Ai Video Maker Youtube

Skip the learning curve of professional editing software. Describe what you want — turn my clips into a YouTube video with intro, transitions, and background...

Security Scan

The skill's behavior largely matches a remote video-rendering service, but there are inconsistencies in declared requirements and the runtime instructions cause user files and auth tokens to be sent to a third-party API — verify vendor trust and intended token handling before installing.

Purpose & Capability

The skill claims to produce YouTube-ready videos and its API calls and upload steps align with that purpose. However, the registry metadata (no required config paths) conflicts with the SKILL.md frontmatter which lists ~/.config/nemovideo/ as a config path. Also NEMO_TOKEN is declared as required but the runtime instructions include an anonymous-token acquisition flow if no token is present — this mismatch should be clarified.

Instruction Scope

The instructions direct the agent to obtain an anonymous token from a remote endpoint, create a session, upload user-supplied files (multipart file uploads or URLs), stream SSE data, poll render endpoints, and read the skill's YAML frontmatter and install path to set attribution headers. Uploading user files and actively generating/authenticating tokens are within a video service's needs, but they also introduce remote data transfer and token management that the SKILL.md does not fully describe (e.g., where tokens are stored, for how long, or whether uploads are retained).

Install Mechanism

Instruction-only skill with no install spec or code files; nothing is written to disk by an installer. This minimizes install-time risk.

Credentials

Only NEMO_TOKEN is declared as the primary credential, which is appropriate for a cloud service. But the SKILL.md both expects NEMO_TOKEN and provides a flow to fetch an anonymous token if missing — the registry requiring the env var while runtime supports auto-provisioning is inconsistent. The SKILL.md also references reading install paths and the skill's YAML frontmatter to populate headers; these are plausible but should be documented.

Persistence & Privilege

The skill is not always-on and does not request system-wide privileges. It does not declare writing other skills' configs. Note: autonomous invocation (default) remains enabled — combine this with the remote upload/token behavior when deciding trust.

What to consider before installing

This skill will upload whatever video/image files the user supplies to mega-api-prod.nemovideo.ai and will create or use a NEMO_TOKEN to interact with that service. Before installing or using it: 1) Confirm you trust the remote domain and its privacy/retention policy — do not upload sensitive footage unless you are sure. 2) Ask the skill author how NEMO_TOKEN is stored, whether anonymous tokens persist locally or on the service, and how long uploads are retained. 3) Note the metadata mismatch: the registry says no config paths but the SKILL.md references ~/.config/nemovideo/ and also provides an auto-token flow despite declaring NEMO_TOKEN as required — request clarification. 4) If you have concerns, prefer providing your own service token (NEMO_TOKEN) with limited scope or testing with non-sensitive sample footage first.

Getting Started

Share your video clips or images and I'll get started on AI video creation. Or just tell me what you're thinking.

Try saying:

• "create my video clips or images"
• "export 1080p MP4"
• "turn my clips into a YouTube"

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.

AI Video Maker for YouTube — Create and Export YouTube Videos

Send me your video clips or images and describe the result you want. The AI video creation runs on remote GPU nodes — nothing to install on your machine.

A quick example: upload a 2-minute screen recording and a logo image, type "turn my clips into a YouTube video with intro, transitions, and background music", and you'll get a 1080p MP4 back in roughly 1-2 minutes. All rendering happens server-side.

Worth noting: keeping your source clips under 3 minutes each speeds up AI processing significantly.

Matching Input to Actions

User prompts referencing ai video maker youtube, 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.

Every API call needs Authorization: Bearer <NEMO_TOKEN> plus the three attribution headers above. If any header is missing, exports return 402.

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

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

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 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 → "turn my clips into a YouTube video with intro, transitions, and 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 "turn my clips into a YouTube video with intro, transitions, and 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 best YouTube upload compatibility.