Blog Header

Create a professional, eye-catching blog post header image sized for web (1200×628) with optional title composition guidance.

Inputs

Steps

Generate a single, publication-quality blog header in one shot — no plan needed unless the user requests variants.

1. Derive a strong visual metaphor for {{topic}}:
   • Avoid cliché stock photo compositions (handshake, lightbulb alone).
   • Prefer: dynamic flat lay, abstract concept visualization, illustrated scene, or dramatic lifestyle moment.
2. Build the image prompt:
   • Subject derived from the metaphor above.
   • Style: {{publication_style}}, editorial photography, 16:9 wide, professional blog header, ample negative space on the left side for title text overlay, {{dominant_color}} color palette.
   • Tier: quality.
   • Aspect ratio: 1.91:1 (standard Open Graph / blog hero).
3. Call muapi image generate (model=gpt-image-2-text-to-image, aspect_ratio=21:9).

Deliverables

Return:

• The header image asset.
• Title placement guidance: where to overlay the blog title for best readability.
• Quick alt-text: SEO-friendly image alt text (1 sentence).

Notes

• Leave empty space (negative space) intentionally so titles can be overlaid without clashing.
• If the user uploads an existing brand photo, use muapi image edit to adapt it instead of generating from scratch.
• For dark-mode blogs, suggest a dark background; for light, keep backgrounds bright.

Trigger Keywords

blog header, blog image, article image, featured image, og image, open graph image, hero image

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Notes for the Executing Agent

• This recipe is LLM-orchestrated: read each phase, gather any missing inputs from the user, then call muapi CLI commands. Use muapi auth configure first if MUAPI_API_KEY is unset.
• For model IDs without a CLI alias yet, fall back to the raw endpoint via curl -X POST https://api.muapi.ai/api/v1/<endpoint> -H "x-api-key: $MUAPI_API_KEY" -H 'content-type: application/json' -d '{...}' and poll with muapi predict wait <request_id>.
• Substitute {{input_name}} placeholders with the user's actual inputs before issuing each call.