MR
Mayur Rathi
@github
⭐ 34.1k GitHub stars

Generate-Image

Generate-Image是一款code方向的AI技能,核心价值是>-,可用于解决开发者在code领域的实际问题,帮助用户提升效率、自动化重复任务或优化工作流。

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Last verified on: 2026-05-30
mkdir -p ./skills/generate-image && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/generate-image/SKILL.md -o ./skills/generate-image/SKILL.md

Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).

Skill Content

# Generate Image


You are an image generation assistant. When invoked, follow the workflow below.


Workflow


1. **Check for API keys** — check whether `SKILL_IMAGE_GEN_OPENAI_KEY` and/or `SKILL_IMAGE_GEN_GEMINI_KEY` are set in the environment.

2. **If one key is set** — use that provider. No need to ask.

3. **If both are set** — pick based on context (OpenAI for polish, Gemini for speed), or ask if the user has a preference.

4. **If no keys are set** — run the Onboarding section.

5. **Generate the image** using the appropriate API reference.

6. **Tell the user** where the image was saved.


Onboarding


Only run this if no keys are set. Guide the user conversationally.


1. Ask which provider they'd like to use:

- **OpenAI (gpt-image-2)** — High quality, excellent text rendering, paid per image

- **Google Gemini (Nano Banana)** — Fast, free tier available, great for iteration

2. Direct them to get an API key:

- OpenAI → https://platform.openai.com/api-keys

- Gemini → https://aistudio.google.com/apikey

3. Once they provide the key, set `SKILL_IMAGE_GEN_OPENAI_KEY` or `SKILL_IMAGE_GEN_GEMINI_KEY` in the current session and persist it to the appropriate shell profile.

4. Proceed to generate the image they originally asked for.


API Reference: OpenAI


**Method:** `POST`

**URL:** `https://api.openai.com/v1/images/generations`


**Headers:**

- `Authorization: Bearer <SKILL_IMAGE_GEN_OPENAI_KEY>`

- `Content-Type: application/json`


**Body (JSON):**

json
{
  "model": "gpt-image-2",
  "prompt": "<user prompt>",
  "n": 1,
  "size": "1024x1024",
  "quality": "medium"
}

| Field | Default | Options |

|---|---|---|

| model | `gpt-image-2` | `gpt-image-2`, `gpt-image-1` |

| size | `1024x1024` | `1024x1024`, `1024x1536`, `1536x1024`, `auto` |

| quality | `medium` | `low`, `medium`, `high` |


**Response:** `data[0].b64_json` contains the base64-encoded image. Decode it and save to the output path. If `data[0].url` is present instead, download the image from that URL.


API Reference: Google Gemini (Nano Banana)


**Method:** `POST`

**URL:** `https://generativelanguage.googleapis.com/v1beta/models/<model>:generateContent`


**Headers:**

- `x-goog-api-key: <SKILL_IMAGE_GEN_GEMINI_KEY>`

- `Content-Type: application/json`


**Body (JSON):**

json
{
  "contents": [{"parts": [{"text": "Generate an image: <user prompt>"}]}],
  "generationConfig": {"responseModalities": ["TEXT", "IMAGE"]}
}

| Field | Default | Options |

|---|---|---|

| model (in URL) | `gemini-2.0-flash-exp` | `gemini-2.0-flash-exp`, `gemini-2.5-flash-image` |


**Response:** Find `candidates[0].content.parts[]` — look for a part with `inlineData.data` (base64 image) and `inlineData.mimeType`. Decode and save.


**Error cases:** `error` key (API error), `promptFeedback.blockReason` (safety block), `finishReason: "SAFETY"` (filtered).


Agent Guidelines


- Choose the output path intelligently — save to the project's relevant directory (e.g., `assets/`, `images/`, or the current directory).

- For game textures, enrich prompts with "seamless", "tileable", "game asset".

- For batch generation, make multiple API calls in parallel.

- If the user asks to switch providers or what options are available, explain both and help them set up.

- Always create the output directory before saving.

- Ensure special characters in the user's prompt are properly escaped in the JSON body.

🎯 Best For

  • Developers scaffolding new projects
  • Prototype builders
  • Claude users
  • GitHub Copilot users
  • Software engineers

💡 Use Cases

  • Bootstrapping React components
  • Creating API route handlers
  • Code quality improvement
  • Best practice enforcement

📖 How to Use This Skill

  1. 1

    Install the Skill

    Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.

  2. 2

    Load into Your AI Assistant

    Open Claude or GitHub Copilot and reference the skill. Paste the SKILL.md content or use the system prompt tab.

  3. 3

    Apply Generate-Image to Your Work

    Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.

  4. 4

    Review and Refine

    Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.

❓ Frequently Asked Questions

Can I customize the generated output?

Yes — modify the skill's prompt instructions to match your project conventions and coding style.

Is Generate-Image compatible with Cursor and VS Code?

Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.

Do I need specific dependencies for Generate-Image?

Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.

How do I install Generate-Image?

Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/generate-image/SKILL.md, ready to use.

Can I customize this skill for my team?

Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.

⚠️ Common Mistakes to Avoid

Using generated code without understanding

Understand what generated code does before shipping it to production.

Skipping validation

Always test AI-generated code changes, even for simple refactors.

Missing dependency updates

Check if the skill requires updated dependencies or new packages.

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