Generate images using the brand's visual identity and Gemini API. Reads brand/creative-kit.md for visual style, crafts narrative prompts, and produces images via Nano Banana Pro (gemini-3-pro-image-preview). Supports on-brand and freestyle modes. Use when the user needs a blog header, social graphic, product shot, hero image, banner, thumbnail, or any generated image. Also use proactively when building content that would benefit from visuals. Triggers on "generate image", "create image", "make me an image", "blog header", "social graphic", "product shot", "hero image", "banner", "thumbnail", "I need an image", "visual for", or any request for generated artwork. Even if they just say "image" or "picture for this" — this is the skill.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
Generate images using the brand's visual identity and Gemini API. Reads brand/creative-kit.md for visual style, crafts narrative prompts, and produces images via Nano Banana Pro (gemini-3-pro-image-preview). Supports on-brand and freestyle modes. Use when the user needs a blog header, social graphic, product shot, hero image, banner, thumbnail, or any generated image. Also use proactively when building content that would benefit from visuals. Triggers on "generate image", "create image", "make me an image", "blog header", "social graphic", "product shot", "hero image", "banner", "thumbnail", "I need an image", "visual for", or any request for generated artwork. Even if they just say "image" or "picture for this" — this is the skill.
["generate image","create image","blog header","social graphic","product shot","need an image","make me an image","visual for","hero image","banner","thumbnail"]
allowed-tools
["Bash(python3 *)"]
/image-gen — On-Brand Image Generation
Describe what you need. Get an image that looks like your brand made it.
This skill reads your visual brand identity from brand/creative-kit.md, crafts a narrative prompt that bakes in your style constraints, and generates the image via Gemini API. No brand style defined yet? It still works — just at a lower enhancement level. Run /visual-style first for the best results.
On Activation
Check GEMINI_API_KEY environment variable.
If missing: "Image generation requires a Gemini API key. Set GEMINI_API_KEY in your environment. Get one at ai.google.dev."
Do not proceed without it.
Read brand files in priority order:
brand/creative-kit.md — look for ## Visual Brand Style section
brand/voice-profile.md — personality informs image tone
Fully on-brand — style anchors, lighting, mood, composition all applied
Phase 1: Discovery
Use AskUserQuestion. Ask one at a time. Skip questions the user already answered in their request.
Question 1: Purpose
"What's this image for?"
Blog header / article illustration
Social media post (which platform?)
Product shot / marketing asset
Hero image / landing page
Presentation / slide deck
Something else (describe it)
This determines aspect ratio:
Use case
Default ratio
Resolution
Blog header
16:9
2K
Social square
1:1
2K
Social story
9:16
2K
Hero / banner
21:9
2K
Product shot
4:3
4K
Thumbnail
16:9
1K
Question 2: Feeling
"What should someone feel when they see this?"
(Free text — this drives the prompt's emotional anchor)
Question 3: Style override (only if L3 brand style exists)
"Use your on-brand style, or something different?"
On-brand (default) — applies Visual Brand Style from creative-kit.md
Different — describe the style you want instead
If user picks "different," their description overrides the brand style for this image only.
Skip discovery when: The user's request is specific enough. "Generate a 16:9 blog header showing a glowing terminal on a dark background, warm rim lighting" — don't ask what they want, they just told you.
You are an expert Nano Banana prompt engineer. Your job is to turn the user's brief into a single, high-quality prompt for Nano Banana 2 (or Pro), a "thinking" image model used for professional asset production.
Core principle: brief a senior art director, don't list keywords. Write natural language in full sentences. Never use "tag soup" like "dog, park, 4k, realistic."
The 10 Rules
1. General style. Be specific and descriptive about subject, setting, composition, camera/viewpoint, lighting, mood, materials, and textures. Full sentences, not comma lists.
2. Context and purpose. Always encode the purpose and audience (YouTube thumbnail, app icon, hero banner, tweet graphic, 4K wallpaper). Let purpose guide style, polish level, and framing.
