X Openai Imagegen 8c8b9f6

---
name: x-openai-imagegen-8c8b9f6
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 the agent 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.
---

### 导出副本边界与技能别名

迁移、安装、修复和经验回写只作用于当前加载的目标宿主技能副本。原 Codex 技能/MCP、下载的源码和 `original-skills` 均为来源材料，不因执行本副本而写入；只有用户另行明确要求配置 Codex 产品本身时，才按该产品流程处理指定目标。脚本需要安装目录或状态目录时，显式提供目标路径；不得从 `CODEX_HOME` 隐式推断写入位置。

跨技能名称可能沿用源库别名。先查 [技能别名表](../../skill-aliases.json)，优先选择同来源、同插件的条目，再使用其 `id` 调用或读取对应入口；不得假定旧 `$skill`、`superpowers:skill` 名称在新宿主直接注册。不能确定目标时先核对，不替换真实产品命令或 Shell 环境变量。

## 跨宿主使用约定

本副本供支持 Agent Skills 的客户端使用。先检查当前宿主实际提供的工具、权限和已连接服务，再执行正文工作流。原文中的宿主工具标识是能力参考；只有实际发现该工具时才可调用，其他宿主应使用经核验的等价 MCP、CLI 或 API。没有等价能力的步骤必须报告不可用，不能虚构调用或完成状态。

技能资源以本技能目录为基准；输出写入当前项目中用户指定的位置。通过技能名称或自然语言触发，具体快捷命令由宿主决定。涉及真实产品、模型 API、协议字段和官方网址的名称保持原意。授权、审批、隐私和工具范围限制在迁移后仍然有效；不得为兼容而扩大权限。具体依赖与迁移说明见同目录 `PORTABILITY.md`。

# 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 native-tool mode (preferred):** discover a host-provided image generation/editing capability and its actual schema. The source host names it `image_gen`; a verified equivalent MCP/tool may be used with the same requested image and edit semantics. Check that provider's authentication requirements; do not assume the tool exists or needs no credentials.
- **Fallback CLI mode (explicit-only):** `scripts/image_gen.py` CLI. Use only when the user explicitly asks for the CLI path. Requires `OPENAI_API_KEY`.

Within the explicit CLI fallback only, the CLI exposes three subcommands:

- `generate`
- `edit`
- `generate-batch`

Rules:
- Use the discovered native image capability by default for normal image generation/editing. All `image_gen` references below mean that verified capability and its actual schema; report a missing capability rather than fabricating a call.
- Never switch to CLI fallback automatically.
- 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 native-tool mode, take the actual output file or artifact location from the tool result; no client-specific default directory is assumed.
- Do not guess either a temporary directory or a private application directory as the output destination.
- Use destination parameters only when the discovered schema documents them. Otherwise generate first, then copy/move from the actual returned artifact location when file access allows it; report unavailable export rather than claiming a saved file.
- 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 actual returned artifact location.
- Never leave a project-referenced asset only at the actual returned artifact location.
- 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 inspect it with an available image-viewing capability and attach/reference it using the image tool's supported input mechanism, 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 explicit CLI fallback path, use the CLI `generate-batch` subcommand only when the user explicitly chose CLI mode and needs many prompts/assets.

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, fallback CLI only if explicitly requested.
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 the discovered image-viewing capability and use the image tool's verified attachment/reference mechanism.
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. If the user explicitly chooses the CLI fallback, then and only then use the fallback-only docs for quality, `input_fidelity`, masks, output format, output paths, and network setup.
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 actual returned artifact location.
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 actual returned artifact location.
16. For batches, persist only the selected finals in the workspace unless the user explicitly asked to keep discarded variants.
17. Always report the final saved path for any workspace-bound asset, plus the final prompt and whether the built-in tool or fallback CLI mode was used.

## 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

## 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.
- 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.
- 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 explicit 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 explicit CLI fallback only, see `references/cli.md` and `references/image-api.md` for `quality`, `input_fidelity`, masks, output format, and output-path guidance.

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`.

## Fallback CLI mode only

### Temp and output conventions
These conventions apply only to the explicit 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 downscaling only:
```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. Do not load or use it unless the user explicitly chooses CLI mode.