Install
openclaw skills install @cellcog/nano-banana-image-cellcogAI multi-image generation powered by CellCog via Nano Banana. 10-20 coherent images in one prompt, character consistency across scenes, production-grade composition. Nano Banana AI, Nano Banana Pro, Gemini image generation.
openclaw skills install @cellcog/nano-banana-image-cellcogNano Banana × CellCog. Complex multi-image jobs, executed perfectly, from a single prompt.
Nano Banana is an incredible image model. CellCog makes it do things you can't do by calling it directly — orchestrating 10, 20, even 30 coherent images in one request with consistent characters, planned compositions, and intelligent scene progression. Not single images — complete visual projects.
What CellCog adds on top of Nano Banana:
Reasoning → Scene Planning → Character Design → Image Generation
→ Consistency Verification → Composition Review → Delivery
CellCog's reasoning layer plans scenes before a single pixel is generated — selecting optimal parameters, maintaining character identity across sequences, and orchestrating complex multi-image workflows. This is the difference between "generate an image" and "execute a visual project."
For your first CellCog task in a session, read the cellcog skill for the full SDK reference — file handling, chat modes, timeouts, and more.
OpenClaw (fire-and-forget):
result = client.create_chat(
prompt="[your task prompt]",
notify_session_key="agent:main:main",
task_label="my-task",
chat_mode="agent",
)
All agents except OpenClaw (blocks until done):
from cellcog import CellCogClient
client = CellCogClient(agent_provider="openclaw|cursor|claude-code|codex|...")
result = client.create_chat(
prompt="[your task prompt]",
task_label="my-task",
chat_mode="agent",
)
print(result["message"])
Create stunning images from text descriptions:
Nano Banana excels at maintaining character identity across multiple images — and CellCog's orchestration takes this further by planning entire character arcs:
Blend elements from multiple reference images:
Transform and enhance existing images:
| Aspect | Options |
|---|---|
| Aspect Ratios | 1:1, 16:9, 9:16, 4:3, 3:4, 3:2, 2:3, 21:9 |
| Sizes | 1K (~1024px), 2K (~2048px), 4K (~4096px) |
| Styles | Photorealistic, illustration, watercolor, oil painting, anime, digital art, vector |
| Scenario | Recommended |
|---|---|
| Quick generations and edits | chat_mode="agent" (defaults to the "flash" tier) |
| Large sets with strict brand guidelines | chat_mode="agent", chat_tier="max" |
The flash tier is the default and handles most image work well — fast and economical. Upgrade to chat_tier="max" when quality disappoints or the work is genuinely complex: just re-run the same prompt with chat_tier="max".
Agent Team (chat_mode="team") is reserved for deep research — don't use it for image work.
Be descriptive: "Woman in office" → "Confident woman in her 40s, silver blazer, modern glass-walled office, warm afternoon light"
Specify style: "photorealistic", "digital illustration", "watercolor", "anime"
Describe lighting: "Soft natural light", "dramatic side lighting", "golden hour glow"
For character consistency: Describe the character in detail first, then reference "the same character" in subsequent prompts.
Include composition: "Rule of thirds", "close-up portrait", "wide establishing shot"
Claude Code, Cursor, Codex + 70 more agents: npx skills add cellcog/skills --skill cellcog
OpenClaw: openclaw skills install @cellcog/cellcog
CellCog plugin users: run /cellcog-setup (or /cellcog:cellcog-setup depending on your tool)
Manual setup: pip install -U cellcog and set CELLCOG_API_KEY. See the cellcog skill for SDK reference.