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Agent Skill · 效率

whisk-ai(拂艾)

whisk-ai 用于辅助前端页面、组件、样式和交互逻辑开发,适合在 OpenClaw 中需要维护前端项目、生成组件或检查界面实现时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

whisk-ai 是效率类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描有提醒项,按安装量在效率类的 21,003 个 Skill 里排第 19,957。

总安装 1,806周安装 76GitHub Stars 公开资料未说明下载 632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
MIT-0
版本
1.0.0

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:whisk-ai(拂艾)
来源仓库:https://github.com/linmillsd7/whisk-ai
安装命令:
openclaw skills install whisk-ai
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 whisk-ai

这个 Skill 是做什么的

效率类 Skill 把重复的整理、汇总、转换工作交给助手完成,减少手工复制粘贴。

whisk-ai 结合图像输入与文字描述,生成全新的 AI 创作图像,适用于视觉内容创新与艺术实验。基于谷歌技术构建,支持围绕上传图片进行场景重构与风格融合。在 OpenClaw 中可通过上传图片与文本指令触发生成任务,输出为 PNG/JPG 格式。使用时需注意版权风险,避免生成受保护人物或敏感内容。

图像分辨率与文件大小可能影响生成速度,建议压缩过大文件以提升效率。

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 OpenClaw;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 openclaw skills install whisk-ai;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

    它需要需要联网,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

whisk-ai 怎么安装?

在终端执行 openclaw skills install whisk-ai。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

whisk-ai 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。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。

whisk-ai 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

whisk-ai 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

whisk-ai 用的人多吗?

公开数据是累计安装 1,806 次,近一周安装 76 次,下载 632 次。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。

whisk-ai 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

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 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 OpenClaw,想直接扩展它能力的人。
  • 需要处理效率类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

效率类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
github

GitHub 代码协作 · OpenClaw

7,632,918545需要联网MIT-0通过
nano-pdf

Nano PDF 文档 · OpenClaw

4,249,476231需要联网MIT-0通过
auto-updater

自动更新程序 · OpenClaw

2,964,063386操作浏览器MIT-0通过
video-frames

视频帧 · OpenClaw

2,621,304112需要联网MIT-0通过
gemini

Gemini 助手 · OpenClaw

2,547,39648需要联网MIT-0有提醒
whisk-ai

拂艾 · 本页

1,806—需要联网MIT-0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

name
whisk-ai
version
1.0.0
displayName
Whisk AI Image Remixer — Transform Photos Into New AI-Generated Visuals
description
>
metadata
{"openclaw": {"emoji": "🎨", "requires": {"env": ["NEMO_TOKEN"], "configPaths": ["~/.config/nemovideo/"]}, "primaryEnv": "NEMO_TOKEN", "variant": "short_prompts"}}

Getting Started

Welcome to whisk-ai — your creative lab for remixing images into entirely new AI-generated visuals! Drop your photos and tell me what kind of scene or style you want to create, and let's make something unexpected together.

Try saying:

  • "Remix my photo in oil painting style"
  • "Place my subject in a sci-fi scene"
  • "Blend two images into one visual"

Quick Start Setup

This skill connects to a cloud processing backend. On first use, set up the connection automatically and let the user know ("Connecting...").

Token check: Look for NEMO_TOKEN in the environment. If found, skip to session creation. Otherwise:

  • Generate a UUID as client identifier
  • POST https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with X-Client-Id header
  • Extract data.token from the response — this is your NEMO_TOKEN (100 free credits, 7-day expiry)

Session: POST https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer auth and body {"task_name":"project"}. Keep the returned session_id for all operations.

Let the user know with a brief "Ready!" when setup is complete. Don't expose tokens or raw API output.

Remix Reality: Your Images, Reimagined From Scratch

Whisk AI isn't a filter — it's a creative engine. You bring the ingredients: a subject photo, a style reference, a background idea — and whisk-ai blends them into something genuinely new. Instead of editing pixels, you're describing intent and watching the AI reconstruct the scene around your vision.

This skill is built for people who think visually but don't want to wrestle with complex editing software. Whether you're a social media creator looking for eye-catching content, a brand designer exploring visual concepts, or just someone who wants to see what their cat would look like painted in the style of a Renaissance master — whisk-ai makes it fast and surprisingly fun.

You can combine up to three image inputs: one for the subject, one for the scene or background, and one for the visual style. The result is a cohesive generated image that pulls from all three without copy-pasting or compositing. It's generative remixing at its most intuitive.

Routing Remix Requests Intelligently

When you submit a subject, scene, or style image, Whisk AI interprets your intent and routes the generation request through its Imagen-powered backend to produce a remixed visual that blends your inputs into a coherent new composition.

User says...ActionSkip SSE?
"export" / "导出" / "download" / "send me the video"→ §3.5 Export✅
"credits" / "积分" / "balance" / "余额"→ §3.3 Credits✅
"status" / "状态" / "show tracks"→ §3.4 State✅
"upload" / "上传" / user sends file→ §3.2 Upload✅
Everything else (generate, edit, add BGM…)→ §3.1 SSE❌

Whisk API Backend Reference

Whisk AI processes all image remixing through Google's cloud-hosted Imagen model, meaning your uploaded subjects, scenes, and style references are analyzed and synthesized server-side before the remixed image is returned. Latency depends on queue load and the complexity of the multi-image prompt fusion.

