Highlight Editor Best

Turn a 30-minute gameplay or event recording into 1080p highlight reel clips just by typing what you need. Whether it's generating short highlight reels from...

Security Scan

The skill's requests and runtime instructions are consistent with a cloud-based video highlight service, but there are a few small inconsistencies and privacy trade-offs you should understand before installing.

Purpose & Capability

Name and description describe a remote video highlight service; the only required credential (NEMO_TOKEN) and the documented API endpoints match that purpose. Requiring an API token to run exports is appropriate for a cloud render service.

Instruction Scope

Instructions remain within the stated domain (upload videos, start sessions, poll render status, stream SSE). The skill will automatically obtain an anonymous token from the vendor if NEMO_TOKEN is not present and instructs the agent to hide raw token values from the user. It also asks the agent to detect the install path for an attribution header — this implies checking the agent's install location. The skill does not instruct reading unrelated files or unrelated environment variables, but it's not explicit where session_id or token are persisted (memory vs disk).

Install Mechanism

Instruction-only skill with no install spec and no code files — nothing is written to disk by an installer. This is the lowest-risk install model.

Credentials

Only NEMO_TOKEN is required and declared as primaryEnv, which fits the service. However SKILL.md firmware metadata references a config path (~/.config/nemovideo/) not present in the registry metadata — a small inconsistency. The automatic anonymous-token flow means the skill will call the vendor endpoint and use whichever token it obtains; that token grants access to render and download user uploads, so treat it like a credential.

Persistence & Privilege

always is false and the skill does not request system-wide privileges. Autonomous invocation is default but not by itself a concern. The skill does ask to detect install path for attribution headers but does not request modifying other skills or system configs.

Assessment

This skill appears to do what it says: upload your videos to a remote GPU-backed service and return rendered highlight clips. Before installing: (1) Accept that your raw video and any audio/metadata will be uploaded to mega-api-prod.nemovideo.ai — review that service's privacy/TOS if you have sensitive content. (2) The skill will use or obtain a NEMO_TOKEN (anonymous tokens grant rendering access for ~7 days); treat that token like a credential. (3) There is a minor metadata inconsistency: SKILL.md mentions a config path (~/.config/nemovideo/) while the registry lists none — ask the author whether any files are ever written to disk. (4) If you prefer, pre-provision a NEMO_TOKEN (instead of letting the skill obtain one) so you control token lifecycle. If you want, I can extract the full SKILL.md and highlight any places where the agent might persist state or where uploads occur.

Getting Started

Send me your raw video footage and I'll handle the AI highlight extraction. Or just describe what you're after.

Try saying:

• "create a 30-minute gameplay or event recording into a 1080p MP4"
• "extract the best moments and compile them into a 2-minute highlight reel"
• "generating short highlight reels from long recordings for content creators and sports videographers"

First-Time Connection

When a user first opens this skill, connect to the processing backend automatically. Briefly let them know (e.g. "Setting up...").

Authentication: Check if NEMO_TOKEN is set in the environment. If it is, skip to step 2.

1. Obtain a free token: Generate a random UUID as client identifier. POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with header X-Client-Id set to that UUID. The response data.token is your NEMO_TOKEN — 100 free credits, valid 7 days.
2. Create a session: POST to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Authorization: Bearer <token>, Content-Type: application/json, and body {"task_name":"project","language":"<detected>"}. Store the returned session_id for all subsequent requests.

Keep setup communication brief. Don't display raw API responses or token values to the user.

Highlight Editor Best — Extract and Export Top Moments

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 30-minute gameplay or event recording, type "extract the best moments and compile them into a 2-minute highlight reel", and you'll get a 1080p MP4 back in roughly 1-2 minutes. All rendering happens server-side.

Worth noting: trimming your source video to under 10 minutes speeds up highlight detection significantly.

Matching Input to Actions

User prompts referencing highlight editor best, 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.

Skill attribution — read from this file's YAML frontmatter at runtime:

• X-Skill-Source: highlight-editor-best
• X-Skill-Version: from frontmatter version
• X-Skill-Platform: detect from install path (~/.clawhub/ → clawhub, ~/.cursor/skills/ → cursor, else unknown)

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

Error Codes

• 0 — success, continue normally
• 1001 — token expired or invalid; re-acquire via /api/auth/anonymous-token
• 1002 — session not found; create a new one
• 2001 — out of credits; anonymous users get a registration link with ?bind=<id>, registered users top up
• 4001 — unsupported file type; show accepted formats
• 4002 — file too large; suggest compressing or trimming
• 400 — missing X-Client-Id; generate one and retry
• 402 — free plan export blocked; not a credit issue, subscription tier
• 429 — rate limited; wait 30s and retry 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.

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

Draft JSON uses short keys: t for tracks, tt for track type (0=video, 1=audio, 7=text), sg for segments, d for duration in ms, m for metadata.

Example timeline summary:

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 2-minute 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 2-minute 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.