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Agent Skill · 图像处理

gpt-image-2(GPT Image 2 图像生成)

用于辅助图像生成、图片编辑、视觉素材处理或图像模型工作流。它适合让 Agent 根据文本生成图片、处理背景、整理视觉提示词或调用相关图像工具。使用时需要确认输入图片、版权来源、输出格式和模型限制;涉及人物、品牌、商品或公开展示素材时,应额外核对授权、真实性和内容合规边界。

gpt-image-2 是图像处理类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描已通过,按安装量在图像处理类的 1,562 个 Skill 里排第 16。

总安装 260,736周安装 11,170GitHub Stars 1下载 91,392核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:gpt-image-2(GPT Image 2 图像生成)
来源仓库:https://github.com/agentspace-so/agent-skills
仓库路径:skills/gpt-image-2
安装命令:
npx skills add https://github.com/agentspace-so/agent-skills --skill gpt-image-2
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/agentspace-so/agent-skills --skill gpt-image-2

这个 Skill 是做什么的

图像处理类 Skill 处理生成、裁剪、压缩和批量转换图片的任务。

gpt-image-2 用于辅助图像生成、图片编辑、视觉素材处理或图像模型工作流。适合让 Agent 根据文本生成图片、处理背景、整理视觉提示词或调用相关工具。使用时需要确认输入图片、版权来源、输出格式和模型限制。涉及人物、品牌或公开展示素材时,应额外核对授权、真实性和内容合规边界。

适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/gpt-image-2

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/agentspace-so/agent-skills --skill gpt-image-2;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

gpt-image-2 怎么安装?

在终端执行 npx skills add https://github.com/agentspace-so/agent-skills --skill gpt-image-2。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

gpt-image-2 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

gpt-image-2 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

gpt-image-2 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

gpt-image-2 用的人多吗?

公开数据是累计安装 260,736 次,近一周安装 11,170 次,下载 91,392 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

gpt-image-2 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理图像处理类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

图像处理类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
ai-image-generation

AI 图像生成 · Codex、Claude

4,365,552382调用外部服务未明确有提醒
openai-image-gen

OpenAI 图像生成 · OpenClaw

1,906,88432需要联网MIT-0有提醒
image

图像 · OpenClaw

525,99926需要联网MIT-0通过
baoyu-cover-image

宝玉封面图 · Codex、Claude

474,24016,779只读MIT有提醒
baoyu-image-gen

宝玉图像生成器 · Codex、Claude

468,00716,778接触敏感数据MIT通过
gpt-image-2

GPT Image 2 图像生成 · 本页

260,7361接触敏感数据未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

🪞 GPT Image 2 — Image Generation via Your ChatGPT Subscription

agentspace.so · GitHub

Generate images with GPT Image 2 (ChatGPT Images 2.0) inside your agent, using your existing ChatGPT Plus or Pro subscription — no separate OpenAI access, no Fal or Replicate tokens, no per-image billing.

Text-to-image, image-to-image editing, style transfer, and multi-reference composition. Runs entirely through the local codex CLI you're already logged into.

**Heads up — this skill requires a ChatGPT Plus or Pro subscription *plus* the Codex CLI installed locally. If you have neither, you can use GPT Image 2 in the browser via RunComfy instead — hosted, no ChatGPT subscription or local install needed (RunComfy account required): - Text-to-image: runcomfy.com/models/openai/gpt-image-2/text-to-image - Image edit (i2i):** runcomfy.com/models/openai/gpt-image-2/edit The rest of this document covers the local Codex CLI flow for agents whose user has a ChatGPT subscription.

GPT Image 2 example — flat-color lobster repainted as a 1950s ukiyo-e woodblock print

*Example output: a plain flat-color icon repainted via --ref in ukiyo-e style — composition preserved, rendering swapped, period-appropriate red seal added by the model unprompted.*

When to trigger

Trigger when the user explicitly asks for GPT Image 2 via their ChatGPT subscription, for example:

  • "use GPT Image 2" / "use gpt-image-2" / "use ChatGPT Images 2.0"
  • "use Image 2" / "image 2 this"
  • attached a reference image and asked to remix / edit / restyle it

Do not auto-trigger for a plain "generate an image" request if the user didn't specify this route. If they did specify it, do not silently fall back to HTML mockups, screenshots, or a different image model.

