Nano banana

Skill tuoxie2046/claude-code-research-skills/skills/nano-banana

A curated collection of Claude Code skills & plugins for academic research paper workflows — figures, writing, polishing, peer-review simulation, multi-source literature search (MCP), and full research→write→review pipelines.

Install

npx -y skills add tuoxie2046/claude-code-research-skills --skill nano-banana

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Generate images from the terminal with Google's nano-banana (Gemini image model) via the Antigravity CLI (`agy`), billed to the signed-in Google account. Use whenever the user asks to create / draw / generate an image, picture, icon, logo, illustration, poster, infographic, or diagram specifically with nano-banana / Gemini / Antigravity — or wants rich, text-heavy infographics. For the OpenAI gpt-image-2 path use the separate `gpt-image` skill instead.

SKILL.md

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nano-banana — image generation via Antigravity (`agy`)

Wraps the agy (Google Antigravity) agent so Claude Code can generate images with nano-banana. Unlike a plain CLI, image generation here is an agent tool (generate_image) invoked through natural language — the wrapper builds a strict instruction, runs agy -p, then verifies the output file actually landed (with recovery from agy's brain/ working dir). Auth is the Google account signed into agy.

All commands go through the helper:

~/.claude/skills/nano-banana/scripts/nano-banana.sh <check|generate|batch> [args]

Workflow

Preflight once per session: nano-banana.sh check. If it reports no creds / auth, the user must run agy once interactively to log in (browser OAuth) — you cannot do this for them. Token expiry surfaces at generate time as an auth error.
Generate with an explicit --output PNG path. The helper prints IMAGE\t<path>\t<WxH> on success.
Always Read the produced PNG to view it and verify, then report the path.
Iterate by re-running with a refined prompt.

Commands

Generate (text → PNG):

~/.claude/skills/nano-banana/scripts/nano-banana.sh generate \
  --prompt "PROMPT TEXT" --output ./out.png

Batch (many prompts → many images; one per prompt, continue-on-error, manifest):

# from a file (one prompt per line; blank lines and # comments skipped)
~/.claude/skills/nano-banana/scripts/nano-banana.sh batch \
  --prompts-file ./prompts.txt --outdir ./renders
# inline / stdin
~/.claude/skills/nano-banana/scripts/nano-banana.sh batch --prompt "a" --prompt "b" --outdir ./out
printf 'one\ntwo\n' | ~/.claude/skills/nano-banana/scripts/nano-banana.sh batch --prompts-file -

Name an output by prefixing a line with stem<TAB>prompt (else 001.png, …). Default --outdir is ./nb-batch-<timestamp>/; read <outdir>/manifest.tsv for the stem→status→path→prompt map. Per-image failures are recorded and skipped, but an auth failure aborts the whole batch (no point retrying every prompt against dead auth) — re-login with agy and re-run.

How it works / behaviors

Underlying tool is generate_image (nano-banana is the model). There is no agy image subcommand — the agent calls the tool from the prompt.
The agent generates a JPEG into ~/.gemini/antigravity-cli/brain/<conv-id>/, then converts to PNG at your --output. The wrapper forces an absolute path and, if the agent doesn't save there, recovers the newest brain image and converts it via sips.
Output paths are absolutized (agy resets cwd). Parent dirs are created.
One retry on a no-file result; auth/eligibility errors fail fast with guidance.
Non-destructive: success requires the output file to actually change this run (checked via its inode:mtime:size), so a pre-existing/stale file is never reported as a fresh result and is not deleted if generation fails.

⚠️ Safety

This path drives an autonomous agent (agy -p … --dangerously-skip-permissions): tool calls and file writes are auto-approved. A prompt is therefore not just image text — a malicious or careless prompt could instruct the agent to run other tools or touch other files. Only pass trusted prompt text (and trusted --prompts-file contents). For the non-agent, capability-scoped path, prefer the gpt-image skill.

Strengths & limitations (vs gpt-image-2)

Strengths: excellent in-image text rendering; tends to produce rich, fully fleshed-out infographics (adds detail, gradients, sub-labels). Great for posters and content-heavy diagrams; strong at consistent image editing (via prompt + a file path).
Limitations:
- No precise size control through the agent — it returns its own dimensions (often ~16:9). If you need an exact size, post-process with sips -z H W.
- Slower than gpt-image-2 (it's an agent turn: tool call + format convert).
- Tends to elaborate — if you want strictly minimal output, say "minimal, no extra text, exactly three boxes" etc.
- Needs a live agy login; tokens expire and require interactive re-auth.

Troubleshooting

agy ... not found → install Antigravity (https://antigravity.google) or ensure ~/.local/bin is on PATH.
auth / IneligibleTier / "Authentication required" → run agy once interactively to log in; the standalone gemini CLI is not usable on free-tier accounts.
No file produced → check stderr for the agy transcript; re-run (one retry is automatic).