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Agent Skill · 图像处理

image-gen(图像生成器)

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。

image-gen 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,权限为执行命令;许可证信息还不明确,第三方扫描有提醒项,按安装量在图像处理类的 1,562 个 Skill 里排第 210。

总安装 16,338周安装 674GitHub Stars 49下载 5,338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:image-gen(图像生成器)
来源仓库:https://github.com/marswaveai/skills
仓库路径:skills/image-gen
安装命令:
npx skills add https://github.com/marswaveai/skills --skill image-gen
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/marswaveai/skills --skill image-gen

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。

image-gen 用于辅助图像生成、图片编辑和视觉素材处理。适合根据文本生成图片、处理背景或整理视觉提示词。使用时需确认输入图片、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,应核对授权和合规边界。image-gen 属于图像处理类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/image-gen

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/marswaveai/skills --skill image-gen;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

    它需要执行命令,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

image-gen 怎么安装?

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

image-gen 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。

image-gen 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/marswaveai/skills --skill image-gen 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

image-gen 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。

image-gen 用的人多吗?

公开数据是累计安装 16,338 次,近一周安装 674 次,下载 5,338 次,GitHub 49 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。

image-gen 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。

先想清楚的地方

  • 需要执行命令,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。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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

  • 不能接受 Skill 在你机器上执行命令的人。
  • 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
image-gen

图像生成器 · 本页

16,33849执行命令未明确有提醒

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

When to Use

  • User wants to generate an AI image from a text description
  • User says "generate image", "draw", "create picture", "配图"
  • User says "生成图片", "画一张", "AI图"
  • User needs a cover image, illustration, or concept art

When NOT to Use

  • User wants to create audio content (use /podcast, /speech)
  • User wants to create a video (use /explainer)
  • User wants to edit an existing image (not supported)
  • User wants to extract content from a URL (use /content-parser)

Purpose

Generate AI images using the ListenHub CLI. Supports text prompts with optional reference images (local files or URLs), multiple resolutions, and aspect ratios. Images are saved as local files.

Hard Constraints

  • Always check CLI auth following shared/cli-authentication.md
  • Follow shared/cli-patterns.md for command execution and error handling
  • Always read config following shared/config-pattern.md before any interaction
  • Output saved to .listenhub/image-gen/YYYY-MM-DD-{jobId}/ — never ~/Downloads/

Step -1: CLI Auth Check

Follow shared/cli-authentication.md § Auth Check. If CLI is not installed or not logged in, auto-install and auto-login — never ask the user to run commands manually.

Step 0: Config Setup

Follow shared/config-pattern.md Step 0 (Zero-Question Boot).

If file doesn't exist — silently create with defaults and proceed:

mkdir -p ".listenhub/image-gen"
echo '{"outputDir":".listenhub","outputMode":"inline"}' > ".listenhub/image-gen/config.json"
CONFIG_PATH=".listenhub/image-gen/config.json"
CONFIG=$(cat "$CONFIG_PATH")

Do NOT ask any setup questions. Proceed directly to the Interaction Flow.

If file exists — read config silently and proceed:

CONFIG_PATH=".listenhub/image-gen/config.json"
[ ! -f "$CONFIG_PATH" ] && CONFIG_PATH="$HOME/.listenhub/image-gen/config.json"
CONFIG=$(cat "$CONFIG_PATH")

Setup Flow (user-initiated reconfigure only)

Only run when the user explicitly asks to reconfigure. Display current settings:

当前配置 (image-gen):
  输出方式:{inline / download / both}

Then ask:

  1. outputMode: Follow shared/output-mode.md § Setup Flow Question.

Save immediately:

NEW_CONFIG=$(echo "$CONFIG" | jq --arg m "$OUTPUT_MODE" '. + {"outputMode": $m}')
echo "$NEW_CONFIG" > "$CONFIG_PATH"
CONFIG=$(cat "$CONFIG_PATH")

Interaction Flow

Step 1: Image Description

Free text input. Ask the user:

Describe the image you want to generate.

If the prompt is very short (< 10 words) and the user hasn't asked for verbatim generation, offer to help enrich the prompt. Otherwise, use as-is.

Step 2: Model

Ask:

Question: "Which model?"
Options:
  - "pro (recommended)" — gemini-3-pro-image-preview, higher quality
  - "flash" — gemini-3.1-flash-image-preview, faster and cheaper, unlocks extreme aspect ratios (1:4, 4:1, 1:8, 8:1)

Step 3: Resolution and Aspect Ratio

Ask both together (independent parameters):

Question: "What resolution?"
Options:
  - "1K" — Standard quality
  - "2K (recommended)" — High quality, good balance
  - "4K" — Ultra high quality, slower generation
Question: "What aspect ratio?"
Options (all models):
  - "16:9" — Landscape, widescreen
  - "1:1" — Square
  - "9:16" — Portrait, phone screen
  - "Other" — 2:3, 3:2, 3:4, 4:3, 21:9

If flash model was selected, also offer: 1:4 (narrow portrait), 4:1 (wide landscape), 1:8 (extreme portrait), 8:1 (panoramic)

Step 4: Reference Images (optional)

Question: "Any reference images for style guidance?"
Options:
  - "Yes" — Provide file paths or URLs
  - "No references" — Generate from prompt only

If yes: Collect reference image paths or URLs (comma-separated). The CLI handles both local files and URLs natively — no need to distinguish between them.

