gpt-image-2-pro-max

name	gpt-image-2-pro-max
description	Production prompt-engineering pipeline for GPT-Image-2 / OpenAI image generation. Pairs a 'media-designer' agent with a hosted searchable corpus of 7,405 community-vetted prompts (9,903 reference images), decomposed across 10 controlled vocabularies (subjects, styles, lighting, cameras, moods, palettes, compositions, mediums, techniques, usecases). Each record carries: full prompt body, twitter/X attribution link, downloaded reference image. Workflow: agent diagnoses the user brief → searches the corpus → picks a mood-aligned base → refactors the chosen prompt into a parameterised {argument} template → resolves arguments from user intent → returns the final paste-ready prompt with attribution + reference image. Use when the user wants a polished image-generation prompt for ads, posters, product shots, portraits, character sheets, UI mockups, infographics, exploded-view diagrams, or any other GPT-Image-2 / OpenAI image task.
category	ai
keywords	["gpt-image-2","image-prompts","prompt-library","openai-image","ai-image","prompt-engineering","ad-creative","product-photography","media-designer"]
metadata	{"author":"Richard Ng","version":"1.0.0","endpoint":"https://gpt-image-2-prompts.goclawoffice.com"}

GPT-Image-2 Prompt Library + Media Designer Agent

Two-piece skill:

1. scripts/search.py — thin HTTP client over a hosted corpus of 7,405 community-vetted prompts (9,903 reference images). BM25-ranked, tagged across 10 facets.
2. ~/.claude/agents/media-designer.md — agent profile that uses the search tool to turn a user brief into a paste-ready GPT-Image-2 prompt.

The tool finds candidates. The agent owns the judgement (which base, which slots to parameterise, which to keep literal, mood/palette fit).

When to Apply

Must use

• User wants a GPT-Image-2 / OpenAI image-generation prompt for a real production task (ad, poster, product shot, character sheet, UI mockup, portrait)
• User describes a brief and wants a polished prompt back, not just inspiration
• User is studying how top creators structure prompts and wants attributed examples

Skip

• User wants the image rendered — route to ai-multimodal or ai-artist
• Task unrelated to image prompts
• User already has a finished prompt and just wants it run

Recommended Workflow

For any production prompt request:

1. Read ~/.claude/agents/media-designer.md
2. Run the 6-step workflow it defines
3. Return the 4-block output (Base · Parameterised · Resolved · Rationale)

Hosted backend

Endpoint: https://gpt-image-2-prompts.goclawoffice.com

7,405 community-vetted prompts and 9,903 reference images indexed across 10 facets (subjects, styles, lighting, cameras, moods, palettes, compositions, mediums, techniques, usecases). Each record carries the prompt body, attribution, and a reference image. Rate-limited per IP — fair-use friendly, but please don't scrape.

CLI

search.py [query] [--shape SHAPE] [--has-image] [-n N] [--full] [--persist PATH]

# Use the shared skill venv on Windows / POSIX:
#   Windows : .claude\skills\.venv\Scripts\python.exe
#   POSIX   : .claude/skills/.venv/bin/python3
# Examples below use the Windows path; swap on POSIX.

# Free-text search (this is what the agent calls)
.claude\skills\.venv\Scripts\python.exe .claude\skills\gpt-image-2-pro-max\scripts\search.py "luxury shoe ecommerce ad cream pastel" -n 5

# Narrow by shape when the brief is specific about format
.claude\skills\.venv\Scripts\python.exe .claude\skills\gpt-image-2-pro-max\scripts\search.py "perfume bottle" --shape ecommerce -n 3

# Persist top hits as a markdown reference deck (with embedded images)
.claude\skills\.venv\Scripts\python.exe .claude\skills\gpt-image-2-pro-max\scripts\search.py "neon ui" --persist plans\neon-refs.md

Filter knobs:

• --shape — portrait | poster | ui | character | comparison | ecommerce | ad | thumbnail | infographic | comic
• --has-image — only records with a reference image
• -n N — top N (default 5)
• --full — don't truncate prompt body
• --persist PATH — write top results to a markdown file with embedded reference images

Output Anatomy

#1  bm25=-15.59  shape=ecommerce
  id    : z9q36mnc
  title : Futuristic Bionic Super Shoe
  author: @<creator>
  tweet : https://x.com/<creator>/status/<tweet_id>
  image : <reference image URL>
  tags  : subjects=product,fashion-item | styles=cinematic | cameras=low-angle |
          moods=luxurious,intense,futuristic | palettes=gold-black |
          techniques=parameterised-template
  prompt:
    Extreme futuristic {argument name="subject" default="cheetah bionic super shoe"} ...

Agent Profile

.claude/agents/media-designer.md defines the canonical workflow. Headline contents:

Section	Purpose
Mental model	brief → diagnose → search → pick (mood-aware) → refactor → resolve → output
Step 1 — Diagnose	Extract product, brand, shape, mood, palette, technique signals from the brief
Step 2 — Search	Synthesise tokens, run .claude/skills/.venv/Scripts/python.exe .claude/skills/gpt-image-2-pro-max/scripts/search.py "<tokens>" -n 5
Step 3 — Pick	Mood-mismatch rejection table — pastel briefs reject moody/gritty/dark-amber, etc.
Step 4 — Refactor	Replace product-specifics with {argument name="X" default="Y"} slots; keep mood/lighting/style words literal
Step 5 — Resolve	Fill slots from user intent; default-fallback when ambiguous; never invent
Step 6 — Output	4 blocks: Base (cite author + tweet) · Parameterised · Resolved · Rationale (≤80 words)