GPT-Image2-Skill
GPT Image 2/2.5 prompt gallery, image prompt library, agentic skill, and CLI for OpenAI image generation/editing
Install / Use
npx skills add wuyoscar/GPT-Image2-SkillInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
AI & Machine LearningSupported Platforms
Our assessment of GPT-Image2-Skill
GPT-Image2-Skill scores 84/100 on our quality scale, 597th of 965 AI & Machine Learning skills we index.
Its SKILL.md is 220 KB long, well organised into 133 sections with 147 code examples: long enough that it reads more like full documentation than a focused instruction file, which agents can find harder to follow.
With 6,009 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 36 days ago, so GPT-Image2-Skill is actively maintained.
- Our last check on 2026-10-18 found the source still online.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the first 100 KB of the file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.
AI review by kimi-k2.7-code on 2026-09-24. Automated pattern scan on 2026-09-24. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
GPT-Image2-Skill compared with similar skills
All 4 of these similar skills score higher than GPT-Image2-Skill; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| GPT-Image2-Skill (this skill)by wuyoscar | 84 | 6.0k | 36d ago | SKILL.md |
| claude-memby thedotmack | 100 | 113.5k | today | CLAUDE.md |
| taste-skillby Leonxlnx | 100 | 110.6k | today | CLAUDE.md |
| Understand-Anythingby Egonex-AI | 100 | 90.9k | 10d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 78.9k | today | CLAUDE.md |
Frequently asked questions
- How do I install GPT-Image2-Skill?
- Run
npx skills add wuyoscar/GPT-Image2-Skill. The install tabs above show the steps for each supported agent. - Which AI agents does GPT-Image2-Skill work with?
- It is written for Claude Code and OpenAI Codex, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is GPT-Image2-Skill safe to use?
- Our scan of the first 100 KB of the file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is GPT-Image2-Skill still maintained?
- The repository was last updated 36 days ago, so GPT-Image2-Skill is actively maintained.
Skill content
View source on GitHub<a id="overview"></a>
🧭 What about this report
<table border="1" cellspacing="0" cellpadding="6"> <tr> <th align="left">Item</th> <th align="left">Value</th> </tr> <tr> <td>Gallery size</td> <td><strong>163 numbered entries</strong> across 31 categories, with selected images below</td> </tr> <tr> <td>Surfaces</td> <td><strong>2 Agent Skills + CLI</strong>: Claude Code / Codex, OpenClaw, Hermes Agent and other skill-capable agent runtimes</td> </tr> <tr> <td>Last update</td> <td><strong>2026-09-10</strong></td> </tr> <tr> <td>Docs</td> <td><strong>English + 中文</strong></td> </tr> </table>The repo keeps the GPT Image 2 prompt collection and gallery alongside 2.5 API support, reading material and task-specific references. The two skills handle image generation/editing and image-to-prompt extraction.
TBH, GPT Image 2.5 feels seriously capable. I prefer giving it a clear reference: a shape, a sketch or an image. Sometimes showing a layout from a PDF is more useful than describing it at length. It's how I like to work with GPT-6, too: minimize the prompt; make the reference clear.
I collect prompts, useful building blocks and references here to help you find a workflow that suits the job. Thanks for all the love this little gallery has received 🫶.
For the CLI, export the relevant PDF pages as PNG, WebP or JPG, then attach them with -i. See the supported image-reference formats. Keep required text and edit constraints explicit.
For PPT work, try vector-style diagrams, icons and slide layouts. The Image API outputs PNG, JPEG or WebP; editable SVG or PowerPoint shapes need a separate authoring step.
<a id="sunburst-samples"></a>
✨ Made with GPT Image 2.5
Two 2K samples: an exploded watch assembly with detailed callouts, and a multi-storey cafe cutaway built from a reference image. Both use gpt-image-2.5-sunburst, 2048x2048 and high.
A · No. 113 · Prompt
Create a premium technical exploded-view illustration of a fictional mechanical wristwatch called the Meridian 8, centered on a dark slate background with fine blueprint grid accents. Show the watch components separated vertically with precise spacing: sapphire crystal, dial, hands, chapter ring, movement plates, escapement, balance wheel, mainspring barrel, case, crown, and leather strap sections. Use realistic brushed steel, brass, ruby jewel accents, and deep navy dial details. Add crisp callouts and labels with the in-image text "Meridian 8", "Exploded Assembly", "42 mm Case", "25 Jewels", and "Power Reserve 72 h". Include numbered callouts "01" through "10" with short labels like "Balance Wheel", "Mainspring Barrel", and "Sapphire Crystal". The result should be highly detailed, technically believable, sharply rendered, and suitable for an industrial design plate with clean hierarchy, exact labeling, and refined material realism.
B uses the reference from No. 54, keeping the overall street layout while reworking the interiors, floors and lighting. Reference attribution: EvoLinkAI · Source.
