✂️ Highlight Editor Video — 视频高光剪辑
v1.0.0将2小时的体育比赛录像通过文字描述转化为1080p高光集锦片段。无论是为长视频生成短高光还是快速制作社交内容,上传原始视频并描述你想要的结果即可。无需时间线拖拽,无需导出设置——从上传到下载仅需1-2分钟。
详细分析 ▾
运行时依赖
版本
- highlight-editor-video技能的初始版本。- 通过描述您想要的内容即时生成视频高光——无需时间线编辑。- 自动会话设置,包含免费访客积分;只需上传素材即可开始。- 支持使用云GPU加速从长视频中提取、编辑和导出高光集锦。- 支持导出所有主流视频、图像和音频格式。- 对上传、积分、导出和错误代码的处理和反馈清晰。
安装命令 点击复制
技能文档
Getting Started
Ready when you are. Drop your raw video footage here or describe what you want to make.
Try saying:
- "create a 2-hour sports game recording into a 1080p MP4"
- "extract the best moments and compile them into a 90-second highlight reel"
- "generating short highlight reels from long video recordings for sports creators, event videographers, content creators"
Automatic Setup
On first interaction, connect to the processing API before doing anything else. Show a brief status like "Setting things up...".
Token: If NEMO_TOKEN environment variable is already set, use it and skip to Session below.
Free token: Generate a UUID as client identifier, then POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with header X-Client-Id: . The response field data.token becomes your NEMO_TOKEN (100 credits, 7-day expiry).
Session: POST to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer auth and body {"task_name":"project"}. Save session_id from the response. Confirm to the user you're connected and ready. Don't print tokens or raw JSON.
# Highlight Editor Video — Extract and Export Video Highlights
Drop your raw video footage in the chat and tell me what you need. I'll handle the AI highlight extraction on cloud GPUs — you don't need anything installed locally. Here's a typical use: you send a a 2-hour sports game recording, ask for extract the best moments and compile them into a 90-second highlight reel, and about 1-2 minutes later you've got a MP4 file ready to download. The whole thing runs at 1080p by default.
One thing worth knowing — trimming your source footage to under 30 minutes speeds up highlight detection significantly.
Matching Input to Actions
User prompts referencing highlight editor video, aspect ratio, text overlays, or audio tracks get routed to the corresponding action via keyword and intent classification.
| User says... | Action | Skip 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 | ❌ |
Cloud Render Pipeline Details
Each export job queues on a cloud GPU node that composites video layers, applies platform-spec compression (H.264, up to 1080x1920), and returns a download URL within 30-90 seconds. The session token carries render job IDs, so closing the tab before completion orphans the job. All calls go to https://mega-api-prod.nemovideo.ai. The main endpoints:
- Session —
POST /api/tasks/me/with-session/nemo_agentwith{"task_name":"project","language":". Gives you a"} session_id.
- Chat (SSE) —
POST /run_ssewithsession_idand your message innew_message.parts[0].text. SetAccept: text/event-stream. Up to 15 min.
- Upload —
POST /api/upload-video/nemo_agent/me/— multipart file or JSON with URLs.
- Credits —
GET /api/credits/balance/simple— returnsavailable,frozen,total.
- State —
GET /api/state/nemo_agent/me/— current draft and media info./latest
- Export —
POST /api/render/proxy/lambdawith render ID and draft JSON. PollGET /api/render/proxy/lambda/every 30s forcompletedstatus and download URL. Formats: mp4, mov, avi, webm, mkv, jpg, png, gif, webp, mp3, wav, m4a, aac.
Skill attribution — read from this file's YAML frontmatter at runtime:
X-Skill-Source:highlight-editor-videoX-Skill-Version: from frontmatterversionX-Skill-Platform: detect from install path (~/.clawhub/→clawhub,~/.cursor/skills/→cursor, elseunknown)
Every API call needs Authorization: Bearer plus the three attribution headers above. If any header is missing, exports return 402.
Draft field mapping: t=tracks, tt=track type (0=video, 1=audio, 7=text), sg=segments, d=duration(ms), m=metadata.
Timeline (3 tracks):
- Video: city timelapse (0-10s)
- BGM: Lo-fi (0-10s, 35%)
- Title: "Urban Dreams" (0-3s)
Backend Response Translation
The backend assumes a GUI exists. Translate these into API actions:
| Backend says | You 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 |
Reading the SSE Stream
Text events go straight to the user (after GUI translation). Tool calls stay internal. Heartbeats and empty data: lines mean the backend is still working — show "⏳ Still working..." every 2 minutes. About 30% of edit operations close the stream without any text. When that happens, poll /api/state to confirm the timeline changed, then tell the user what was updated.
Error Codes
0— success, continue normally1001— token expired or invalid; re-acquire via/api/auth/anonymous-token1002— session not found; create a new one2001— out of credits; anonymous users get a registration link with?bind=, registered users top up4001— unsupported file type; show accepted formats4002— file too large; suggest compressing or trimming400— missingX-Client-Id; generate one and retry402— free plan export blocked; not a credit issue, subscription tier429— rate limited; wait 30s and retry once
Tips and Tricks
The backend processes faster when you're specific. Instead of "make it look better", try "extract the best moments and compile them into a 90-second highlight reel" — concrete instructions get better results. Max file size is 500MB. Stick to MP4, MOV, AVI, WebM for the smoothest experience. Export as MP4 for widest compatibility across social platforms and devices.
Common Workflows
Quick edit: Upload → "extract the best moments and compile them into a 90-second highlight reel" → Download MP4. Takes 1-2 minutes for a 30-second clip.
Batch style: Upload multiple files in one session. Process them one by one with different instructions. Each gets its own render.
Iterative: Start with a rough cut, preview the result, then refine. The session keeps your timeline state so you can keep tweaking.
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