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Agent Skill · 前端设计

letzai-api(letzai API 控制)

用于辅助 API 设计、接口文档、请求响应结构和服务集成说明。它适合让 Agent 梳理 endpoint、生成 OpenAPI 草稿、检查字段命名、整理错误码或辅助前后端联调。使用时需要确认真实业务语义、鉴权方式、分页和错误处理规则;涉及生成接口文档时,应避免凭空补字段,最好从现有代码、schema 或接口样例中提取事实。

letzai-api 是前端设计类 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,第三方扫描有未通过项,按安装量在前端设计类的 10,216 个 Skill 里排第 488。

总安装 21,890周安装 877GitHub Stars 公开资料未说明下载 7,086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:letzai-api(letzai API 控制)
来源仓库:https://github.com/letz-ai/letzai-skill
仓库路径:skills/letzai-api
安装命令:
npx skills add https://github.com/letz-ai/letzai-skill --skill letzai-api
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/letz-ai/letzai-skill --skill letzai-api

这个 Skill 是做什么的

前端设计类 Skill 关注页面结构、视觉规范和组件写法,输出通常是可运行的界面代码。

通过 LetzAI API 生成具有多个模型选项、自定义训练模型、编辑和升级的 AI 图像和视频。

支持三种图像模型(Nano Banana Pro、Flux2 Max、SeeDream)和三种视频模型(VEO、Kling、Wan),具有可配置的分辨率模式和尺寸 包括基于上下文的图像编辑、皮肤修复和带强度控制的放大(1-3x) 通过提示中的 @modelname 语法可访问针对人物、物体和样式的自定义训练模型 使用轮询模式异步生成(图像/编辑间隔 3 秒,视频间隔 2-3 秒),直到状态达到“就绪” 需要 Bearer 令牌认证和付费 LetzAI 订阅;信用成本因模型和分辨率而异

来源仓库内路径:skills/letzai-api

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/letz-ai/letzai-skill --skill letzai-api;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

letzai-api 怎么安装?

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

letzai-api 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。

letzai-api 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。

letzai-api 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。

letzai-api 用的人多吗?

公开数据是累计安装 21,890 次,近一周安装 877 次,下载 7,086 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。

letzai-api 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

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。
  • 需要处理前端设计类任务、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。

前端设计类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
frontend-design

前端设计 · Codex、Claude

8,823,360126,072只读MIT通过
vercel-composition-patterns

Vercel 组合模式 · Codex、Claude

3,858,81625,893需要联网MIT通过
vercel-react-native-skills

Vercel React native skills 前端 · Codex、Claude

2,468,66425,878只读MIT通过
adapt

adapt 搜索 · Codex、Claude

2,027,04023,125操作浏览器未明确通过
bolder

bolder 分析 · Codex、Claude

2,004,28823,130只读未明确通过
letzai-api

letzai API 控制 · 本页

21,890—接触敏感数据未明确未通过

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

LetzAI API Integration Skill

Overview

This skill enables Claude to help users integrate with the LetzAI API for AI-powered image and video generation, editing, and upscaling. Users can also leverage custom-trained AI models (persons, objects, styles) via the @modelname syntax.

Authentication

Setting Up Authentication

const headers = {
  'Content-Type': 'application/json',
  'Authorization': 'Bearer YOUR_API_KEY'
};
headers = {
    'Content-Type': 'application/json',
    'Authorization': 'Bearer YOUR_API_KEY'
}

Core Workflows

1. Image Generation

Endpoint: POST /images

Required Parameters:

  • prompt (string): Text description of the desired image. Can include @modelname to use trained models.

