repo-native SVG/vector/code assets, prefer editing those directly instead.
  9. Augment the prompt based on specificity:
    • If the user's prompt is already specific and detailed, normalize it into a clear spec without adding creative requirements.
    • If the user's prompt is generic, add tasteful augmentation only when it materially improves output quality.
  10. Use the built-in image_gen tool by default.
  11. For transparent-output requests, ask built-in image_gen for a transparent background and preserve the generated alpha channel.
  12. Inspect outputs and validate: subject, style, composition, text accuracy, and invariants/avoid items.
  13. Iterate with a single targeted change, then re-check.
  14. For preview-only work, render the image inline; the underlying file may remain at the default $CODEX_HOME/generated_images/... path.
  15. For project-bound work, move or copy the selected artifact into the workspace and update any consuming code or references. Never leave a project-referenced asset only at the default $CODEX_HOME/generated_images/... path.
  16. For batches or multi-asset requests, persist every requested deliverable final in the workspace unless the user explicitly asked to keep outputs preview-only. Discarded variants do not need to be kept unless requested.
  17. If the user explicitly chooses or confirms the CLI fallback, then use the fallback-only docs for model, quality, size, input_fidelity, masks, output format, output paths, and network setup.
  18. Always report the final saved path(s) for any workspace-bound asset(s), plus the final prompt or prompt set and whether the built-in tool or fallback CLI mode was used.

Transparent image requests

Ask built-in image_gen for a genuinely transparent background and preserve its alpha.

Prompt augmentation

Reformat user prompts into a structured, production-oriented spec. Make the user's goal clearer and more actionable, but do not blindly add detail.

Treat this as prompt-shaping guidance, not a closed schema. Use only the lines that help, and add a short extra labeled line when it materially improves clarity.

Specificity policy

Use the user's prompt specificity to decide how much augmentation is appropriate:

  • If the prompt is already specific and detailed, preserve that specificity and only normalize/structure it.
  • If the prompt is generic, you may add tasteful augmentation when it will materially improve the result.

Allowed augmentations:

  • composition or framing hints
  • polish level or intended-use hints
  • practical layout guidance
  • reasonable scene concreteness that supports the stated request

Not allowed augmentations:

  • extra characters or objects that are not implied by the request
  • brand names, slogans, palettes, or narrative beats that are not implied
  • arbitrary side-specific placement unless the surrounding layout supports it
Show full SKILL.md (925 more words)Show less

Use-case taxonomy (exact slugs)

Classify each request into one of these buckets and keep the slug consistent across prompts and references.

Generate:

  • photorealistic-natural — candid/editorial lifestyle scenes with real texture and natural lighting.
  • product-mockup — product/packaging shots, catalog imagery, merch concepts.
  • ui-mockup — app/web interface mockups and wireframes; specify the desired fidelity.
  • infographic-diagram — diagrams/infographics with structured layout and text.
  • scientific-educational — classroom explainers, scientific diagrams, and learning visuals with required labels and accuracy constraints.
  • ads-marketing — campaign concepts and ad creatives with audience, brand position, scene, and exact tagline/copy.
  • productivity-visual — slide, chart, workflow, and data-heavy business visuals.
  • logo-brand — logo/mark exploration, vector-friendly.
  • illustration-story — comics, children’s book art, narrative scenes.
  • stylized-concept — style-driven concept art, 3D/stylized renders.
  • historical-scene — period-accurate/world-knowledge scenes.

Edit:

  • text-localization — translate/replace in-image text, preserve layout.
  • identity-preserve — try-on, person-in-scene; lock face/body/pose.
  • precise-object-edit — remove/replace a specific element (including interior swaps).
  • lighting-weather — time-of-day/season/atmosphere changes only.
  • background-extraction — transparent background / clean cutout. Ask built-in image_gen for actual transparency.
  • style-transfer — apply reference style while changing subject/scene.
  • compositing — multi-image insert/merge with matched lighting/perspective.
  • sketch-to-render — drawing/line art to photoreal render.

Shared prompt schema

Use the following labeled spec as shared prompt scaffolding for both top-level modes:

text
Use case: <taxonomy slug>
Asset type: <where the asset will be used>
Primary request: <user's main prompt>
Input images: <Image 1: role; Image 2: role> (optional)
Scene/backdrop: <environment>
Subject: <main subject>
Style/medium: <photo/illustration/3D/etc>
Composition/framing: <wide/close/top-down; placement>
Lighting/mood: <lighting + mood>
Color palette: <palette notes>
Materials/textures: <surface details>
Text (verbatim): "<exact text>"
Constraints: <must keep/must avoid>
Avoid: <negative constraints>

Notes:

  • Asset type and Input images are prompt scaffolding, not dedicated CLI flags.
  • Scene/backdrop refers to the visual setting. It is not the same as the fallback CLI background parameter, which controls output transparency behavior.
  • Fallback-only execution notes such as Quality:, Input fidelity:, masks, output format, and output paths belong in the CLI path only. Do not treat them as built-in image_gen tool arguments.