3. Text and infographics. If text must appear, put it clearly in quotes in the prompt. Ask for legible, clean typography and specify style (bold sans-serif, monospace, handwritten). For data, ask the model to compress into infographics, diagrams, or whiteboards.
4. Character and brand consistency. When reference images exist, explicitly refer to them: "Keep the person's facial features exactly the same as Image 1." Allow changes in pose, expression, angle while preserving identity.
5. Grounding and realism. For real data, locations, or products, tell the model to rely on up-to-date factual knowledge. Encourage coherent details consistent with physics.
6. Editing and restoration. For edits to existing images, give semantic instructions: "remove," "replace," "add," "restore," "change the season." Maintain original structure, only change what's intended.
7. Dimensional and structural control. For floor plans, schematics, wireframes, grids, tell the model to follow that layout closely. For 2D↔3D, describe how the new representation should look while preserving key relationships.
8. Resolution, detail, and format. Specify resolution ("high detail suitable for 4K wallpaper," "clean 16:9 thumbnail"). Call out micro details and textures when needed (brushed steel, cracked paint, mossy stone).
9. Narrative and sequences. For multiple images, describe the story arc, emotional beats, what stays consistent across images. Specify count, format, and identity/style consistency.
10. Output rules. Do not ask follow-up questions about the prompt. Resolve small ambiguities with sensible professional defaults. Output a single flowing narrative prompt.
Prompt Structure
Build the narrative in this order, woven into flowing prose:
Purpose and format — what this is for, aspect ratio, resolution
Scene — what's happening, where, environment
Subject — detailed description with textures, materials, poses
Composition — framing, focal point, depth of field, negative space
Lighting — source, quality, color temperature, interaction with materials
Mood — emotional tone, atmosphere
Text — any text in quotes with typography specification
Technical — camera/lens for photorealistic, style reference for illustrated
Brand Constraints (L3)
When Visual Brand Style exists, weave constraints INTO the narrative — don't add as a separate block:
Primary Aesthetic → sets overall style direction
Lighting → overrides generic lighting with brand-specific lighting
Backgrounds → constrains background treatment
Composition → constrains layout/framing
Mood → anchors emotional tone
Avoid → explicit exclusions baked into prompt
Reference Prompts → use as structural templates, adapting subject matter
import os
from google import genai
from google.genai import types
client = genai.Client(api_key=os.environ["GEMINI_API_KEY"])
response = client.models.generate_content(
model="gemini-3.1-flash-image-preview",
contents=["<narrative prompt>"],
config=types.GenerateContentConfig(
response_modalities=['TEXT', 'IMAGE'],
image_config=types.ImageConfig(
aspect_ratio="<ratio>",
image_size="<resolution>",
),
),
)
for part in response.parts:
if part.text:
print(part.text)
elif part.inline_data:
image = part.as_image()
image.save("output.png")
Default to gemini-3.1-flash-image-preview (Nano Banana 2). Launched Feb 26, 2026. Pro quality at Flash speed/pricing. 4K support, up to 14 reference images for style consistency. Use gemini-3-pro-image-preview (Nano Banana Pro) only when text-heavy infographics or premium quality justify the higher cost.
Save and Log
Save image to project directory (e.g., images/, assets/, or wherever the project keeps visuals)
Append to brand/assets.md:
| <date> | image | <file-path> | image-gen | <1-line description of what was generated> |
Phase 4: Iterate
After generating, show the image and offer:
"Image generated. Want me to:"
Adjust the lighting or mood
Change the composition or framing
Try a completely different approach
Generate more variations
Ship it
For adjustments, use Gemini's multi-turn chat API for iterative refinement — pass the previous image + adjustment prompt.
chat = client.chats.create(model="gemini-3-pro-image-preview")
response = chat.send_message("Make the lighting warmer and add more negative space on the left for text")
Image Editing
When the user provides an existing image to modify:
