# Image > > - **Type:** Skill - **Install:** `agentstack add skill-smixs-visual-skills-image` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [smixs](https://agentstack.voostack.com/s/smixs) - **Installs:** 0 - **Category:** [Content & Media](https://agentstack.voostack.com/c/content-and-media) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [smixs](https://github.com/smixs) - **Source:** https://github.com/smixs/visual-skills/tree/main/image - **Website:** https://github.com/smixs/visual-skills ## Install ```sh agentstack add skill-smixs-visual-skills-image ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Image Prompting — Nano Banana & GPT Image 2 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. --- # 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. 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** → [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` as a deliberate fidelity lever. Size constraints (multiples of 16, max 3:1, up to 2560×1440). 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: ``` Model: Quality: (only for gpt-image-2) Size / Ratio: Prompt: Notes: - ``` For edits, also include an explicit preserve-list (mandatory for gpt-image-2, recommended for nano-banana): ``` Change: Preserve: Constraints: ``` --- # 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), 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). ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [smixs](https://github.com/smixs) - **Source:** [smixs/visual-skills](https://github.com/smixs/visual-skills) - **License:** MIT - **Homepage:** https://github.com/smixs/visual-skills Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-smixs-visual-skills-image - Seller: https://agentstack.voostack.com/s/smixs - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.