Augmentation rules:

  • Keep it short.
  • Add only the details needed to improve the prompt materially.
  • For edits, explicitly list invariants (change only X; keep Y unchanged).
  • If any critical detail is missing and blocks success, ask a question; otherwise proceed.

Examples

Generation example (hero image)
text
Use case: product-mockup
Asset type: landing page hero
Primary request: a minimal hero image of a ceramic coffee mug
Style/medium: clean product photography
Composition/framing: wide composition with usable negative space for page copy if needed
Lighting/mood: soft studio lighting
Constraints: no logos, no text, no watermark
Edit example (invariants)
text
Use case: precise-object-edit
Asset type: product photo background replacement
Primary request: replace only the background with a warm sunset gradient
Constraints: change only the background; keep the product and its edges unchanged; no text; no watermark

Prompting best practices

  • Structure prompt as scene/backdrop -> subject -> details -> constraints.
  • Include intended use (ad, UI mock, infographic) to set the mode and polish level.
  • Use camera/composition language for photorealism.
  • Only use SVG/vector stand-ins when the user explicitly asked for vector output or a non-image placeholder.
  • Quote exact text and specify typography + placement.
  • For tricky words, spell them letter-by-letter and require verbatim rendering.
  • For multi-image inputs, reference images by index and describe how they should be used.
  • For edits, repeat invariants every iteration to reduce drift.
  • Iterate with single-change follow-ups.
  • If the prompt is generic, add only the extra detail that will materially help.
  • If the prompt is already detailed, normalize it instead of expanding it.
  • For CLI fallback only, see references/cli.md and references/image-api.md for model, quality, input_fidelity, masks, output format, and output-path guidance.
  • For transparent images, ask built-in image_gen for actual transparency and preserve its alpha.

More principles shared by both modes: references/prompting.md. Copy/paste specs shared by both modes: references/sample-prompts.md.

Guidance by asset type

Asset-type templates (website assets, game assets, wireframes, logo) are consolidated in references/sample-prompts.md.

gpt-image-2 guidance for CLI fallback

The fallback CLI defaults to gpt-image-2.

  • Use gpt-image-2 for new CLI/API workflows unless the user confirms a different model.
  • CLI gpt-image-2 does not support background=transparent; ask before using gpt-image-1.5 unless the user explicitly requested that model.
  • gpt-image-2 always uses high fidelity for image inputs; do not set input_fidelity with this model.
  • gpt-image-2 supports quality values low, medium, high, and auto.
  • Use quality low for fast drafts, thumbnails, and quick iterations. Use medium, high, or auto for final assets, dense text, diagrams, identity-sensitive edits, or high-resolution outputs.
  • Square images are typically fastest to generate. Use 1024x1024 for fast square drafts.
  • If the user asks for 4K-style output, use 3840x2160 for landscape or 2160x3840 for portrait.
  • gpt-image-2 size may be auto or WIDTHxHEIGHT if all constraints hold: max edge <= 3840px, both edges multiples of 16px, long-to-short ratio <= 3:1, total pixels between 655,360 and 8,294,400.

Popular gpt-image-2 sizes:

  • 1024x1024 square
  • 1536x1024 landscape
  • 1024x1536 portrait
  • 2048x2048 2K square
  • 2048x1152 2K landscape
  • 3840x2160 4K landscape
  • 2160x3840 4K portrait
  • auto

Fallback CLI mode only

Temp and output conventions

These conventions apply only to the CLI fallback. They do not describe built-in image_gen output behavior.

  • Use tmp/imagegen/ for intermediate files (for example JSONL batches); delete them when done.
  • Write final artifacts under output/imagegen/.
  • Use --out or --out-dir to control output paths; keep filenames stable and descriptive.
Dependencies

Prefer uv for dependency management in this repo.

Required Python package:

bash
uv pip install openai

Optional for image inspection and downscaling:

bash
uv pip install pillow

Portability note:

  • If you are using the installed skill outside this repo, install dependencies into that environment with its package manager.
  • In uv-managed environments, uv pip install ... remains the preferred path.
Environment
  • OPENAI_API_KEY must be set for live API calls.
  • Do not ask the user for OPENAI_API_KEY when using the built-in image_gen tool.
  • Never ask the user to paste the full key in chat. Ask them to set it locally and confirm when ready.

