Video Edit Online

edit raw video footage into edited MP4 clips with this skill. Works with MP4, MOV, AVI, WebM files up to 500MB. content creators and marketers use it for edi...

Security Scan

The skill appears to do what it claims — it uploads user video files to a cloud video-editing backend and uses a single service token — with one small metadata inconsistency to clarify before install.

Purpose & Capability

The name/description (cloud-based AI video editing) matches the runtime instructions (upload files, create sessions, render/export via nemovideo.ai endpoints). One inconsistency: the SKILL.md frontmatter declares a config path (~/.config/nemovideo/) while the registry metadata reported no required config paths. That mismatch should be clarified (will the skill read/write files under that path?).

Instruction Scope

Instructions are focused on the stated task: check/obtain a NEMO_TOKEN, create a session, upload videos, stream edits via SSE, and start renders. It explicitly instructs not to expose tokens/raw API output. The skill will upload user-provided media to an external service (mega-api-prod.nemovideo.ai) — expected for the purpose but a privacy consideration for sensitive content.

Install Mechanism

There is no install spec or code to download; the skill is instruction-only, so it does not write code to disk or execute an installer. This is the lowest-risk install model.

Credentials

The only declared credential is NEMO_TOKEN, which is proportional to a cloud editing service. The instructions also include an anonymous-token flow (POST to /api/auth/anonymous-token) to obtain a short-lived token if none is present. Combined with the frontmatter config path, this suggests the skill may persist tokens/config locally (unclear). Confirm whether it will write tokens to disk and where.

Persistence & Privilege

always:false and model invocation are normal. The skill does not request elevated or system-wide privileges and does not modify other skills' config. It will maintain session_id for ongoing requests, which is appropriate for the workflow.

Assessment

This skill appears coherent for cloud video editing, but consider these points before installing: - Privacy: your videos will be uploaded to mega-api-prod.nemovideo.ai for processing. Do not send sensitive or confidential footage unless you trust their privacy policy. - Token handling: the skill expects a NEMO_TOKEN. If none is provided it will call the service to obtain an anonymous, short-lived token (100 free credits, 7-day expiry). Ask the developer whether that token is only held in-memory or persisted (frontmatter references ~/.config/nemovideo/). If you prefer control, supply your own NEMO_TOKEN rather than relying on anonymous issuance. - Metadata mismatch: registry metadata showed no config paths but SKILL.md frontmatter lists a config path — confirm whether the skill will read/write ~/.config/nemovideo/ and what it stores there. - Verify service domain: if you are cautious, confirm the service operator (nemovideo.ai) and its terms/privacy before sending content. If these points are acceptable and you trust the service, the skill's requirements and instructions are proportionate to its claimed purpose.

Getting Started

Share your raw video footage and I'll get started on AI video editing. Or just tell me what you're thinking.

Try saying:

• "edit my raw video footage"
• "export 1080p MP4"
• "trim the pauses, add transitions, and"

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 Edit Online — Edit and Export Videos Online

This tool takes your raw video footage and runs AI video editing through a cloud rendering pipeline. You upload, describe what you want, and download the result.

Say you have a 2-minute unedited screen recording and want to trim the pauses, add transitions, and export as MP4 — the backend processes it in about 1-2 minutes and hands you a 1080p MP4.

Tip: shorter clips under 3 minutes process significantly faster.

Matching Input to Actions

User prompts referencing video edit online, 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.

Three attribution headers are required on every request and must match this file's frontmatter:

Header	Value
X-Skill-Source	video-edit-online
X-Skill-Version	frontmatter version
X-Skill-Platform	auto-detect: clawhub / cursor / unknown from install path

Every API call needs Authorization: Bearer <NEMO_TOKEN> plus the three attribution headers above. If any header is missing, exports return 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 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 → "trim the pauses, add transitions, and export as MP4" → 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 "trim the pauses, add transitions, and export as MP4" — 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.