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magic-light-ai魔法光 AI

Agent Skill

magic-light-ai 用于辅助部署、云资源、容器和基础设施运维,适合在 OpenClaw 中需要检查配置、整理部署步骤或排查环境问题时使用。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。

总安装

3,170

周安装

127

GitHub Stars

公开资料未说明

下载量

1,026
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:magic-light-ai(魔法光 AI)
来源仓库:https://github.com/dsewell-583h0/magic-light-ai
安装命令:
openclaw skills install magic-light-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 magic-light-ai

简介

使用云端 AI 技术修复视频画面中的光线缺陷。

  • 支持 MP4/MOV 格式上传与亮度、对比度智能优化。
  • 处理时长与画质提升程度取决于原始素材质量与订阅等级。
  • 上传内容仅限授权素材,禁止传播他人隐私影像。
  • 建议先裁剪片段测试效果再处理完整视频文件。magic-light-ai 属于开发类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
magic-light-ai
version
1.0.1
displayName
Magic Light AI — Add Lighting Effects to Your Videos
description
>
metadata
{"openclaw": {"emoji": "💡", "requires": {"env": ["NEMO_TOKEN"], "configPaths": ["~/.config/nemovideo/"]}, "primaryEnv": "NEMO_TOKEN", "variant": "short_prompts"}}

Getting Started

Ready for magic light ai. Drop your video or tell me what lighting you want.

Try saying:

  • "warm up the lighting in my clip"
  • "add a golden hour glow effect"
  • "fix the dark areas in this footage"

First-Time Connection

On first use, the skill sets up a connection to the cloud backend. You'll see a brief "Setting up..." message.

Authentication: If NEMO_TOKEN is already in your environment, skip ahead to creating a session.

  1. Get a free token: Make a UUID for client identification. Send a POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with X-Client-Id as the UUID. Grab data.token from the response — that's 100 free credits for 7 days.
  2. Open a session: POST https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer token auth and body {"task_name":"project","language":"<detected>"}. Hang on to the session_id.

No raw tokens or JSON get shown to the user.

Fix Lighting and Add Glow Effects to Footage

Upload your video and describe the lighting change you want. Everything runs on cloud GPUs — nothing to install on your machine.

Real example: I had a dim interview clip shot in a basement. Typed "brighten the subject and add soft warm fill light" and 90 seconds later the MP4 looked like it was filmed in a proper studio. Default output is 1080p.

Worth noting — the AI handles color grading alongside lighting, so asking for "warm sunset tones" gives you both the light direction and the color shift.

How Requests Get Routed

Depending on what you type, your message goes to different handlers.

What you sayWhere it goesSkips SSE?
"export" / "download" / "send video" / "导出"Export handlerYes
"credits" / "balance" / "积分" / "余额"Credit checkYes
"status" / "show tracks" / "状态"State queryYes
"upload" / user attaches file / "上传"Upload handlerYes
Anything else (lighting, effects, color...)Main SSE pipelineNo

Cloud Rendering Details

Your video gets uploaded to a GPU cluster for processing. The rendering queue re-encodes at 8Mbps for 1080p. Lighting adjustments are frame-accurate — no bleed between edited and unedited segments.

Every API call needs these headers: Authorization: Bearer <NEMO_TOKEN> plus attribution headers X-Skill-Source, X-Skill-Version, X-Skill-Platform. Without them, export returns a 402 error.

Attribution values come from this file's YAML block: X-Skill-Source = magic-light-ai, X-Skill-Version from the version field, X-Skill-Platform from install path (~/.clawhub/ = clawhub, ~/.cursor/skills/ = cursor, else unknown).

Base URL: https://mega-api-prod.nemovideo.ai

Start session: POST /api/tasks/me/with-session/nemo_agent with {"task_name":"project","language":"<lang>"}. Returns task_id and session_id.

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

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

Check credits: GET /api/credits/balance/simple — fields: available, frozen, total.

Get session state: GET /api/state/nemo_agent/me/<sid>/latest — look at data.state.draft, data.state.video_infos, data.state.generated_media.

Export video (no credit cost): POST /api/render/proxy/lambda with {"id":"render_<ts>","sessionId":"<sid>","draft":<json>,"output":{"format":"mp4","quality":"high"}}. Then poll GET /api/render/proxy/lambda/<id> every 30 seconds until status is completed. Download from output.url.

Accepted file types: mp4, mov, avi, webm, mkv, jpg, png, gif, webp, mp3, wav, m4a, aac.

What Errors Mean

CodeProblemWhat to do
0All goodKeep going
1001Token expired or badGet a fresh token from /api/auth/anonymous-token
1002Session goneStart a new session
2001Out of creditsAnonymous users: show signup link with ?bind=<id>. Others: top up
4001Wrong file typeList the accepted formats
4002File too bigTell them to trim or compress first
400No X-Client-Id headerGenerate one and resend
402Export blocked on free tierNot a credits problem — need to register or upgrade
429Too many requestsWait 30 seconds, try once more

Translating Backend GUI Instructions

The backend was built for a visual editor, so it sometimes refers to buttons and panels. Translate them:

Backend saysWhat you do instead
"click [button]" or "点击"Run the matching API call
"open [panel]" or "打开"Fetch session state
"drag/drop" or "拖拽"Send an edit command through SSE
"preview in timeline"Print a track summary
"Export button" or "导出"Run the export flow

SSE Stream Behavior

Text responses go to the user after GUI translation. Tool calls are internal only. Heartbeats and blank data: lines mean work is in progress — say "Still working on it..." every 2 minutes or so.

Around 30% of edits close the stream with no text output. If that happens, check /api/state to verify the edit landed, then summarize what changed.

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

Track summary format:

Timeline (3 tracks): 1. Video: sunset beach (0-15s) 2. Audio: ambient waves (0-15s, 40%) 3. Text: "Golden Hour" (2-5s)

Tips and Tricks

Be specific about what you want. "Fix the lighting" is okay but "add warm fill light from the left, keep shadows soft" gets much better results.

You can combine lighting with other edits in a single request — the backend handles multi-step operations.

Max upload is 500MB. Stick with MP4 or MOV for the smoothest experience. Output is always MP4.

Quick Start

  1. Upload your footage (drag and drop into the chat)
  2. Describe the lighting: "make it look like golden hour with soft shadows"
  3. Wait about 60-120 seconds
  4. Download the finished MP4

No account needed for your first 100 credits. Works with MP4, MOV, AVI, WebM.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

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

97.46%
按下载量换算1,000

安全审计

VirusTotal

通过

ClawScan

可疑

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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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