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Image Generation

作者 xrow GmbH · GitHub ↗ · v1.48.0 · MIT-0
cross-platform ✓ 安全检测通过
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当前安装
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版本数
在 OpenClaw 中安装
/install xrowgmbh-image-generation
功能描述
Create or revise document, PDF, web, or review images with the requested format, sharp raster output, and artifact validation.
使用说明 (SKILL.md)

Image Generation Skill

Use this skill when creating, regenerating, or reviewing images, diagrams, screenshots, or generated graphics for Markdown, PDF, DOCX, web pages, merge requests, or release artifacts.

What Went Wrong In claw-support !1

The image review loop failed because the work drifted between SVG and PNG, optimized technical metadata before preserving the requested format, and claimed image quality based on checks that did not match the maintainer's desired output. Treat this as the default failure mode to prevent.

Rules

  • Preserve the requested output format. If the reviewer asks for PNG, do not switch to SVG unless they explicitly accept that change.
  • Preserve the requested style or earlier format when asked. Do not redesign a diagram as a workaround for readability feedback.
  • Render raster images directly at the final source resolution. Never upscale a smaller bitmap and call it high resolution.
  • For PDF-bound PNGs, use at least 300 DPI metadata and enough pixels for the printed/displayed size.
  • Scale text, line widths, arrows, borders, spacing, and icons together. Large canvases with tiny text still fail review.
  • Prefer vector-like drawing primitives rendered into the final PNG canvas for diagrams. Avoid screenshots or nested raster snippets unless the screenshot is the actual subject.
  • Keep source-of-truth files clear: if the deliverable is PNG, make the build pipeline consume PNGs directly and avoid hidden SVG dependencies unless SVG is explicitly the editable source.
  • Use *.image.genai prompt files as the source-of-intent convention for generated repository images. For example.image.genai, generate sibling example.svg, example.png, and example.webp files unless the task explicitly narrows the output formats.
  • Do not add broad image-generation rules to repository AGENTS.md unless asked. Prefer a reusable skill or a task-local note.

Workflow

  1. Read the requested artifact path, target consumer, and exact format from the issue or MR discussion.
  2. Inspect existing artifacts before editing: dimensions, format, DPI metadata, source generator, and where each image is embedded.
  3. If a *.image.genai file exists or a new generated image is needed, treat that file as the editable prompt/brief and create the matching .svg, .png, and .webp siblings next to it.
  4. Make the smallest change that satisfies the reviewer: format, resolution, clarity, or embedding.
  5. Rebuild every downstream artifact that embeds the image, such as Markdown previews, PDFs, DOCX files, and release bundles.
  6. Validate the actual output, not only the source file.

*.image.genai Convention

  • Keep the file next to the intended generated assets, for example docs/architecture.image.genai.
  • Store the image brief, required dimensions, style constraints, text that must appear in the image, and downstream consumers in the prompt file.
  • Generate all sibling formats from the same brief: docs/architecture.svg, docs/architecture.png, and docs/architecture.webp.
  • Prefer the SVG sibling for diagrams and scalable documentation, the PNG sibling for PDFs or places that need stable raster rendering, and the WebP sibling for web delivery.
  • Regenerate all siblings together after prompt changes so the formats do not drift.
  • Before final review, run {baseDir}/scripts/check-image-genai.py --root \x3Cworkspace> to find prompt files without generated image siblings and generated images that are older than their prompt.
  • Mention the prompt file and the generated sibling dimensions in MR notes.

Validation Checklist

  • Image files have the requested extension and MIME format.
  • Every changed *.image.genai file has matching .svg, .png, and .webp siblings unless the MR explains why a format is intentionally omitted.
  • The generated image checker passes; add All Images generated to the final Definition of Done when it does.
  • PNG files are generated at source size, not post-upscaled.
  • PDF-bound PNG files have sufficient dimensions for their displayed size and about 300 DPI metadata when print quality is requested.
  • Text and fine lines are readable after the image is embedded in the final PDF or web page.
  • The PDF or DOCX contains the intended image objects and does not silently drop unsupported formats.
  • CI checks enforce the important invariants when the repository already has artifact validation.
  • MR notes state the exact format, dimensions, validation commands, and any remaining limitation.

GitLab Review Handling

For image-related MR feedback, answer the latest reviewer request first. If new feedback contradicts an earlier fix, stop extending the previous approach and explicitly revert or narrow it to the latest requested format. Resolve discussions only after the final artifact has been rebuilt and checked.

安全使用建议
Install if you want a reusable workflow for generated image assets. Be aware that running the included checker over a large repository will traverse files looking for *.image.genai prompts, and following the skill may create or update SVG, PNG, and WebP siblings next to those prompt files.
能力评估
Purpose & Capability
The stated purpose is creating, revising, and validating generated images, and the instructions and helper script directly support that purpose.
Instruction Scope
Runtime guidance is scoped to image artifacts, generated sibling files, validation, and review notes; it explicitly avoids broad repository instruction changes unless asked.
Install Mechanism
The artifact contains a SKILL.md and one executable Python validation script, with no install hooks, package installs, network setup, or automatic execution.
Credentials
The checker can recursively scan a user-supplied workspace root for *.image.genai files and sibling image freshness, but it only reads paths and mtimes and skips common dependency/control directories.
Persistence & Privilege
No background workers, credential/session access, privilege escalation, or durable configuration changes are present; image file generation is user-directed and purpose-aligned.
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 xrowgmbh-image-generation
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /xrowgmbh-image-generation 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
v1.48.0
- Updated documentation clarifies rules, workflow, and validation checklist for image generation and review. - Emphasizes strict preservation of requested output formats and styles. - Introduces and details the `*.image.genai` file convention for image briefs and sibling artifact generation. - Outlines step-by-step workflow for inspecting, generating, and validating images. - Adds a comprehensive checklist for image format, resolution, and review validation. - Guidance for handling GitLab image-related review feedback included.
元数据
Slug xrowgmbh-image-generation
版本 1.48.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Image Generation 是什么?

Create or revise document, PDF, web, or review images with the requested format, sharp raster output, and artifact validation. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 34 次。

如何安装 Image Generation?

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

Image Generation 是免费的吗?

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

Image Generation 支持哪些平台?

Image Generation 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 Generation?

由 xrow GmbH(@xrowgmbh)开发并维护,当前版本 v1.48.0。

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