Higgsfield Gpt Image 2

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 9e3a2f685c72 · 27 Oct 2026Report an issue or request a re-scan

• PublisherOSideMedia GitHub userOwner of the repository this skill is read from
• Source folderOSideMedia/higgsfield-ai-prompt-skill/skills/higgsfield-gpt-image-2The exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdict9e3a2f685c72A new audit runs when the source changes
• Repository statusNot archivedLast push 2 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Use when the user mentions GPT Image 2.0 or GPT Image 2.5, gpt-image-2, gpt-image-2.5, gpt_image_2_5, GPT-Image-2 prompts, the Flare / Sunburst variants, a transparent-background image, or wants to generate an image…

---
name: higgsfield-gpt-image-2
description: "Use when the user mentions GPT Image 2.0 or GPT Image 2.5, gpt-image-2, gpt-image-2.5, gpt_image_2_5, GPT-Image-2 prompts, the Flare / Sunburst variants, a transparent-background image, or wants to generate an image with GPT Image 2.0 or 2.5. Covers when to prefer GPT Image 2.5 over 2.0 (transparent background, xhigh/max quality, extra aspect ratios — from platform and OpenAI facts), the CLI-vs-MCP disagreement on gpt_image_2, the three-format prompt taxonomy (Format A structured JSON for UI mockups and layout-dense images; Format B dense cinematic prose for single-subject scenes; Format C auto-derive meta-prompt for theme-only concepts), per-format craft patterns, output conventions, the 6-item pre-delivery checklist, and cross-surface workflow context (companion static-ads-workflow.md for ad recreation; higgsfield-marketing-studio cross-surface-workflow.md §3 for ms_image / DTC Ads Higgsfield-native alternative)."
user-invocable: true
metadata:
  tags: [higgsfield, gpt-image-2, gpt-image-2.5, flare, sunburst, transparent-background, prompt-director, image, json, prose, meta-prompt, layout, mockup, infographic, character-sheet, ui-mockup, landing-page, static-ads, cross-surface]
  version: 1.3.1
  updated: 2026-09-26
  parent: higgsfield
---

# Higgsfield GPT Image 2.0

<!-- CONTENTS — generated by scripts/build_index.py; do not hand-edit -->
**Contents**

- [GPT Image 2.5](#gpt-image-25)
  - [Surface](#surface)
  - [When to prefer 2.5 vs 2.0](#when-to-prefer-25-vs-20)
  - [CLI ↔ MCP disagreement on `gpt_image_2`](#cli--mcp-disagreement-on-gpt_image_2)
  - [Prompting status on 2.5](#prompting-status-on-25)
- [1. What GPT Image 2.0 is](#1-what-gpt-image-20-is)
- [2. Three prompt formats](#2-three-prompt-formats)
  - [Tie-break](#tie-break)
- [3. Format A — Structured JSON](#3-format-a--structured-json)
  - [Core fields to reach for](#core-fields-to-reach-for)
  - [Key patterns that make JSON prompts work](#key-patterns-that-make-json-prompts-work)
  - [Worked example — minimal shape](#worked-example--minimal-shape)
- [4. Format B — Dense cinematic prose](#4-format-b--dense-cinematic-prose)
  - [What makes prose prompts work](#what-makes-prose-prompts-work)
  - [Worked example](#worked-example)
- [5. Format C — Auto-derive meta-prompt](#5-format-c--auto-derive-meta-prompt)
  - [Structure](#structure)
  - [When to use](#when-to-use)
- [6. Routing decision](#6-routing-decision)
- [7. Output format conventions](#7-output-format-conventions)
- [8. Pre-delivery checklist](#8-pre-delivery-checklist)
- [9. Example routings](#9-example-routings)
- [10. Cross-surface workflow context](#10-cross-surface-workflow-context)
- [11. Source acknowledgment](#11-source-acknowledgment)
  - [Per-claim translation calibration](#per-claim-translation-calibration)
  - [Verification trail](#verification-trail)

<!-- /CONTENTS -->

A prompt director for GPT Image 2.0. Converts plain-text concepts into production-ready prompts that route by output type: structured JSON for layout-dense images (UI mockups, infographics, character sheets, multi-panel posters), dense cinematic prose for single-subject scenes (portraits, photographs, landscapes), or auto-derive meta-prompts for theme-only concepts where the model self-generates the composition.

