Image prompting skill for Nano Banana (NBP/NB2) and GPT Image 2.5 (Flare/Sunburst). Writes ready-to-use prompts with model/quality/size recommendations. Use when: "нарисуй", "сгенерируй картинку", "image prompt", "промпт для картинки", blog covers, slides, posters, product shots, UI mockups, storybo

Image Prompting — Nano Banana & GPT Image 2.5 This skill writes image prompts. It does not generate images. The output is: model name + quality / size / aspect ratio + the prompt itself. The body of this SKILL.md is intentionally thin so you cannot fake a result by reading it alone. The actual rules — what the models reward, what they punish, how to phrase a 5 slot template, when to add quality: high , when to use image grounding — live only in the reference files. Route first — is this actually an image prompt task? Motion, clips, montage (Seedance, Kling, Veo, any image to video): use the sibling video skill. This skill's storyboard and keyframe outputs feed it. No idea or script yet (user wants a concept or an ad scenario, not a picture): if the creative director skill is installed, start there — it develops ideas and scripts for commercials and beyond ([github.com/smixs/creative director skill](https://github.com/smixs/creative director skill)). A concrete image is needed — this skill. Continue below. Mandatory reading order — DO NOT WRITE A PROMPT WITHOUT THIS Past attempts to write prompts directly from this skill body produced lazy, generic results. Each model has its own physics; common rules collapse into mush when applied without model specific syntax. Read in this order before producing any prompt: Step 1 — always read first → [models.md](references/models.md) Decide: Nano Banana (NB2 or NBP) or GPT Image 2.5 (Flare for speed, Sunburst for precision edits). The choice changes the prompt syntax fundamentally — natural language paragraphs vs. labeled 5 slot template, quality settings, which features exist (image grounding only on NB, EXACT TEXT discipline only on GPT Image, etc.). If the user named a model — confirm and proceed. If not — pick using the table in models.md , then state your choice in the output header. Step 2 — read one model file (the one you picked) Nano Banana → [nano banana.md](references/nano banana.md) Image grounding for real locations. Extreme aspect ratios (1:8, 8:1, 4:1). Thinking mode. JSON for 5+ elements. Up to 14 reference images. Why you must NOT write 50mm / f stop / ISO numbers. GPT Image 2.5 → [gpt image.md](references/gpt image.md) 5 slot template (Scene / Subject / Important Details / Use Case / Constraints). Anti slop banned words list. quality: low / medium / high / xhigh / max as a deliberate fidelity lever. Size constraints (multiples of 16, max 3:1, up to 4K 3840×2160). Two column edit logic (Change / Preserve / Constraints). Up to 16 reference images with explicit roles. The model file is non negotiable. Skipping it is the single biggest cause of weak prompts. Step 3 — always read after the model file → [golden rules.md](references/golden rules.md) Universal rules that apply to both models: start with a verb, positive framing, hex colors, quote text, edit don't re roll, one change per iteration, reference images. Step 4 — task shaped reading (load only what matches the request) Pick zero or more, depending on what the user asked for: Text in image, infographic, diagram, multilingual rendering → [text rendering.md](references/text rendering.md) Edit existing image (object removal, lighting swap, colorization, restoration, localization) → [editing.md](references/editing.md) Character continuity across multiple images / panels → [characters.md](references/characters.md) Presentation slides → [slides.md](references/slides.md) Sequential narrative (storyboard, comic, panel sequence) → [storyboards.md](references/storyboards.md) Sketch → final, wireframes, structural input → [structural.md](references/structural.md) 2D → 3D, floor plans, isometric → [dimensional.md](references/dimensional.md) Vision analysis / image to prompt / style transfer from a reference image → [vision decomposer.md](references/vision decomposer.md). Load this whenever the user attaches an image and asks to recreate, match, decompose, or transfer its style. Multi panel compositions (grids, collages, storyboard sheets in ONE image) → [multi panel.md](references/multi panel.md). 9 cell TVC grids, 2x2 portrait grids, 3 panel campaign collages, 4x3 borderless grids, 6 frame cinematic sequences, before/after splits, 12 panel storyboard posters. Industry pattern libraries — proven prompt templates by vertical. Load the matching file: E commerce product shots → [patterns/ecommerce.md](references/patterns/ecommerce.md) Fashion editorial campaigns → [patterns/fashion editorial.md](references/patterns/fashion editorial.md) Food & beverage advertising → [patterns/food beverage.md](references/patterns/food beverage.md) Cinematic portraits → [patterns/portrait cinema.md](references/patterns/portrait cinema.md) Posters & illustration → [patterns/poster illustration.md](references/patterns/poster illustration.md) Character design (turnarounds, expression sheets, outfit grids) → [patterns/character design.md](references/patterns/character design.md) UI mockups & social media formats → [patterns/ui social.md](references/patterns/ui social.md) Step 5 — read for production language → [creative direction.md](references/creative direction.md) Studio quality vocabulary for lighting design, camera and hardware, color grading and film stock, materiality and texture. Read when you need precise terms beyond what golden rules.md covers. Step 6 — read if structuring a complex prompt → [prompt framework.md](references/prompt framework.md) Universal element checklist (subject, context, action, environment, camera, lighting, mood, materials, palette, format), detail modes (concise / standard / verbose / cinematic verbose), parameterized templates, output structure with parameters and exclusions. Output format When you return the prompt, structure it like this: For edits, also include an explicit preserve list (mandatory for gpt image 2.5, recommended for nano banana): Final response style Prefer: ready to copy prompts, hex colors, concrete materials, named compositions, model specific syntax (5 slot for GPT Image, natural prose for Nano Banana). Avoid: tag soup ("cool, modern, 4k"), vague praise ("stunning, epic, masterpiece" — actively hurts GPT Image 2.5), negative framing ("no people, no cars" — invert to positive), external comparisons ("like Apple ad" — describe the visual properties instead), numerical lens parameters in Nano Banana prompts (it ignores them). Author: Serge Shima ([t.me/aimastersme](https://t.me/aimastersme) · [sergeshima.com](https://sergeshima.com) · [aimasters.me](https://aimasters.me)) · License: CC BY 4.0 — attribution required · Source: [smixs/visual skills](https://github.com/smixs/visual skills)