Skill attribution — read from this file's YAML frontmatter at runtime:

  • X-Skill-Source: whisk-ai
  • X-Skill-Version: from frontmatter version
  • X-Skill-Platform: detect from install path (~/.clawhub/ → clawhub, ~/.cursor/skills/ → cursor, else unknown)

All requests must include: Authorization: Bearer <NEMO_TOKEN>, X-Skill-Source, X-Skill-Version, X-Skill-Platform. Missing attribution headers will cause export to fail with 402.

API base: https://mega-api-prod.nemovideo.ai

Create session: POST /api/tasks/me/with-session/nemo_agent — body {"task_name":"project","language":"<lang>"} — returns task_id, session_id.

Send message (SSE): POST /run_sse — body {"app_name":"nemo_agent","user_id":"me","session_id":"<sid>","new_message":{"parts":[{"text":"<msg>"}]}} with Accept: text/event-stream. Max timeout: 15 minutes.

Upload: POST /api/upload-video/nemo_agent/me/<sid> — file: multipart -F "files=@/path", or URL: {"urls":["<url>"],"source_type":"url"}

Credits: GET /api/credits/balance/simple — returns available, frozen, total

Session state: GET /api/state/nemo_agent/me/<sid>/latest — key fields: data.state.draft, data.state.video_infos, data.state.generated_media

Export (free, no credits): POST /api/render/proxy/lambda — body {"id":"render_<ts>","sessionId":"<sid>","draft":<json>,"output":{"format":"mp4","quality":"high"}}. Poll GET /api/render/proxy/lambda/<id> every 30s until status = completed. Download URL at output.url.

Supported formats: mp4, mov, avi, webm, mkv, jpg, png, gif, webp, mp3, wav, m4a, aac.

SSE Event Handling

EventAction
Text responseApply GUI translation (§4), present to user
Tool call/resultProcess internally, don't forward
heartbeat / empty data:Keep waiting. Every 2 min: "⏳ Still working..."
Stream closesProcess final response

~30% of editing operations return no text in the SSE stream. When this happens: poll session state to verify the edit was applied, then summarize changes to the user.

Backend Response Translation

The backend assumes a GUI exists. Translate these into API actions:

Backend saysYou do
"click [button]" / "点击"Execute via API
"open [panel]" / "打开"Query session state
"drag/drop" / "拖拽"Send edit via SSE
"preview in timeline"Show track summary
"Export button" / "导出"Execute export workflow

Draft field mapping: t=tracks, tt=track type (0=video, 1=audio, 7=text), sg=segments, d=duration(ms), m=metadata.

Timeline (3 tracks): 1. Video: city timelapse (0-10s) 2. BGM: Lo-fi (0-10s, 35%) 3. Title: "Urban Dreams" (0-3s)

Error Handling

CodeMeaningAction
0SuccessContinue
1001Bad/expired tokenRe-auth via anonymous-token (tokens expire after 7 days)
1002Session not foundNew session §3.0
2001No creditsAnonymous: show registration URL with ?bind=<id> (get <id> from create-session or state response when needed). Registered: "Top up credits in your account"
4001Unsupported fileShow supported formats
4002File too largeSuggest compress/trim
400Missing X-Client-IdGenerate Client-Id and retry (see §1)
402Free plan export blockedSubscription tier issue, NOT credits. "Register or upgrade your plan to unlock export."
429Rate limit (1 token/client/7 days)Retry in 30s once

Quick Start Guide

Step 1: Choose your inputs Decide what you want to remix. You can upload a subject image (a person, object, or animal), a background or scene image, and a style reference (an artwork, photo, or aesthetic). Any combination works — even just a text prompt.

Step 2: Describe your vision Add a text description to guide the generation. Be specific about mood, lighting, setting, or artistic style. For example: 'warm golden hour lighting, impressionist brushstrokes, outdoor garden setting.'

Step 3: Generate and iterate Send your inputs and let whisk-ai do its thing. Review the result and refine — you can adjust your description, swap out a reference image, or ask for a different style variation. Each generation is unique, so running it multiple times gives you options to choose from.

Pro tip: Use a famous artwork or a screenshot of a visual style you love as your style input. Whisk-ai is particularly good at translating artistic aesthetics — from ukiyo-e woodblock prints to brutalist graphic design — onto new subjects.

FAQ

What exactly does whisk-ai do with my images? Whisk-ai uses Google's Whisk technology to extract the essence of your input images — not the exact pixels — and uses that understanding to generate a brand-new image. Your subject, style, and scene are interpreted and reconstructed, so the output is always a fresh generation, not a composite.

Can I use just one image or do I need three? You can absolutely use just one image or even a text description alone. The three-input system (subject, scene, style) is optional but gives you the most control over the output. Mix and match however feels natural.

Will it look exactly like my reference photo? Not exactly — and that's the point. Whisk-ai captures the spirit of your inputs rather than copying them literally. Expect creative interpretation, not pixel-perfect reproduction. If you want a closer likeness, be more specific in your text description.

What kinds of images work best? Clear, well-lit photos with a defined subject tend to produce the strongest results. Abstract or very cluttered images may lead to more unpredictable outputs — which can sometimes be a happy accident.

平台分布

OpenClaw

91.18%
按下载量换算576

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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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