Video Editor Highlights

Get highlight reel clips ready to post, without touching a single slider. Upload your raw video footage (MP4, MOV, AVI, WebM, up to 500MB), say something lik...

Security Scan

The skill's requirements and runtime instructions are consistent with a remote video-processing service: it uploads user video to a nemo video API using a NEMO_TOKEN (or an anonymous token it can fetch), but it will send your media and session metadata to an external service and the package has no homepage or source to verify the backend.

Purpose & Capability

Name/description (automatic highlight extraction and export) align with the declared single credential (NEMO_TOKEN) and the SKILL.md which describes calls to a video-processing backend. Requesting a single API token is proportionate for a cloud render service.

Instruction Scope

Instructions are focused on uploading video, creating sessions, streaming SSE, and triggering renders on mega-api-prod.nemovideo.ai. They explicitly tell the agent to check for NEMO_TOKEN and, if absent, obtain an anonymous token from the service. No instructions request unrelated local files, but the skill specifies detecting install path and adding attribution headers, which implies reading environment/paths to determine platform.

Install Mechanism

No install spec and no code files (instruction-only) — lowest-risk install footprint. Nothing is downloaded or written to disk by an installer.

Credentials

Only NEMO_TOKEN is declared as required and is used as the Bearer token for API calls — reasonable for a remote service. The SKILL.md also describes generating an anonymous token if none is present. Metadata lists a config path (~/.config/nemovideo/) which isn't referenced in runtime steps; that extra config-path claim is slightly disproportionate and worth verifying.

Persistence & Privilege

always:false and default autonomous invocation are used. The skill does not request permanent system-wide privileges or attempt to modify other skills. No install-time persistence is requested.

Assessment

This skill appears to be coherent for a cloud-based video highlight service, but note: it will upload your raw footage to an external nemo-video backend (mega-api-prod.nemovideo.ai). Before installing, consider: 1) privacy — don't upload sensitive or private footage unless you trust the service; 2) tokens — if you set NEMO_TOKEN, treat it like a credential; if you don't set one the skill will fetch an anonymous token for you (100 free credits, 7-day expiry); 3) provenance — there is no homepage or source listed (owner ID only), which reduces your ability to audit the backend; 4) config path claim (~/.config/nemovideo/) is present in metadata though not used in instructions — verify why it’s needed. If you proceed, test with non-sensitive sample videos, and avoid placing other secrets in environment variables the skill can read.

Getting Started

Ready when you are. Drop your raw video footage here or describe what you want to make.

Try saying:

• "generate 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, YouTubers, event videographers"

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.

Video Editor Highlights — Extract and Export Video Highlights

Send me your raw video footage and describe the result you want. The AI highlight extraction runs on remote GPU nodes — nothing to install on your machine.

A quick example: upload a 2-hour sports game recording, type "extract the best moments and compile them into a 90-second highlight reel", and you'll get a 1080p MP4 back in roughly 1-2 minutes. All rendering happens server-side.

Worth noting: trimming your footage to under 10 minutes before upload speeds up highlight detection.

Matching Input to Actions

User prompts referencing video editor highlights, 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.

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

Endpoint	Method	Purpose
/api/tasks/me/with-session/nemo_agent	POST	Start a new editing session. Body: {"task_name":"project","language":"<lang>"}. Returns session_id.
/run_sse	POST	Send a user message. Body includes app_name, session_id, new_message. Stream response with Accept: text/event-stream. Timeout: 15 min.
/api/upload-video/nemo_agent/me/<sid>	POST	Upload a file (multipart) or URL.
/api/credits/balance/simple	GET	Check remaining credits (available, frozen, total).
/api/state/nemo_agent/me/<sid>/latest	GET	Fetch current timeline state (draft, video_infos, generated_media).
/api/render/proxy/lambda	POST	Start export. Body: {"id":"render_<ts>","sessionId":"<sid>","draft":<json>,"output":{"format":"mp4","quality":"high"}}. Poll status every 30s.

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

Headers are derived from this file's YAML frontmatter. X-Skill-Source is video-editor-highlights, X-Skill-Version comes from the version field, and X-Skill-Platform is detected from the install path (~/.clawhub/ = clawhub, ~/.cursor/skills/ = cursor, otherwise unknown).

Include Authorization: Bearer <NEMO_TOKEN> and all attribution headers on every request — omitting them triggers a 402 on export.

Error Handling

Code	Meaning	Action
0	Success	Continue
1001	Bad/expired token	Re-auth via anonymous-token (tokens expire after 7 days)
1002	Session not found	New session §3.0
2001	No credits	Anonymous: show registration URL with ?bind=<id> (get <id> from create-session or state response when needed). Registered: "Top up credits in your account"
4001	Unsupported file	Show supported formats
4002	File too large	Suggest compress/trim
400	Missing X-Client-Id	Generate Client-Id and retry (see §1)
402	Free plan export blocked	Subscription tier issue, NOT credits. "Register or upgrade your plan to unlock export."
429	Rate limit (1 token/client/7 days)	Retry in 30s once

SSE Event Handling

Event	Action
Text response	Apply GUI translation (§4), present to user
Tool call/result	Process internally, don't forward
heartbeat / empty data:	Keep waiting. Every 2 min: "⏳ Still working..."
Stream closes	Process 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.

Translating GUI Instructions

The backend responds as if there's a visual interface. Map its instructions to API calls:

• "click" or "点击" → execute the action via the relevant endpoint
• "open" or "打开" → query session state to get the data
• "drag/drop" or "拖拽" → send the edit command through SSE
• "preview in timeline" → show a text summary of current tracks
• "Export" or "导出" → run the 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)

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.

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.