Skills

Skill 183 of 200

Generate Image

Generate images using AI. Use when asked to generate, create, or make images, textures, icons, sprites, artwork, visual assets, or mockups.

2 minutes · 342 words · 5 sections

Install

$ npx skills add github/awesome-copilot --skill generate-image

$ npx skills add github/awesome-copilot

» /plugin marketplace add github/awesome-copilot

The first command installs just this skill, by the name in its SKILL.md; the second installs the whole repository.

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

Workflow

Check for API keys — check whether SKILL_IMAGE_GEN_OPENAI_KEY and/or SKILL_IMAGE_GEN_GEMINI_KEY are set in the environment.
If one key is set — use that provider. No need to ask.
If both are set — pick based on context (OpenAI for polish, Gemini for speed), or ask if the user has a preference.
If no keys are set — run the Onboarding section.
Generate the image using the appropriate API reference.
Tell the user where the image was saved.

Onboarding

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

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
Direct them to get an API key:
- OpenAI → https://platform.openai.com/api-keys (opens in a new tab)
- Gemini → https://aistudio.google.com/apikey (opens in a new tab)
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.
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):

{
  "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):

{
  "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.

About this skill

Trigger

Generate images using AI. Use when asked to generate, create, or make images, textures, icons, sprites, artwork, visual assets, or mockups. Supports OpenAI (gpt-image-2) and Google Gemini (Nano Banana). Requires an API key for the chosen provider.

The verbatim description from this skill’s front matter — the string an agent matches on to decide whether to load it.

Licence: MIT
Source: skills/generate-image/SKILL.md

Colophon for this issue

Colophon

How this issue was made

Source

github.com/github/awesome-copilot, branch main, last pushed 24 September 2026.

Licence

MIT — the text of every skill is reproduced unmodified, frontmatter included, under the upstream licence.

Discovery

200 skills found by walking the repository tree for SKILL.md, not by matching a directory convention. 2 distinct layouts observed: .github/skills/*/SKILL.md, skills/*/SKILL.md.

Authorship

3 of 200 skills are installed into the repository rather than published from it. They are shelved in the closing “Credited skills” part and remain the work of their own authors. A skill counts as credited when its only copy lives under one agent’s dot directory; visible directories, per-agent mirror sets, and skills installable from this repository on skills.sh all count as published.

Issue colours

Resolved from a curated brand profile — hue 295°, chroma 0.190. Two accent tones are generated per issue and each is proven against its own ground before it ships: a single accent that passes AA on both light and dark paper is arithmetically impossible.

Heading repairs

1 repair applied to this skill so the document has one h1 and no skipped levels:

Removed “Generate Image”, a leading h1 that duplicated the skill title.

Spec compliance

Every skill satisfies the Agent Skills specification’s frontmatter rules.

Images

Images inside a skill come from the upstream repository. Where the author gave no alternative text we mark the image decorative rather than inventing a description — a plausible caption we made up is worse than none for the reader who depends on it.

Marketplace

A plugin manifest is published at .github/plugin/marketplace.json by GitHub, declaring 162 plugins. It is read for editorial metadata only — never as the skill index, which is always the repository tree.

Signal

Install counts come from skills.sh. They measure downloads, not quality, and an unranked repository is not an unread one.

Agent surfaces

The whole issue is available as one markdown document at /github/awesome-copilot.md, and each skill at its own .md URL.

Publication

Set by Skills Docs from the source repository. Body text is Literata at the reader’s chosen size and measure; code is Geist Mono. Nothing on this page was written by us except this paragraph.

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

Workflow

Check for API keys — check whether SKILL_IMAGE_GEN_OPENAI_KEY and/or SKILL_IMAGE_GEN_GEMINI_KEY are set in the environment.
If one key is set — use that provider. No need to ask.
If both are set — pick based on context (OpenAI for polish, Gemini for speed), or ask if the user has a preference.
If no keys are set — run the Onboarding section.
Generate the image using the appropriate API reference.
Tell the user where the image was saved.

Onboarding

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

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
Direct them to get an API key:
- OpenAI → https://platform.openai.com/api-keys (opens in a new tab)
- Gemini → https://aistudio.google.com/apikey (opens in a new tab)
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.
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):

{
  "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):

{
  "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.