<a href="docs/isometric/isometric-cafe.png"><img src="docs/isometric/isometric-cafe.png" width="320" alt="Original isometric cafe district used as the edit reference"/></a>
B · No. 54 · Edit prompt
Use the reference image as the layout anchor for a richly detailed isometric two-block cafe district at blue hour. Keep the street footprint, corner cafe, neighboring bookstore, bakery and fountain plaza recognizable. Transform it into a three-storey architectural cutaway diorama with coherent 30-degree isometric geometry.
Open the front-facing walls to reveal the cafe espresso bar and upstairs jazz lounge; bookshelves, reading nooks and a spiral staircase in the bookstore; pastry cases and a working oven in the bakery. Add a rooftop glass greenhouse, tiny terraces, copper plumbing, tiled stairs, balconies, hanging plants and warm lights visible through rain-speckled windows. At street level show wet cobbles, bicycles, the coffee cart, varied miniature pedestrians and reflections around the fountain. Every floor, doorway and staircase should connect plausibly.
Use warm amber interiors against deep teal evening shadows, tactile brick, glazed tiles, glass and brushed brass. Preserve crisp detail throughout the scene, with a clean dark navy background and room around the floating diorama. Give the scene depth through cutaway rooms and layered architecture. Use restrained, readable storefront lettering: "NIGHT OWL CAFE", "OPEN BOOKS", and "DAWN BAKERY". Keep the composition square and visually balanced.
See the sample record for settings, inputs and review notes.
<a id="image-example"></a>
🖼️ From reference image to result
Thanks to @LunarXuan for Get Prompt from Image. A vision-capable agent extracts a prompt from a reference image, then passes it to gpt-image or another generator. The contributor-provided reference and its generated result are shown below.
Attach an image and invoke the skill with a slash command, $get-prompt-from-image, or plain language:
/get-prompt-from-image
Extract a reusable English positive prompt and a targeted negative prompt from this image, then recreate it with gpt-image.
📝 Extracted prompt used for the generated result
Positive Prompt
A highly polished semi-realistic Japanese narrative illustration rendered in a painterly digital style, using varied brush widths, a combination of hard edges and soft transitions, restrained contour lines, and controlled surface texture. The image should feel like a cold cinematic game-concept artwork. Use a wide 16:9 composition with strong depth in a snowy urban alley, where the snow-covered road narrows toward a distant vanishing point near the center. Place a large fluffy dark blue-gray wolfdog in the left foreground, shown in side profile facing right with its head raised, interacting with a hooded young woman kneeling near the center-right. She crouches in the snow facing left, gently touching the wolfdog’s muzzle or forehead with one gloved hand while the other rests near her knee for balance, creating a restrained and intimate gesture. She wears an oversized pale-gray winter hooded jacket with pointed ear-like details on top, dark gray panels, pockets, straps, and small muted red-orange accents, over black clothing, fitted black pants, and heavy dark boots. Short black or deep-brown hair falls from beneath the hood; her face is partly shadowed as she looks down at the wolfdog with a quiet, tired, yet gentle expression. Render the wolfdog’s fur with layered directional brushstrokes, making the back, neck, and tail thick and voluminous, with cool blue-gray shadows, pale highlights, and a subtle rim light along the silhouette. On the left, include metal fencing, utility boxes, and dense dark shrubs; in the distance, show tall urban buildings, street lamps, utility poles, and a blue-gray sky. On the right, include dark building facades, windows, snow-covered roof edges, evergreen branches, and foreground cardboard boxes and industrial clutter. Any environmental labels should remain blurred graphic marks with no readable text. Let the main light enter from the distant upper-left side of the alley, combining cold blue ambient shadows with warm golden reflections in the distance. Add subtle rim light to the snow, the woman, and the wolfdog, with medium-high contrast and warm orange clothing details acting as focal accents. Snow, slush, and shallow puddles in the foreground should show damp reflections. Use atmospheric perspective to soften distant buildings while keeping the woman and wolfdog clear. Establish depth through foreground, middle ground, background, occlusion, and perspective lines rather than strong blur. The mood is loneliness, trust, and a brief moment of tenderness in a frozen city. Preserve rough painterly strokes, cool-warm contrast, cinematic composition, and refined post-processing. Clearly remain a 2D semi-realistic painterly illustration, not photography, pure flat vector art, or 3D rendering.
Negative Prompt
photorealistic, 3D render, flat vector style, pure cel shading, watercolor bleed, oil painting impasto, chibi proportions, deformed anatomy, malformed hands, extra limbs, oversized wolf, sunny summer weather, cluttered composition, readable text, watermark
<a id="quickstart"></a>
🚀 Quick start
| Task | Use |
|---|---|
| Generate or edit an image | gpt-image |
| Extract a prompt from a reference | get-prompt-from-image |
| Work in a terminal | CLI examples below, with the selected --model |
🎛️ Choose a model
| Model | Best starting point | |---|---| | `gpt-image-
Truncated for display — read the full file on GitHub.
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
