banana-image

banana-image is a skill for Claude Code, Codex from binary-husky/AlphaAutoResearch. It costs 89 tokens per session (3,536 once invoked), scanned A, original, MIT.

An image-generation skill that creates or edits images from text prompts through a Nano-banana API.

In plain words
What is it for?
Use it to make images, transform supplied images, and request outputs as URLs or encoded image data.
Why use it?
It provides a defined way to request generated visuals or changes to reference images with selected sizes and aspect ratios.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: names the AskUserQuestion tool.

Good fit Use it to make images, transform supplied images, and request outputs as URLs or encoded image data.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/binary-husky/alphaautoresearch/banana_image
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add binary-husky/AlphaAutoResearch --skill banana_image
Clone the repo
git clone --depth 1 https://github.com/binary-husky/AlphaAutoResearch

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for banana-image

README.md
[![agentmods](https://agentmods.dev/badge/skills/binary-husky/alphaautoresearch/banana_image/github.svg)](https://agentmods.dev/skills/binary-husky/alphaautoresearch/banana_image)
Your own site
<a href="https://agentmods.dev/skills/binary-husky/alphaautoresearch/banana_image"><img src="https://agentmods.dev/badge/skills/binary-husky/alphaautoresearch/banana_image/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for banana-image

Your own site · 80×15
<a href="https://agentmods.dev/skills/binary-husky/alphaautoresearch/banana_image"><img src="https://agentmods.dev/badge/skills/binary-husky/alphaautoresearch/banana_image.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 89 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,536 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00089 $0.03536
Opus 5.5 $0.00036 $0.01414
Sonnet 5.5 $0.00018 $0.00707
Haiku 4.5 $0.00009 $0.00354

Measured 2mo ago against content hash bd7d2070e113, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

banana-image scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -X POST ${image_bed_upload_url} \
alpha_auto_research/skills/banana_image/SKILL.md · 395 lines

How it starts

The opening of the file, as written. The whole thing — 395 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Banana Image Generation Skill

密钥

从 research_config.jsonc 的 banana_image 字段读取所有 ${...} 变量。


生成图像

API 概览

  • 格式:OpenAI DALL-E 兼容
  • 模型:Nano-banana-3.1-Flash(Generations,推荐)
  • 请求方式:POST ${image_gen_url}/v1/images/generations

请求参数

Header
参数 类型 必需 说明
Authorization string 否 默认值:Bearer ${image_generation_api_key}
Body(application/json)
参数 类型 必需 说明
model string 是 模型名称,如 gemini-3.1-flash-image-preview
prompt string 是 图像描述提示词
aspect_ratio enum 否 可选值:4:3 3:4 16:9 9:16 2:3 3:2 1:1 4:5 5:4 21:9 1:4 4:1 8:1 1:8
response_format string 否 url 或 b64_json
image array[string] 否 参考图数组(url 或 b64_json)
image_size enum 否 仅 nano-banana-2 支持,可选值:512 1K 2K 4K

请求示例

import http.client
import json

conn = http.client.HTTPSConnection("${image_gen_url}'s host")  # read from research_config.jsonc -> banana_image.image_gen_url
payload = json.dumps({
   "prompt": "cat",  # include image url here if you want to edit one image
   "model": "gemini-3.1-flash-image-preview"
})
headers = {
   'Authorization': 'Bearer ${image_generation_api_key}',  # read from research_config.jsonc -> banana_image.image_generation_api_key
   'Content-Type': 'application/json'
}
conn.request("POST", "/v1/images/generations", payload, headers)
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))

返回响应

  • 200:成功,返回 application/json 对象

Prompting 指南

Interactive prompt crafting for Nano Banana Pro image generation. This skill guides users through a structured process to create effective prompts by clarifying intent and applying proven techniques.

Step 1: Gather Reference Materials

Before asking questions, check if the user has provided:

  • Reference images - Photos to use for character consistency, style, or composition
  • Existing prompts - Previous attempts to improve upon
  • Visual references - Screenshots or examples of desired output

Read the full file on GitHub · 395 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2mo ago First seen · 395 lines · 89 tokens per session scan A bd7d2070e113

Subscribe to this mod's changes

banana-image is a skill published in the GitHub repository binary-husky/AlphaAutoResearch (11 stars, last pushed 5mo ago), licensed MIT. It adds 89 tokens to every session and 3,536 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens