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Agent Skill · 开发

varg-ai(瓦格艾)

varg-ai 用于处理图像、截图、视觉识别或图片素材相关工作,适合在 OpenClaw 中需要让 Agent 分析图片、整理视觉素材或辅助图像流程时使用。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。

varg-ai 是开发类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描有提醒项,按安装量在开发类的 21,746 个 Skill 里排第 3,998。

总安装 9,384周安装 391GitHub Stars 公开资料未说明下载 3,128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
2.0.6

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:varg-ai(瓦格艾)
来源仓库:https://github.com/securityqq/varg-ai
安装命令:
openclaw skills install varg-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 varg-ai

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

varg-ai 用于生成 AI 视频、图像、语音和音乐,适合多媒体内容创作任务。适用于制作动画角色、产品展示、幻灯片或社交媒体视频等内容。通过 clawhub 安装并使用 openclaw skills install varg-ai 命令部署。安装前应确认是否依赖外部 AI 服务接口,以及是否有网络访问权限。

建议结合原始 README 了解输出格式、分辨率及版权使用限制。

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 varg-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

varg-ai 怎么安装?

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

varg-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。

varg-ai 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。

varg-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。

varg-ai 用的人多吗?

公开数据是累计安装 9,384 次,近一周安装 391 次,下载 3,128 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。

varg-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

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 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。
  • 需要处理开发类任务、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。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
varg-ai

瓦格艾 · 本页

9,384—接触敏感数据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
varg-ai
description
>-
Triggers
create a video", "generate video", "make a slideshow",
license
MIT
metadata
author
vargHQ
version
2.0.4
openclaw
requires
env
anyBins
primaryEnv
VARG_API_KEY
homepage
https://varg.ai
compatibility
>-
allowed-tools
Bash(bun:*) Bash(bunx:*) Bash(npx:*) Bash(curl:*) Bash(mkdir:*) Bash(echo:*) Bash(chmod:*) Read Write Edit

Version Check (run once per session)

This skill is version 2.0.4. Before first use each session, check for updates:

curl -sf https://raw.githubusercontent.com/vargHQ/skills/main/varg-ai/SKILL.md | grep 'version:' | head -1

If the remote version is newer than 2.0.4, update and re-read this file:

npx -y skills update

Setup (REQUIRED before first use)

Before generating anything, check authentication and determine rendering mode. Do NOT run interactive CLI tools (like bunx vargai init) -- they require stdin which agents cannot provide. Use the curl-based flow below instead.

1. Check for API key

echo "${VARG_API_KEY:-}" && cat ~/.varg/credentials 2>/dev/null

If VARG_API_KEY is set (via env or credentials file), skip to step 2.

If neither exists, authenticate the user. Try Option A first, fall back to Option B.

Option A: User already has an API key

Ask the user if they have a VARG_API_KEY. If yes, tell them to export it in their terminal:

export VARG_API_KEY=<their_key>

Important: Do NOT ask the user to paste the raw key to you. Ask them to run the export command themselves. Then skip to "Save credentials" below.

Option B: Sign up / sign in via email (OTP)

  1. Ask the user for their email address.
  2. Send a one-time code to their email:
curl -s -X POST https://app.varg.ai/api/auth/cli/send-otp \
  -H "Content-Type: application/json" \
  -d '{"email":"USER_EMAIL"}'
  1. Tell the user: "Check your inbox for a 6-digit verification code from varg.ai"
  2. Ask the user for the code, then verify and capture the response in one step:
VARG_AUTH=$(curl -s -X POST https://app.varg.ai/api/auth/cli/verify-otp \
  -H "Content-Type: application/json" \
  -d '{"email":"USER_EMAIL","code":"THE_6_DIGIT_CODE"}')
export VARG_API_KEY=$(echo "$VARG_AUTH" | grep -o '"api_key":"[^"]*"' | cut -d'"' -f4)
echo "Authenticated. Balance: $(echo "$VARG_AUTH" | grep -o '"balance_cents":[0-9]*' | cut -d: -f2) credits"

The response contains {"api_key":"varg_xxx","email":"...","balance_cents":0,"access_token":"..."}. The key is now in $VARG_API_KEY -- never reference the raw value directly.

Save credentials

Once VARG_API_KEY is set (from either option), save it globally and verify. Always reference $VARG_API_KEY -- never the raw value:

mkdir -p ~/.varg && echo "{\"api_key\":\"$VARG_API_KEY\",\"email\":\"USER_EMAIL\",\"created_at\":\"$(date -u +%Y-%m-%dT%H:%M:%SZ)\"}" > ~/.varg/credentials && chmod 600 ~/.varg/credentials

Verify the key works:

curl -s -H "Authorization: Bearer $VARG_API_KEY" https://api.varg.ai/v1/balance

You should get {"balance_cents": ...}. If you get 401, the key is invalid -- ask the user to double-check it.

Also add to the project .env if one exists:

echo "VARG_API_KEY=$VARG_API_KEY" >> .env

Check balance and add credits

Check balance_cents from the verify-otp response or the balance check above. If balance is 0 (or too low for the user's task), the user needs credits before generating anything. 1 credit = 1 cent. A typical video costs $2-5 (200-500 credits).

Available packages:

Package IDCreditsPrice
credits-20002,000$20
credits-50005,000$50
credits-1000010,000 (recommended)$100
credits-2000020,000$200
credits-5000050,000$500
credits-100000100,000$1,000

Ask the user which package they'd like, then:

  • If you have the access_token (from Option B email OTP), capture it and create a Stripe checkout session:
VARG_ACCESS_TOKEN=$(echo "$VARG_AUTH" | grep -o '"access_token":"[^"]*"' | cut -d'"' -f4)
curl -s -X POST https://app.varg.ai/api/billing/checkout \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $VARG_ACCESS_TOKEN" \
  -H "Origin: https://app.varg.ai" \
  -d '{"packageId":"PACKAGE_ID"}'

Response: {"url":"https://checkout.stripe.com/..."}

Tell the user to open that URL in their browser to complete payment. Credits are added immediately after payment.

  • If you only have the API key (from Option A), direct the user to https://app.varg.ai/dashboard to purchase credits manually.

2. Determine rendering mode

bunffmpegMode
NoNoCloud Render -- read cloud-render.md
YesNoCloud Render -- read cloud-render.md
YesYesLocal Render (recommended) -- read local-render.md

Critical Rules

Everything you know about varg is likely outdated. Always verify against this skill and its references before writing code.

  1. Never guess model IDs -- consult models.md for current models, pricing, and constraints.
  2. Function calls for media, JSX for composition -- Image({...}) creates media, <Clip> composes timeline. Never write <Image prompt="..." />.
  3. Cache is sacred -- identical prompt + params = instant $0 cache hit. When iterating, keep unchanged prompts EXACTLY the same. Never clear cache.
  4. One image per Video -- Video({ prompt: { images: [img] } }) takes exactly one image. Multiple images cause errors.
  5. Duration constraints differ by model -- kling-v3: 3-15s (integer only). kling-v2.5: ONLY 5 or 10. Check models.md.
  6. Gateway namespace -- use providerOptions: { varg: {...} }, never fal, when going through the gateway (both modes).
  7. Renders cost money -- 1 credit = 1 cent. A typical 3-clip video costs $2-5. Use preview mode (local) or cheap models to iterate.
  8. API key hygiene -- Never write a raw API key value into a bash command. After obtaining a key (from the user or OTP response), immediately export VARG_API_KEY=... and use $VARG_API_KEY in all subsequent commands. This prevents keys from leaking into conversation context and terminal history.

Quick Start

Cloud Render (no bun/ffmpeg needed)

# Submit TSX code to the render service
curl -s -X POST https://render.varg.ai/api/render \
  -H "Authorization: Bearer $VARG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"code": "const img = Image({ model: varg.imageModel(\"nano-banana-pro\"), prompt: \"a cabin in mountains at sunset\", aspectRatio: \"16:9\" });\
export default (<Render width={1920} height={1080}><Clip duration={3}>{img}</Clip></Render>);"}'

# Poll for result (repeat until "completed" or "failed")
curl -s https://render.varg.ai/api/render/jobs/JOB_ID \
  -H "Authorization: Bearer $VARG_API_KEY"

