forked from farhoodlabs/skills
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e9d7232718
Inline the API call example directly in SKILL.md instead of providing a separate generate.sh script. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
63 lines
1.5 KiB
Markdown
63 lines
1.5 KiB
Markdown
# MiniMax Image Generation — Implementation Notes
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## API Reference
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- **Endpoint**: `POST /v1/image_generation`
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- **Base URL**: `https://api.minimax.io` (international) or `https://api.minimaxi.com` (China)
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- **Auth**: `Authorization: Bearer <MINIMAX_API_KEY>`
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- **Model**: `image-01`
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- **Response**: JSON with `data.image_base64[]` array
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## Example API Call
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```bash
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curl -X POST "https://api.minimax.io/v1/image_generation" \
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-H "Authorization: Bearer ${MINIMAX_API_KEY}" \
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-H "Content-Type: application/json" \
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-d '{
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"model": "image-01",
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"prompt": "men Dressing in white t shirt, full-body stand front view image :25, outdoor",
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"aspect_ratio": "16:9",
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"num_images": 1,
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"response_format": "base64"
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}'
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```
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## Response Format
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```json
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{
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"data": {
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"image_base64": ["<base64-encoded-jpeg>"]
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},
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"model": "image-01",
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"request_id": "<id>"
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}
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```
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## Aspect Ratios
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| Ratio | Dimensions | Use Case |
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|-------|-----------|----------|
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| `16:9` | 1920×1080 | Desktop wallpaper, banners |
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| `1:1` | 1024×1024 | Social media, profile images |
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| `9:16` | 1080×1920 | Mobile wallpaper, stories |
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| `4:3` | 1024×768 | Presentations |
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| `3:4` | 768×1024 | Posters, portraits |
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## Dependencies
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- `curl` — HTTP requests
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- `jq` — JSON parsing
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- `base64` — Decode image data (coreutils)
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All three are standard Unix tools. No Python or Node required.
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## File Structure
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```
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minimax-image-generation/
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├── SKILL.md # Skill definition + user-facing docs
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└── CLAUDE.md # These implementation notes
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```
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