Vn Ai Video Editor

Get edited MP4 videos ready to post, without touching a single slider. Upload your raw video footage (MP4, MOV, AVI, WebM, up to 500MB), say something like "...

Security Scan

The skill's requirements and runtime instructions are coherent with a cloud-based AI video editing service, but verify provenance and privacy before uploading sensitive footage.

Purpose & Capability

Name/description (VN AI video editing) match the actions in SKILL.md: uploads, editing requests, render/export via nemo video API. The single required env var (NEMO_TOKEN) is appropriate. Minor inconsistency: the SKILL.md frontmatter lists a config path (~/.config/nemovideo/), but the registry metadata earlier indicated no required config paths — this is likely benign but worth confirming.

Instruction Scope

Runtime instructions are narrowly focused on session creation, SSE messaging, uploads, render/export polling, and credits/state checks against the nemovideo API. They do not instruct reading unrelated system files or additional environment variables, nor do they request network calls to unrelated endpoints.

Install Mechanism

Instruction-only skill with no install spec and no code files — nothing is written to disk or installed by the skill itself, which is the lowest-risk install posture.

Credentials

Only NEMO_TOKEN is declared as required and used as the bearer auth token, which is proportionate. The skill will also create an anonymous token via the provider's /api/auth/anonymous-token endpoint if NEMO_TOKEN is not present (100 free credits, 7-day expiry). The earlier-noted configPath in the SKILL.md frontmatter suggests the skill might look under ~/.config/nemovideo/ for stored credentials — this should be clarified because accessing user config would broaden its scope.

Persistence & Privilege

always is false and the skill does not request system-wide persistence. It instructs keeping a session token for task operations (expected for an API client) but does not attempt to modify other skills or agent-wide settings.

Assessment

This skill appears to do what it says: it uploads your videos to a cloud backend and returns edited MP4s, using a NEMO_TOKEN bearer token (or it will get a short-lived anonymous token for you). Before installing or using it, consider: 1) Provenance: the skill has no homepage and the source is unknown — verify the publisher if you need assurance. 2) Privacy: any video you upload will be sent to mega-api-prod.nemovideo.ai; do not upload sensitive or private footage unless you trust that service and have reviewed its terms/privacy. 3) Token and config access: the skill will use NEMO_TOKEN or create an anonymous token; SKILL.md also references a config path (~/.config/nemovideo/) — ask the author whether the skill will read or write local config files. 4) Network behavior: the skill opens SSE streams and uploads files; ensure your environment permits those connections and that this is acceptable. If any of these points are unacceptable, do not install or run the skill until the publisher/source and privacy/security behavior are clarified.

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 footage, 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.

VN AI Video Editor — Edit and Export Polished Videos

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 60-second phone-recorded clip and want to trim the footage, add transitions, and sync background music — the backend processes it in about 1-2 minutes and hands you a 1080p MP4.

Tip: shorter clips under 60 seconds process significantly faster.

Matching Input to Actions

User prompts referencing vn ai video editor, 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 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.

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

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

API base: https://mega-api-prod.nemovideo.ai

Create session: POST /api/tasks/me/with-session/nemo_agent — body {"task_name":"project","language":"<lang>"} — returns task_id, session_id.

Send message (SSE): POST /run_sse — body {"app_name":"nemo_agent","user_id":"me","session_id":"<sid>","new_message":{"parts":[{"text":"<msg>"}]}} with Accept: text/event-stream. Max timeout: 15 minutes.

Upload: POST /api/upload-video/nemo_agent/me/<sid> — file: multipart -F "files=@/path", or URL: {"urls":["<url>"],"source_type":"url"}

Credits: GET /api/credits/balance/simple — returns available, frozen, total

Session state: GET /api/state/nemo_agent/me/<sid>/latest — key fields: data.state.draft, data.state.video_infos, data.state.generated_media

Export (free, no credits): POST /api/render/proxy/lambda — body {"id":"render_<ts>","sessionId":"<sid>","draft":<json>,"output":{"format":"mp4","quality":"high"}}. Poll GET /api/render/proxy/lambda/<id> every 30s until status = completed. Download URL at output.url.

Supported formats: mp4, mov, avi, webm, mkv, jpg, png, gif, webp, mp3, wav, m4a, aac.

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

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.

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 footage, add transitions, and sync background music" → 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 footage, add transitions, and sync background music" — concrete instructions get better results.

Max file size is 500MB. Stick to MP4, MOV, AVI, WebM for the smoothest experience.

Export as MP4 with H.264 codec for the widest compatibility across platforms.