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pixwith-ai-image-generationpixwith ai 图像生成

Agent Skill

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。

总安装

6,928

周安装

283

GitHub Stars

公开资料未说明

下载量

2,241
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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:pixwith-ai-image-generation(pixwith ai 图像生成)
来源仓库:https://github.com/tate-kt/pixwith-ai-image-generation
安装命令:
openclaw skills install pixwith-ai-image-generation
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install pixwith-ai-image-generation

简介

AI视频、图像生成。 40+ models — Sora, Veo 3, Kling, Seedance, GPT Image, Hailuo, WAN.文本到视频、图像到视频、文本到图像、图像到图像。

SKILL.md

name
pixwith-ai
description
AI video, image generation. 40+ models — Sora, Veo 3, Kling, Seedance, GPT Image, Hailuo, WAN. Text-to-video, image-to-video, text-to-image,image-to-image.
homepage
https://pixwith.ai
user-invocable
false
metadata
{"openclaw":{"homepage":"https://pixwith.ai"}}

Pixwith Media Generation

Use This Skill When

Use this skill when the user wants to work with Pixwith through MCP to:

  • generate images from text
  • edit images with one or more reference images
  • generate videos from text
  • generate videos from a reference image
  • inspect available Pixwith models
  • check remaining Pixwith credits

Preconditions

On first install or first use of this skill, explicitly guide the user through setup before attempting any Pixwith tool call.

Use wording like:

"Before this skill can generate media, OpenClaw must be connected to Pixwith through MCP and you need a Pixwith API key.

  • Step 1: Open OpenClaw MCP settings and add the Pixwith MCP server.
  • Step 2: Go to https://pixwith.ai/api and create your Pixwith API key.
  • Step 3: Paste that API key into your Pixwith MCP configuration in OpenClaw.
  • Step 4: After setup is complete, come back here and I can help you list

models, upload images, and create generation tasks."

If the user asks for a copyable MCP configuration, provide a ready-to-paste example using the Pixwith MCP endpoint and remind the user to replace the API key placeholder with their real key:

{
  "mcpServers": {
    "pixwith": {
      "url": "https://api.pixwith.ai/mcp",
      "headers": {
        "Api-Key": "YOUR_PIXWITH_API_KEY"
      }
    }
  }
}

If OpenClaw expects a different MCP configuration shape, adapt the same values to the host format instead of changing the endpoint or auth header.

Do not assume the user understands what MCP is. Describe it as the connection required for OpenClaw to use Pixwith.

If MCP tools are unavailable, tell the user Pixwith is not connected yet and repeat the setup guidance in plain language.

This skill assumes the Pixwith MCP server is already connected in OpenClaw and the following tools are available:

  • list_models
  • get_model_schema
  • upload_image
  • generate
  • get_task_result
  • get_credits

If these tools are unavailable, stop and tell the user the Pixwith MCP server must be configured before this skill can work.

Every request requires a valid Pixwith Api-Key. If authentication fails, send the user to https://pixwith.ai/api.

Core Workflow

1. Discover models

Call list_models first unless the user already provided a known model_id.

This step is mandatory because model availability is dynamic. Do not rely on static model lists in this skill package when choosing what to run.

  • use type="image" for image jobs
  • use type="video" for video jobs
  • keep the returned model_id, name, and summary

2. Inspect the selected model

Always call get_model_schema(model_id) before generate.

Treat input_schema as the source of truth for:

  • whether image_urls are allowed
  • maximum image count
  • valid option keys
  • enum values
  • defaults
  • estimated time
  • credit display

Treat min_credits from list_models only as a quick lower-bound cost hint:

  • it is useful for fast filtering
  • it is not the final price for the specific request
  • for the final price, inspect input_schema.credits

Do not hardcode model parameters when schema data is available.

3. Check credits for expensive jobs

Call get_credits before expensive or long-running jobs, especially video jobs or higher-resolution image jobs.

If credits are insufficient, do not retry automatically. Tell the user to recharge at https://pixwith.ai/pricing.

4. Upload reference images when needed

Call upload_image when the image is local, private, temporary, or otherwise unreliable as a public URL.

Use the returned Pixwith-hosted image_url in generate.image_urls.

5. Create the task

Call generate with:

  • input.prompt
  • input.model_id
  • input.image_urls only when allowed by schema
  • input.options that conform to input_schema.options

Before submission:

  • prompt must not be empty
  • model must come from list_models
  • image count must satisfy schema limits
  • options must be schema-valid

6. Poll until terminal state

Call get_task_result until status becomes terminal.

  • 1: processing
  • 2: completed
  • 3: failed

Recommended polling:

  1. wait about 75% of estimated_time
  2. poll once
  3. if still processing, wait until around estimated_time
  4. poll again
  5. if still processing, poll every 10 seconds

Only expect result_urls when status is 2.

Guardrails

  • Pixwith is asynchronous. Do not present generate as an immediate result.
  • Pixwith tool responses are wrapped. Inspect code before trusting data.
  • Do not guess mode support from the model_id prefix. Pass image_urls only

when the selected schema allows or requires them.

  • Some models are dual-mode under one model_id. For example, Flux MCP IDs

use text-to-image without image_urls and route to Flux Kontext when image_urls is present.

  • Prefer schema-valid retries over speculative retries.
  • Relay backend error messages accurately.

References

  • For authentication, wrapped tool responses, upload semantics, task states, and

failure handling, read references/service-contract.md.

  • For a non-authoritative model snapshot and common parameter patterns, read

references/model-snapshot.md.

Examples

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

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

84.09%
按下载量换算1,884

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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