If the key is missing, give the user these steps:

  1. Create an API key in the OpenAI platform UI: https://platform.openai.com/api-keys
  2. Set OPENAI_API_KEY as an environment variable in their system.
  3. Offer to guide them through setting the environment variable for their OS/shell if needed.

If installation is not possible in this environment, tell the user which dependency is missing and how to install it into their active environment.

Script-mode notes
  • CLI commands + examples: references/cli.md
  • API parameter quick reference: references/image-api.md
  • Network approvals / sandbox settings for CLI mode: references/codex-network.md

Reference map

  • references/prompting.md: shared prompting principles for both modes.
  • references/sample-prompts.md: shared copy/paste prompt recipes for both modes.
  • references/cli.md: fallback-only CLI usage via scripts/image_gen.py.
  • references/image-api.md: fallback-only API/CLI parameter reference.
  • references/codex-network.md: fallback-only network/sandbox troubleshooting for CLI mode.
  • scripts/image_gen.py: fallback-only CLI implementation. Use only when the user explicitly chooses or confirms CLI mode.

© theowenyoung, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 11 other files (scripts, references, assets) in .codex/skills/.system/imagegen of theowenyoung/home.

  • SKILL.md
  • LICENSE.txt
  • agents/openai.yaml
  • assets/imagegen-small.svg
  • assets/imagegen.png
  • references/cli.md
  • references/codex-network.md
  • references/image-api.md
  • references/prompting.md
  • references/sample-prompts.md
  • scripts/image_gen.py
  • scripts/remove_chroma_key.py

Open the folder on GitHubat commit 9bf3ea9

Used in 4 other repositories

We found 4 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 4 other GitHub owners. This page covers the copy in theowenyoung/home, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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  • Oo

    theowenyoung/home

    总结当前打开的网页、用户提供的链接，或某个产品、工具、库、文章、教程与研究页面，生成适合个人网页收藏的原始标题清理版和大纲式中文描述。用户输入 $oo、要求“收藏这个网页”“总结这个工具/链接”“生成网页收藏标题和描述”，或希望留下半年后仍能看懂的网页批注时使用。

    115 GitHub stars~491 tokensUpdated 13 days ago
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Questions about Imagegen

What does Imagegen do?

Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Imagegen is an agent skill from theowenyoung/home. Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts.

When should I use Imagegen?

Imagegen fits situations like: Codex should create a brand-new image; transform an existing image; derive visual variants from references; the output should be a bitmap asset rather than repo-native code.

How do I install Imagegen in Claude Code?

Run `npx skills add theowenyoung/home --skill imagegen -a claude-code`. Or copy the skill folder (.codex/skills/.system/imagegen in theowenyoung/home) into .claude/skills/imagegen in your project. Claude Code loads it when a task matches its description.

How do I install Imagegen in Codex?

Run `npx skills add theowenyoung/home --skill imagegen -a codex`. Or copy the skill folder (.codex/skills/.system/imagegen in theowenyoung/home) into .agents/skills/imagegen in your project. Codex loads it when a task matches its description.

Can I use Imagegen in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add theowenyoung/home --skill imagegen -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/imagegen, .gemini/skills/imagegen, .github/skills/imagegen and .opencode/skills/imagegen in your project.

What does Imagegen need to run?

Going by SKILL.md and its folder, Imagegen needs Python for the scripts in its folder, the command-line tools its instructions call (uv) and credentials named OPENAI_API_KEY. Our summary lists: Python 3; A credential in OPENAI_API_KEY.

Does Imagegen access the network?

SKILL.md names 1 domain. As links in the text: platform.openai.com. This is read from the text; nothing was executed.

Is Imagegen safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Imagegen use?

Imagegen is published under the Apache-2.0 licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Imagegen use?

About 4.8k tokens (SKILL.md is roughly 19k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 11k tokens, read only when the agent opens those files.

What are the alternatives to Imagegen?

Skills that share tags, products or a category with Imagegen: AI Image Generation and Editing (zhayujie/CowAgent, 47k stars), GPT Image Generation CLI (wuyoscar/GPT-Image2-Skill, 5.7k stars), Openai Image Gen (trpc-group/trpc-agent-go, 1.9k stars) and Image Generation (onyx-dot-app/onyx, 32k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Imagegen?

theowenyoung (a GitHub user) maintains it in theowenyoung/home, which has 115 GitHub stars. The repository holds 7 skills in this directory. The repository was last updated on September 27, 2026.

Source: theowenyoung/home on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.