Unified Video

Skip the learning curve of professional editing software. Describe what you want — combine all clips into one seamless video with smooth transitions — and ge...

Security Scan

The skill's behavior mostly matches a cloud video-merge service, but there are small inconsistencies (metadata vs registry) and privacy implications you should understand before uploading videos or granting tokens.

Purpose & Capability

The skill is described as a cloud video merging service and its runtime instructions call a remote rendering API and upload media — that is coherent. However the SKILL.md frontmatter declares a config path (~/.config/nemovideo/) that the registry metadata did not list; this mismatch is unexplained.

Instruction Scope

Instructions are explicit about uploading files, creating sessions, streaming SSE, polling render status, and attaching attribution headers. The agent is told to read this skill's YAML frontmatter and detect install path to set X-Skill-Platform — which requires filesystem access. No other unrelated files or secrets are requested, but the skill will send user-uploaded media to an external API (mega-api-prod.nemovideo.ai).

Install Mechanism

No install spec or code is included (instruction-only). Nothing is written to disk by an installer, which minimizes supply-chain risk.

Credentials

The skill declares NEMO_TOKEN as the primary credential (expected for a cloud API). But the SKILL.md also instructs the agent to request an anonymous token from the same API if NEMO_TOKEN is absent — and the frontmatter lists a config path (~/.config/nemovideo/) that could imply reading/writing local config. The registry metadata earlier listed no config paths, creating an inconsistency. The required credential and anonymous-token flow are plausible but you should confirm whether the skill will persist tokens or write config files locally and whether the config path is actually used.

Persistence & Privilege

The skill does not request always:true, has no install hooks, and uses ephemeral session tokens. It keeps session_id for ongoing jobs but does not request system-wide privileges.

What to consider before installing

This skill appears to implement a remote video-merge service and will upload files to mega-api-prod.nemovideo.ai; that behavior is expected for this purpose. Before installing or using it: (1) Confirm the source/owner and ask for a privacy policy and data retention policy — do not upload sensitive or private videos until you know where they are stored and for how long. (2) Ask whether the skill will write anything to ~/.config/nemovideo/ or persist the anonymous token; prefer ephemeral tokens and explicit user consent for persistent storage. (3) Note the skill can auto-create an anonymous NEMO_TOKEN if none is provided — this is convenient but means uploads can proceed without you supplying a token. (4) If you need stronger guarantees, request a documented API endpoint list, TLS/CA expectations, and whether media is encrypted at rest. The metadata inconsistency (declared configPath in SKILL.md vs. registry showing none) is why I marked this suspicious rather than benign; if the developer confirms no local config access and provides a privacy policy, confidence could be raised.

Getting Started

Got multiple video clips to work with? Send it over and tell me what you need — I'll take care of the AI video merging.

Try saying:

• "merge four separate interview clips totaling 8 minutes into a 1080p MP4"
• "combine all clips into one seamless video with smooth transitions"
• "combining multiple video clips into one continuous video for content creators"

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.

Unified Video — Merge Clips Into One Video

Drop your multiple video clips in the chat and tell me what you need. I'll handle the AI video merging on cloud GPUs — you don't need anything installed locally.

Here's a typical use: you send a four separate interview clips totaling 8 minutes, ask for combine all clips into one seamless video with smooth transitions, 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 — trim silence from each clip before merging for a cleaner final result.

Matching Input to Actions

User prompts referencing unified 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.

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

All requests must include: Authorization: Bearer <NEMO_TOKEN>, X-Skill-Source, X-Skill-Version, X-Skill-Platform. Missing attribution headers will cause export to fail with 402.

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 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 → "combine all clips into one seamless video with smooth transitions" → 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 "combine all clips into one seamless video with smooth transitions" — 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 platforms and devices.