Youtube Image To Video

Skip the learning curve of professional editing software. Describe what you want — turn these images into a YouTube video with music and transitions — and ge...

Security Scan

The skill's stated purpose (turn images into YouTube-ready videos) aligns with the single credential it requests (NEMO_TOKEN) and its API usage, but there are inconsistencies and minor scope-creep (filesystem/config probing and metadata mismatches) that warrant caution before installing.

Purpose & Capability

The skill is an instruction-only adapter for the nemo video rendering service and legitimately needs a NEMO_TOKEN to call that API. However, the SKILL.md metadata declares a config path (~/.config/nemovideo/) and the runtime text instructs detecting install paths for X-Skill-Platform — these file/path-related requirements are not reflected in the registry’s top-level metadata and are not strictly necessary for the core 'images→video' functionality.

Instruction Scope

Instructions are specific about session creation, uploads, SSE, and export polling (which is expected). They also direct the agent to detect platform by checking local install paths (~/.clawhub/, ~/.cursor/skills/) and reference a local config path (~/.config/nemovideo/). Reading those paths is outside the minimal needs for uploading images and may expose local environment/installation details. The skill also requires the agent to include attribution headers (X-Skill-Platform/Source/Version), which could force the agent to reveal environment/install metadata.

Install Mechanism

No install spec or code is present; this is instruction-only so nothing is written to disk by an installer. That reduces risk.

Credentials

Only one credential is required (NEMO_TOKEN), which matches the external service described. The concern is a mismatch between the registry (no required config paths) and the SKILL.md metadata that lists ~/.config/nemovideo/ as a config path — implying it may read local config files or cached tokens in addition to using the declared env var.

Persistence & Privilege

The skill is not always-enabled and does not request elevated platform privileges. It uses transient session IDs for jobs. There is no evidence it modifies other skills or requests permanent presence.

What to consider before installing

This skill mostly does what it says (it calls a nemo video API and needs a NEMO_TOKEN). Before installing, confirm with the author whether the skill needs to read local config or detect install paths — that behavior is not strictly necessary and could reveal information about your environment. If you proceed: (1) prefer giving a short‑lived or limited-permission token rather than a long-lived credential; (2) deny or monitor any agent filesystem access if your platform allows it; (3) ask the maintainer why ~/.config/nemovideo/ and platform-detection are needed and request they remove/clarify that behavior if it’s unnecessary; and (4) avoid providing secrets for unrelated services. If you’re uncomfortable with the agent probing install/config locations, do not install until the author clarifies and removes that requirement.

Getting Started

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

Try saying:

• "convert my images"
• "export 1080p MP4"
• "turn these images into a YouTube"

Getting Connected

Before handling any user request, establish a connection to the backend API. Show a brief status like "Connecting...".

If NEMO_TOKEN is in the environment, use it directly and create a session. Otherwise, acquire a free starter token:

• Generate a UUID as client identifier
• POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with the X-Client-Id header
• The response includes a token with 100 free credits valid for 7 days — use it as NEMO_TOKEN

Then create a session by POSTing to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer authorization and body {"task_name":"project","language":"en"}. The session_id in the response is needed for all following requests.

Tell the user you're ready. Keep the technical details out of the chat.

YouTube Image to Video — Convert Images Into YouTube Videos

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

Say you have five landscape photos from a YouTube travel vlog and want to turn these images into a YouTube video with music and transitions — the backend processes it in about 1-2 minutes and hands you a 1080p MP4.

Tip: using 10 or fewer images keeps transitions smooth and processing fast.

Matching Input to Actions

User prompts referencing youtube image to 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.

Headers are derived from this file's YAML frontmatter. X-Skill-Source is youtube-image-to-video, X-Skill-Version comes from the version field, and X-Skill-Platform is detected from the install path (~/.clawhub/ = clawhub, ~/.cursor/skills/ = cursor, otherwise 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.

Translating GUI Instructions

The backend responds as if there's a visual interface. Map its instructions to API calls:

• "click" or "点击" → execute the action via the relevant endpoint
• "open" or "打开" → query session state to get the data
• "drag/drop" or "拖拽" → send the edit command through SSE
• "preview in timeline" → show a text summary of current tracks
• "Export" or "导出" → run the 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)

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "turn these images into a YouTube video with music and transitions" — concrete instructions get better results.

Max file size is 200MB. Stick to JPG, PNG, WEBP, HEIC for the smoothest experience.

Export as MP4 for widest compatibility with YouTube uploads.

Common Workflows

Quick edit: Upload → "turn these images into a YouTube video with music and 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.