How to invoke

A single bash script handles everything: runs codex exec with the right flags, then decodes the generated image from the persisted session rollout.

Text-to-image:

bash scripts/gen.sh \
  --prompt "<user's raw prompt>" \
  --out <absolute/path/to/output.png>

Image-to-image (reference flag is repeatable for multi-reference composition):

bash scripts/gen.sh \
  --prompt "<user's raw prompt, e.g. 'repaint in watercolor'>" \
  --ref /absolute/path/to/reference.png \
  --out <absolute/path/to/output.png>

Optional: --timeout-sec 300 (default 300).

Default behavior

  • Pass the user's prompt through raw. Do not translate, polish, or add style modifiers unless the user asked for it.
  • Choose the output path. Default to ./image-<YYYYMMDD-HHMMSS>.png in the current working directory if the user didn't specify.
  • Deliver the image. After the script succeeds, display / attach the output file. Do not stop at "done, see path X".
  • Text-heavy layouts are fine. Image 2 handles infographics and timeline prompts well. Do not preemptively warn just because a prompt has a lot of text.

Hard constraints

  • Do not switch routes without permission. If the user said "use GPT Image 2", do not substitute DALL·E, Midjourney, an HTML mockup, or a manual screenshot workflow.
  • Do not rewrite the prompt unless asked.
  • Do not imply this skill works without a local codex login and a valid ChatGPT subscription with image-generation entitlement.

Prerequisites

  1. codex CLI installed — brew install codex or see openai/codex.
  2. Logged in with a ChatGPT plan that includes Image 2 — codex login.
  3. python3 on PATH (ships with macOS; apt install python3 on Linux).

This skill does not grant image-generation capability on its own. It exposes the capability the user already has through their ChatGPT subscription.

Exit codes

codemeaning
0success — output path printed on stdout
2bad args
3codex or python3 CLI missing
4--ref file does not exist
5codex exec failed (auth? network? model?)
6no new session file detected
7imagegen did not produce an image payload (feature not enabled, quota, or capability refused)

On failure, name the layer in one sentence instead of dumping the full stderr at the user.

How it works

The codex CLI reuses the logged-in ChatGPT session and exposes an imagegen tool (gated behind the image_generation feature flag). The script:

  1. snapshots ~/.codex/sessions/ before the run
  2. runs codex exec --enable image_generation --sandbox read-only... (with -i <file> for each reference image)
  3. diffs the sessions directory, then invokes scripts/extract_image.py to scan every new rollout JSONL for a base64 image payload (PNG / JPEG / WebP magic-header match)
  4. decodes the largest matching blob and writes it to --out

Two non-obvious flags other wrappers get wrong on codex-cli 0.111.0+:

  • --enable image_generation is required; the feature is still under-development and off by default.
  • --ephemeral must not be used — ephemeral sessions aren't persisted, so the image payload has nowhere to live.

Data handling

The script is narrowly scoped on purpose:

  • It reads only session rollout files created by its own codex exec invocation. The sessions directory is snapshotted before the call and diffed after, so any prior ~/.codex/sessions/* files (which may contain unrelated Codex conversations) are never touched, read, or transmitted.
  • It writes only two kinds of file: the output PNG at the caller's --out path, and short-lived mktemp logs that are auto-deleted on exit via a trap.
  • No environment variables are read. No credentials are requested. No other paths under ~/.codex/ are accessed.
  • No network calls leave this skill. The only outbound traffic is the one made by the codex CLI itself (to OpenAI, using the user's existing ChatGPT login) — this skill does not add endpoints, telemetry, or callbacks.

What this skill is not

Not a direct OpenAI API client. Not a capability grant — it depends on the user's working Codex CLI login. Not a multi-tenant service (one call per invocation; concurrent calls are serialized by the filesystem-snapshot diff).

适合场景

01

文本生成图片

02

图片风格化

03

产品图和创意图

04

需要 FLUX 模型时

能力概览

能力 1

调用 FLUX 图像模型

能力 2

支持文本生图和图像改写

能力 3

覆盖 LoRA 或风格适配

能力 4

适合创意视觉生成

平台分布

Codex

35.04%
按下载量换算32,024

Claude

28%
按下载量换算25,590

Cursor

18.64%
按下载量换算17,035

Gemini CLI

8.67%
按下载量换算7,924

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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