Optional Parameters:

  • baseModel: AI model to use

- "gemini-3-pro-image-preview" - Nano Banana Pro (recommended) - "flux2-max" - Flux2 Max - "seedream-4-5-251128" - SeeDream 4.5

  • mode: Resolution mode (varies by model)

- Nano Banana Pro: "default", "2k", "4k" - Flux2 Max: "1k", "hd" - SeeDream: "2k", "4k"

  • width / height: Image dimensions (520-2160px)

Workflow:

  1. POST to /images with parameters
  2. Receive id in response
  3. Poll GET /images/{id} every 3 seconds
  4. When status === "ready", access imageVersions.original

For code examples, see examples/image_generation.js

2. Video Generation

Endpoint: POST /videos

Required Parameters:

  • prompt (string): Text description of the desired video
  • Source image (one of):

- imageUrl: URL of source image - originalImageCompletionId: ID from previous image generation

Optional Parameters:

  • settings.mode: Video model

- "default" - Default model - "veo31" - VEO 3.1 - "kling26" - Kling 2.6 - "wan25" - Wan 2.5

  • settings.duration: Video length in seconds (2-12 depending on model)

Workflow:

  1. Ensure you have a source image (generate one first if needed)
  2. POST to /videos with parameters
  3. Receive id in response
  4. Poll GET /videos/{id} every 2-3 seconds
  5. When status === "ready", access videoPaths

For code examples, see examples/video_generation.py

3. Image Editing (Context Editing)

Endpoint: POST /image-edits

Required Parameters:

  • mode: Edit mode

- "context" - AI editing (primary mode) - "skin" - Skin fix

  • prompt: Edit instruction (e.g., "change background to beach")
  • Source image (one of):

- imageUrl: URL of source image - inputImageUrls[]: Array of source image URLs (max 9) - originalImageCompletionId: ID of previously generated LetzAI image

Optional Parameters:

  • settings.model: "gemini-3-pro-image-preview", "flux2-max", "seedream-4-5-251128"
  • settings.resolution: "2k" (HD) or "4k" (Ultra HD)
  • settings.aspect_ratio: "1:1", "16:9", "9:16", "4:3", "3:4", "21:9", "9:21"
  • baseModel: Alternative to settings.model
  • webhookUrl: Optional callback URL
  • organizationId: Optional org ID for billing

Workflow:

  1. POST to /image-edits with parameters
  2. Receive id in response
  3. Poll GET /image-edits/{id} every 3 seconds
  4. When status === "ready", access generatedImageCompletion.imageVersions.original

Note: Inpainting (mode: "in") and Outpainting (mode: "out") are deprecated - use Context Editing instead.

4. Image Upscaling

Endpoint: POST /upscales

Required Parameters:

  • Source image (one of):

- imageUrl: URL of source image - imageCompletionId: ID from previous image generation

Optional Parameters:

  • strength: Upscale factor (1-3)

Workflow:

  1. POST to /upscales with parameters
  2. Receive id in response
  3. Poll GET /upscales/{id} every 3 seconds
  4. When status === "ready", access upscaled image

5. Custom AI Models (Trained Models)

LetzAI users can train custom AI models on persons, objects, or styles via the web interface. These trained models can be used in prompts via the @modelname syntax.

List Models Endpoint: GET /models

Query Parameters:

  • page: int (default: 1)
  • limit: int (default: 10)
  • sortBy: "createdAt" | "usages"
  • sortOrder: "ASC" | "DESC"
  • class: "person" | "object" | "style"

Get Model Details: GET /models/{id}

Model Classes:

  • person: Trained on photos of a specific person
  • object: Trained on product/object images
  • style: Trained on artistic style examples

Using Models in Prompts: Tag models with @modelname syntax:

  • @john_doe on the beach at sunset - Use a person model
  • A product photo featuring @my_product - Use an object model
  • Portrait in @vintage_style aesthetic - Use a style model

Note: Model training is done via the LetzAI web interface (letz.ai), not via API.

