nano-banana-2-skill: AI image generation CLI powered by Gemini 3 Pro

by kingbootoshi · Agent Tool · ★ 417

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install nano-banana-2-skill

git clone https://github.com/kingbootoshi/nano-banana-2-skill.git ~/tools/nano-banana-2

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 75/100

Flagged: sudo usage. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

Quick Facts

Stars417
Forks48
LanguageTypeScript
CategoryAgent Tool
LicenseMIT
Quality Score75/100
Open Issues4
Last Updated2026-04-02
Created2026-02-24
Platformsclaude-code, cli, gemini, node
Est. Tokens~5k

About nano-banana-2-skill

Nano Banana 2 Skill AI image generation CLI powered by Gemini 3.1 Flash Image Preview (default) with support for Gemini 3 Pro and any Gemini model. Multi-resolution (512-4K), aspect ratios, cost tracking, broadcast-grade green screen transparency, reference images, and style transfer. Also ships as a Claude Code skill for AI-assisted image generation workflows.

Compatible Skills

These tools work well together with nano-banana-2-skill for enhanced workflows:

  • claude-art-skill — semantic(0.26)+complementary+shared_fw(anthropic)+same_lang+similar_pop+shared_platform (67%)
  • nano-banana-pro-prompts-recommend-skill — semantic(0.26)+complementary+same_lang+similar_pop+shared_platform (59%)
  • codex-image — semantic(0.31)+complementary+shared_fw(anthropic)+similar_pop+shared_platform (59%)
  • BeatDesign — semantic(0.24)+complementary+same_lang+similar_pop+shared_platform (59%)
  • mediagen — semantic(0.24)+complementary+same_lang+similar_pop+shared_platform (58%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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