Full details: cloud-render.md

Local Render (bun + ffmpeg)

/** @jsxImportSource vargai */
import { Render, Clip, Image } from "vargai/react"
import { createVarg } from "vargai/ai"

const varg = createVarg({ apiKey: process.env.VARG_API_KEY! })

const img = Image({
  model: varg.imageModel("nano-banana-pro"),
  prompt: "a cabin in mountains at sunset",
  aspectRatio: "16:9"
})

export default (
  <Render width={1920} height={1080}>
    <Clip duration={3}>{img}</Clip>
  </Render>
)
bunx vargai render video.tsx --preview   # free preview
bunx vargai render video.tsx --verbose   # full render (costs credits)

Full details: local-render.md

Single Asset (no video composition)

For one-off images, videos, speech, or music without building a multi-clip template:

curl -X POST https://api.varg.ai/v1/image \
  -H "Authorization: Bearer $VARG_API_KEY" \
  -d '{"model": "nano-banana-pro", "prompt": "a sunset over mountains"}'

Full API reference: gateway-api.md

How to Write Video Code

Video code has two layers: media generation (function calls) and composition (JSX).

// 1. GENERATE media via function calls
const img = Image({ model: ..., prompt: "..." })
const vid = Video({ model: ..., prompt: { text: "...", images: [img] }, duration: 5 })
const voice = Speech({ model: ..., voice: "rachel", children: "Hello!" })

// 2. COMPOSE via JSX tree
export default (
  <Render width={1080} height={1920}>
    <Music model={...} prompt="upbeat electronic" duration={10} volume={0.3} />
    <Clip duration={5}>
      {vid}
      <Title position="bottom">Welcome</Title>
    </Clip>
    <Captions src={voice} style="tiktok" withAudio />
  </Render>
)

Component Summary

ComponentTypePurpose
Image()Function callGenerate still image
Video()Function callGenerate video (text-to-video or image-to-video)
Speech()Function callText-to-speech audio
<Render>JSXRoot container -- sets width, height, fps
<Clip>JSXTimeline segment -- duration, transitions
<Music>JSXBackground audio (always set duration!)
<Captions>JSXSubtitle track from Speech
<Title>JSXText overlay
<Overlay>JSXPositioned layer
<Split> / <Grid>JSXLayout helpers

Full props: components.md

Provider Differences (Cloud vs Local)

Both modes use varg.* for all models. The only difference is imports:

Cloud RenderLocal Render
No imports needed (globals are auto-injected)import { ... } from "vargai/react" + import { createVarg } from "vargai/ai"
varg.imageModel("nano-banana-pro")varg.imageModel("nano-banana-pro")
varg.videoModel("kling-v3")varg.videoModel("kling-v3")
varg.speechModel("eleven_v3")varg.speechModel("eleven_v3")

**Always use varg.*Model()** with VARG_API_KEY. It handles routing, caching, billing, and works with a single key. See byok.md for using your own provider keys.

Cost & Iteration

  • 1 credit = 1 cent. nano-banana-pro = 5 credits, kling-v3 = 150 credits, speech = 20-25 credits.
  • Cache saves money. Keep unchanged prompts character-for-character identical across iterations.
  • Preview first (local mode only): --preview generates free placeholders to validate structure.
  • Full pricing: models.md

References

Load these on demand based on what you need:

NeedReferenceWhen to load
Render via APIcloud-render.mdNo bun/ffmpeg, or user wants cloud rendering
Render locallylocal-render.mdbun + ffmpeg available
Patterns & workflowsrecipes.mdTalking head, character consistency, slideshow, lipsync
Model selectionmodels.mdChoosing models, checking prices, duration constraints
Component propscomponents.mdNeed detailed props for any component
Better promptsprompting.mdUser wants cinematic / high-quality results
REST APIgateway-api.mdSingle-asset generation or Render API details
Debuggingcommon-errors.mdSomething failed or produced unexpected results
Full examplestemplates.mdNeed complete copy-paste-ready templates
BYOK keysbyok.mdUsing your own provider API keys for $0 billing

平台分布

OpenClaw

98.54%
按下载量换算3,082

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

可疑

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。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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