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Image Gen

作者 Zakhar Pashkin · GitHub ↗ · v1.0.1 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install imagegen
功能描述
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparen...
使用说明 (SKILL.md)

Image Generation Skill

Use this skill when the user needs a bitmap image rather than repo-native code, SVG, HTML, CSS, canvas, or an existing vector/icon system.

This public ClawHub release is instruction-only. It does not bundle executable API helpers, dependencies, or generated assets. In Codex sessions, use the available built-in image generation or image editing tool. In other agent hosts, use the host's approved image-generation capability and keep the same prompting and verification standards.

When To Use

  • Generate a new raster image: product shot, hero image, concept art, cover, sprite, texture, UI mockup, infographic, or educational visual.
  • Edit an existing image while preserving important invariants such as identity, product shape, text, lighting direction, or composition.
  • Derive visual variants from supplied reference images.
  • Produce multiple related bitmap assets when each output has a distinct prompt or role.

When Not To Use

  • The requested asset should be an SVG, icon font, HTML/CSS composition, canvas graphic, or repo-native component.
  • The repo already has an editable vector/logo/icon system that should be extended directly.
  • The user asks for deterministic code-native output rather than generated imagery.

Workflow

  1. Decide intent: generate for a new image, edit for changing an existing image while preserving parts of it.
  2. Decide whether the image is preview-only or project-bound.
  3. Label every input image by role: edit target, reference image, style source, insert, or supporting context.
  4. Normalize the prompt into a compact production spec. Preserve user constraints and avoid adding unrelated characters, brands, slogans, or story elements.
  5. Use the host-provided image generation/editing tool. For distinct assets, make separate tool calls or jobs rather than relying on variants of one prompt.
  6. Inspect the output for subject accuracy, composition, text rendering, style fit, prohibited content, and requested invariants.
  7. Iterate with one targeted change when needed.
  8. For project-bound assets, place the selected final artifact in the workspace and update consuming references. Never leave a project-referenced final image only in a host default output directory.
  9. Report final saved path(s), whether the output is preview-only or project-bound, and the final prompt used.

Transparent Or Cutout Requests

For simple opaque subjects, request a flat removable chroma-key background and remove it locally with an approved project or host helper if available.

Prompt the source image like this:

Create the requested subject on a perfectly flat solid #00ff00 chroma-key background for background removal.
The background must be one uniform color with no shadows, gradients, texture, reflections, floor plane, or lighting variation.
Keep the subject fully separated from the background with crisp edges and generous padding.
Do not use #00ff00 anywhere in the subject.
No cast shadow, no contact shadow, no reflection, no watermark, and no text unless explicitly requested.

Use a different key color when green appears in the subject. If the subject has hair, smoke, glass, translucent material, soft shadow, reflection, or colors that conflict with practical key colors, explain that true native transparency or manual editing may be required before proceeding.

Prompt Schema

Use only the lines that help the request:

Use case: \x3Ctaxonomy slug>
Asset type: \x3Cwhere the image will be used>
Primary request: \x3Cuser's main prompt>
Input images: \x3CImage 1: role; Image 2: role>
Scene/backdrop: \x3Cenvironment>
Subject: \x3Cmain subject>
Style/medium: \x3Cphoto, illustration, 3D render, diagram, etc.>
Composition/framing: \x3Cwide, close, top-down, centered, negative space>
Lighting/mood: \x3Clighting and mood>
Color palette: \x3Cpalette notes>
Materials/textures: \x3Csurface details>
Text (verbatim): "\x3Cexact text>"
Constraints: \x3Cmust keep or must avoid>
Avoid: \x3Cnegative constraints>

Use-Case Slugs

Generation:

  • photorealistic-natural
  • product-mockup
  • ui-mockup
  • infographic-diagram
  • scientific-educational
  • ads-marketing
  • productivity-visual
  • logo-brand
  • illustration-story
  • stylized-concept
  • historical-scene

Editing:

  • text-localization
  • identity-preserve
  • precise-object-edit
  • lighting-weather
  • background-extraction
  • style-transfer
  • compositing
  • sketch-to-render

Prompting Rules

  • Start with the visual job and intended use.
  • Specify subject, scene, style, composition, lighting, and constraints.
  • Quote exact text and keep it short; generated text can still need correction.
  • For edits, repeat invariants plainly: what must change and what must stay unchanged.
  • For reference images, state exactly how each reference should influence the output.
  • Avoid fake UI evidence, fake policy proof, fake screenshots, endorsements, or brand marks unless the user has rights and asks for them.
  • Prefer real product screenshots or rendered app state where users or reviewers need factual evidence.

Reference Map

  • references/prompting.md: shared prompting principles.
  • references/sample-prompts.md: copy/paste prompt recipes by asset type.
安全使用建议
This skill is safe to install for image prompting workflows. Before using the optional local background-removal helper or CLI fallback, confirm the referenced helper exists and is trusted in your environment, and review generated images for rights-sensitive content such as trademarks, endorsements, or fake evidence.
能力评估
Purpose & Capability
The skill is coherently focused on generating, editing, prompting, and validating bitmap images, including transparent/cutout workflows.
Instruction Scope
Runtime instructions are explicit and mostly constrain behavior: use host-approved image tools, preserve user constraints, avoid fake evidence or unauthorized brand marks, and ask before CLI fallback for higher-risk transparency cases.
Install Mechanism
The artifact contains markdown, YAML metadata, and a license file only; no bundled scripts, dependencies, package install hooks, or executable files were present.
Credentials
The references mention using an existing local chroma-key helper under CODEX_HOME if available, which is purpose-aligned for background removal but should only be used when that helper is trusted in the host environment.
Persistence & Privilege
No persistence, credential access, background workers, profile/session access, or broad local indexing behavior is requested.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install imagegen
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /imagegen 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.1
Publish instruction-only ClawHub bundle for cleaner security checks.
v1.0.0
Initial public Codex image generation skill release.
元数据
Slug imagegen
版本 1.0.1
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 2
常见问题

Image Gen 是什么?

Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparen... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 28 次。

如何安装 Image Gen?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install imagegen」即可一键安装,无需额外配置。

Image Gen 是免费的吗?

是的,Image Gen 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Image Gen 支持哪些平台?

Image Gen 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Image Gen?

由 Zakhar Pashkin(@zack-dev-cm)开发并维护,当前版本 v1.0.1。

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