Translated from Adil Aliyev's `gpt-image-2-director` source corpus per the v3.7.13 / v3.7.15 translation precedent. Two companion satellites extend this sub-skill: `static-ads-workflow.md` covers the ad-recreation workflow that uses GPT Image 2.0 as its generation engine, and `reference-sheet-workflow.md` covers the Automatic Product Reference Sheet + Automatic Prompt Creator workflow (one product image → a multi-view identity-locked reference sheet for high-consistency generation).

Sections 1–11 were written for **GPT Image 2.0**. GPT Image 2.5 — its surface, when to prefer it, and what is and is not documented about prompting it — is the next section.

---

## GPT Image 2.5

New on Higgsfield in the 2026-09-26 catalog as `gpt_image_2_5` — **not yet field-rated**. Facts
below are `[OFFICIAL — platform, snapshot 2026-09-26]` (MCP and CLI agree on all of them) or
`[OFFICIAL — OpenAI docs]` from the [image generation guide](https://platform.openai.com/docs/guides/image-generation)
and the [Sunburst](https://platform.openai.com/docs/models/gpt-image-2.5-sunburst) /
[Flare](https://platform.openai.com/docs/models/gpt-image-2.5-flare) model pages (fetched 2026-09-26).

### Surface

| Param | Values (default first) |
|---|---|
| `variant` | `flare` · `sunburst` |
| `quality` | `low` · `medium` · `high` · **`xhigh`** · **`max`** |
| `resolution` | `1k` · `2k` · `4k` |
| `background` | omit (model default) · `auto` · `opaque` · **`transparent`** |
| aspect ratio | auto, 1:1, 3:2, 2:3, 4:3, 3:4, 16:9, 9:16, 21:9, 27:16, 16:27, 9:8, 8:9, 4:5, 5:4 |
| media | `image_references` (generation and editing) |

A bare call is `flare` / `low` / `1k` — draft settings. OpenAI: "Use `quality: "low"` for quick
drafts. For final assets, compare higher quality settings to find the right balance of detail,
latency, and cost." State `quality` and `resolution` explicitly in every delivered settings header.

### When to prefer 2.5 vs 2.0

Only platform and vendor facts — neither model has been field-rated against the other here, so no
quality ranking is claimed.

**Prefer GPT Image 2.5 when:**
- **The asset needs a transparent background** — `background: transparent`. It is the first
  transparent-background route in the MCP catalog, and the only one both Higgsfield surfaces
  agree on. OpenAI's API needs PNG or WebP output for transparency; Higgsfield exposes no
  output-format parameter, so confirm the downloaded file actually carries alpha before
  building on it.
- **A quality tier above `high` is wanted** — `xhigh` / `max`. OpenAI: earlier GPT Image models
  "support quality settings up to `high`".
- **The frame needs 27:16, 16:27, 9:8, or 8:9** — 2.5 

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/OSideMedia/higgsfield-ai-prompt-skill
git -C higgsfield-ai-prompt-skill checkout 9e3a2f685c72

Works in

1. 2026-10-27
   ✓SAFE B 89 / 100

   first audit · source 9e3a2f685c72

Questions about Higgsfield Gpt Image 2

• github.com/OSideMedia/higgsfield-ai-prompt-skill/tree/main/skill — the audited source at commit 9e3a2f685c72 and every finding aboveObserved · 27 Oct 2026

Spotted something wrong? Report a correction · Methodology