Workflow Decision Tree

User wants to create an image:

  1. Determine appropriate model based on quality/cost needs
  2. Use POST /images with appropriate baseModel
  3. If using a trained model, include @modelname in the prompt
  4. Poll GET /images/{id} every 3s until ready
  5. Return imageVersions.original URL

User wants to use a custom trained model:

  1. Use GET /models to list available trained models (filter by class if needed)
  2. Include @modelname in the prompt when generating images
  3. Generate image normally with POST /images

User wants to edit an existing image:

  1. Obtain source image URL, inputImageUrls array, or originalImageCompletionId
  2. Use POST /image-edits with mode="context"
  3. Include settings for resolution, aspect_ratio, and model as needed
  4. Poll GET /image-edits/{id} every 3s until ready
  5. Return generatedImageCompletion.imageVersions.original

User wants to create a video:

  1. Ensure they have a source image (URL or imageCompletionId)
  2. If no source image, generate one first using /images
  3. Use POST /videos with desired settings
  4. Poll GET /videos/{id} every 2-3s until ready
  5. Return video URL from videoPaths

User wants to upscale an image:

  1. Obtain source image URL or imageCompletionId
  2. Use POST /upscales with desired strength
  3. Poll GET /upscales/{id} every 3s until ready
  4. Return upscaled image URL

Status Polling Pattern

LetzAI uses asynchronous generation. After any POST request, you must poll the corresponding GET endpoint until the job completes.

Status Values

StatusMeaning
newJob created, queued for processing
in progress / generatingCurrently processing
readyComplete - fetch URLs from response
failedError occurred - check error message

Polling Intervals

  • Images: Every 3 seconds
  • Videos: Every 2-3 seconds
  • Image Edits: Every 3 seconds
  • Upscales: Every 3 seconds

For detailed polling implementation, see examples/polling_pattern.md

Pricing Reference

FeatureModelCredits
Image GenNano Banana Pro80/160/240 (1k/HD/4K)
Image GenFlux2 Max60/120 (1k/HD)
Image GenSeeDream80/160 (HD/4K)
EditingSame as aboveSame pricing
VideoDefault60 cr/sec (2-6 sec)
VideoVEO 3.11500-6000 cr (8 sec)
VideoKling 2.6750-1500 cr (5-10 sec)
UpscaleAll40 cr

Error Handling

Common HTTP Status Codes

StatusMeaningSolution
401Invalid or missing API keyCheck Authorization header format
402Insufficient creditsTop up at letz.ai/subscription
400Invalid parametersVerify baseModel, mode, dimensions
404Resource not foundCheck the ID is correct
429Rate limitedImplement exponential backoff
500Server errorRetry after delay

Error Response Format

{
  "error": "Error description",
  "code": "ERROR_CODE"
}

Limitations

  • Async Generation: All generation is asynchronous - must poll for results
  • Video Source: Video generation requires a source image
  • Reference Images: Maximum 9 reference images for image editing
  • Model Training: Cannot train custom AI models via API - use letz.ai web interface
  • API Key Required: Paid subscription required for API access

Quick Reference: API Endpoints

EndpointMethodPurpose
/imagesGETList user's images
/imagesPOSTCreate image (prompt, baseModel, mode, width, height)
/images/{id}GETGet image status & URLs (poll every 3s)
/images/{id}/interruptionPUTStop image generation
/images/{id}/privacyPUTChange image privacy
/videosGETList user's videos
/videosPOSTCreate video (prompt, imageUrl, settings)
/videos/{id}GETGet video status & URLs (poll every 2-3s)
/videos/{id}/interruptionPUTStop video generation
/videos/{id}/privacyPUTChange video privacy
/image-editsGETList user's edits
/image-editsPOSTEdit image (mode, prompt, imageUrl/inputImageUrls, settings)
/image-edits/{id}GETGet edit status & URLs (poll every 3s)
/upscalesPOSTUpscale image (imageUrl/imageUrls, strength, mode, size)
/upscales/{id}GETGet upscale status & URLs (poll every 3s)
/modelsGETList trained AI models (filter by class: person/object/style)
/models/{id}GETGet specific model details

Key Response Fields

  • Images/Upscales: imageVersions.original, imageVersions["1920x1920"], imageVersions["640x640"]
  • Edits: generatedImageCompletion.imageVersions.original
  • Videos: videoPaths object, videoVersions array
  • Status values: new, in progress/generating, ready, failed

Additional Resources

平台分布

Codex

34.25%
按下载量换算2,427

Claude

29.38%
按下载量换算2,082

Cursor

18.18%
按下载量换算1,288

Gemini CLI

9.24%
按下载量换算655

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

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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