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higgsfield-gpt-image-2

Generates production-ready GPT Image 2.0 prompts using a three-format routing taxonomy.

Updated Jul 15, 2026
Search Tags:#prompt engineering#image generation#higgsfield#ui mockup#gpt-image-2#static ads#json prompts
One-click install
npx skills add https://github.com/executiveusa/buffer-blaster- --skill higgsfield-gpt-image-2-executiveusa
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: higgsfield-gpt-image-2
Source: https://github.com/executiveusa/buffer-blaster-/tree/main/skills/higgsfield/skills/higgsfield-gpt-image-2
Command: npx skills add https://github.com/executiveusa/buffer-blaster- --skill higgsfield-gpt-image-2-executiveusa

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Writing effective prompts for GPT Image 2.0 requires knowing which prompt structure fits the concept — structured JSON for layout-dense images, dense cinematic prose for single-subject scenes, or an auto-derive meta-prompt for theme-only ideas. This Skill removes that guesswork by routing any plain-text concept to the correct format and applying per-format craft patterns. ## Core Features & Use Cases - Three-format prompt taxonomy: Format A (structured JSON for UI mockups, infographics, character sheets, multi-panel posters), Format B (dense cinematic prose for portraits, scenes, landscapes), and Format C (auto-derive meta-prompts for theme-only concepts). - Production discipline: A 6-item pre-delivery checklist covering region coverage, counts and labels, real text preservation, realism framing, style specificity, and JSON validity. - Satellite workflows: Companion documents cover static ad recreation (fractional-coordinate layout zones, safe-zone rules, brand-vs-structure separation) and product reference sheet generation with identity-lock prompts. - Use Case: A user asks for "a landing page for a matcha tea startup" and receives a complete structured JSON prompt with header, hero, and ingredient-grid regions ready to paste directly into GPT Image 2.0. ## Quick Start Ask the assistant to write a GPT Image 2.0 prompt for your concept, describing the subject and whether it is a layout-heavy design, a single scene, or just a theme.

Frequently Asked Questions about higgsfield-gpt-image-2

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I write a GPT Image 2.0 prompt for a UI mockup or landing page?▼

Use Format A, a single structured JSON object describing every visible region with fields like type, style, layout, and background. Name positions explicitly (top-left, mid-right) and give counts plus labels for repeated items like buttons or icons.

What prompt format works best for portraits and cinematic scenes in GPT Image 2.0?▼

Use Format B, one dense prose paragraph ordered from medium and subject through setting, lighting, palette, and mood. Use film-photography language like "35mm film photograph" instead of "photorealistic" to avoid plasticky skin on faces.

When should I use a meta-prompt instead of a direct image prompt?▼

Use Format C when the user provides only a theme and wants the model to self-generate the entire composition, such as a poster about a topic or a relationship diagram. If specific layout details are given, use Format A instead.

Can GPT Image 2.0 render text in multiple languages inside images?▼

Yes, GPT Image 2.0 renders multi-line paragraphs, mixed CJK and Latin scripts, and small UI labels sharply. Embed the exact text in quotation marks within the prompt and keep non-Latin scripts in their original form.

How do I recreate a winning ad format with my own brand using GPT Image 2.0?▼

Use the static-ads satellite workflow: derive the reference ad's layout as fractional-coordinate zones, generate a brand-neutral wireframe, then override all visual elements with your brand's colors and typography. Keep the top and bottom 10% of the frame free of text and buttons.

Why do GPT Image 2.0 faces look plasticky and how do I fix it?▼

The plasticky-skin effect is triggered by realism-flagged prompts using words like "photorealistic". Frame realism as film photography instead — grain, flash, 35mm, editorial portrait — which produces the desired look without the failure mode.

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