  • Max 5 references
  • Supported formats: jpg, png, webp, gif
  • Max 10MB per file

Each reference will be passed as a --reference flag to the CLI.

Step 5: Confirm & Generate

Summarize all choices:

Ready to generate image:

  Prompt: {prompt text}
  Model: {pro / flash}
  Resolution: {1K / 2K / 4K}
  Aspect ratio: {ratio}
  References: {yes — N image(s) / no}

  Proceed?

Wait for explicit confirmation before running the CLI command.

Workflow

  1. Build CLI command: Construct the listenhub image create command with all collected parameters.
  2. Execute: Run the command with run_in_background: true and timeout: 180000: listenhub image create \ --prompt "{description}" \ --model "{model}" \ --lang "{lang}" \ --aspect-ratio {16:9|9:16|1:1} \ --size {1K|2K|4K} \ --json If reference images were provided, add --reference for each: listenhub image create \ --prompt "{description}" \ --model "{model}" \ --lang "{lang}" \ --aspect-ratio 16:9 \ --size 2K \ --reference./sketch.png \ --reference./photo.jpg \ --json The --lang flag provides a language hint for the prompt. Detect from the user's prompt language (e.g., Chinese prompt → zh, English prompt → en).
  3. Parse result and present Read OUTPUT_MODE from config. Follow shared/output-mode.md for behavior. Parse the CLI JSON output to extract the image URL: IMAGE_URL=$(echo "$RESULT" | jq -r '.imageUrl') inline or both: Download to a temp file, then use the Read tool. JOB_ID=$(date +%s) listenhub download "$IMAGE_URL" -o /tmp/image-gen-${JOB_ID}.jpg Then use the Read tool on /tmp/image-gen-{jobId}.jpg. The image displays inline in the conversation. Present: 图片已生成! download or both: Save to the artifact directory. JOB_ID=$(date +%s) DATE=$(date +%Y-%m-%d) JOB_DIR=".listenhub/image-gen/${DATE}-${JOB_ID}" mkdir -p "$JOB_DIR" listenhub download "$IMAGE_URL" -o "${JOB_DIR}/${JOB_ID}.jpg" Present: 图片已生成! 已保存到.listenhub/image-gen/{YYYY-MM-DD}-{jobId}/: {jobId}.jpg

Prompt Handling

Default: Pass the user's prompt directly without modification.

When to offer optimization:

  • Prompt is very short (a few words) AND user hasn't requested verbatim
  • Ask: "Would you like help enriching the prompt with style/lighting/composition details?"

When to never modify:

  • Long, detailed, or structured prompts — treat the user as experienced
  • User says "use this prompt exactly"

Optimization techniques (if user agrees):

  • Style: "cyberpunk" → add "neon lights, futuristic, dystopian"
  • Scene: time of day, lighting, weather
  • Quality: "highly detailed", "8K quality", "cinematic composition"
  • Always use English keywords (models trained on English)
  • Show optimized prompt before submitting

API Reference

  • CLI authentication: shared/cli-authentication.md
  • CLI execution patterns: shared/cli-patterns.md
  • Config pattern: shared/config-pattern.md
  • Output mode: shared/output-mode.md

Composability

  • Invokes: nothing (direct CLI call)
  • Invoked by: platform skills for cover images (Phase 2)

Example

User: "Generate an image: cyberpunk city at night"

Agent workflow:

  1. Prompt is short → offer enrichment → user declines
  2. Ask model → "pro"
  3. Ask resolution → "2K"
  4. Ask ratio → "16:9"
  5. No references
listenhub image create \
  --prompt "cyberpunk city at night" \
  --model "gemini-3-pro-image-preview" \
  --lang en \
  --aspect-ratio 16:9 \
  --size 2K \
  --json

Parse CLI JSON output per outputMode (see shared/output-mode.md).

Example 2 — With Reference Images

User: "Generate an image in this style" (provides local files and a URL)

Agent workflow:

  1. Ask prompt → "a serene mountain lake at dawn"
  2. Ask model → "pro"
  3. Ask resolution → "2K"
  4. Ask ratio → "16:9"
  5. References → /path/to/style-reference.png, https://example.com/photo.jpg
listenhub image create \
  --prompt "a serene mountain lake at dawn" \
  --model "gemini-3-pro-image-preview" \
  --lang en \
  --aspect-ratio 16:9 \
  --size 2K \
  --reference /path/to/style-reference.png \
  --reference https://example.com/photo.jpg \
  --json

Parse CLI JSON output per outputMode (see shared/output-mode.md).

平台分布

Codex

33.01%
按下载量换算1,762

Claude

31.97%
按下载量换算1,707

Cursor

19.27%
按下载量换算1,029

Gemini CLI

9.72%
按下载量换算519

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

可疑

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/marswaveai/skills --skill image-gen 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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