Image To Video Llm

Skip the learning curve of professional editing software. Describe what you want — turn this image into a 5-second animated video clip with smooth motion — a...

Security Scan

The skill's requests and runtime instructions are consistent with its stated purpose (cloud image→video conversion) and only ask for a single service token and uploads to the described API, but the service is third‑party and the skill source is unknown so exercise normal caution before sending private images or long‑lived credentials.

Purpose & Capability

Name and description (convert still images into short videos) align with the declared requirement (NEMO_TOKEN) and the endpoints described in SKILL.md. The required env var and the listed config path (~/.config/nemovideo/) are proportionate to a cloud video service.

Instruction Scope

Instructions are focused on authenticating, creating a session, uploading media, streaming SSE responses, and starting renders on mega-api-prod.nemovideo.ai — all consistent with the stated purpose. Minor scope notes: SKILL.md instructs the agent to read this file's YAML frontmatter for attribution and to detect the agent install path (~/.clawhub/ or ~/.cursor/skills/) to set X-Skill-Platform, which probes the agent filesystem; it also instructs generating and persisting an anonymous token if NEMO_TOKEN is absent. The skill does not instruct reading unrelated system files or other credentials.

Install Mechanism

No install spec or code files are present (instruction-only), so nothing is downloaded or written to disk by the skill itself. This minimizes supply-chain/install risk.

Credentials

Only NEMO_TOKEN is declared as required (primary credential), which matches the API usage described. The skill also documents a flow to create an anonymous token via an API call (100 credits, 7‑day expiry) — this is a reasonable fallback but means temporary tokens will be created if NEMO_TOKEN is not preconfigured. Consider whether you want to provide a long‑lived production token vs. using ephemeral anonymous tokens for privacy.

Persistence & Privilege

always is false and there is no install behavior that modifies other skills or global agent settings. The skill does ask to save session_id and tokens for session management (expected for this service) but does not request elevated or permanent platform privileges.

Assessment

This skill appears to do what it says: it will upload images to a third‑party API (mega-api-prod.nemovideo.ai) and requires a NEMO_TOKEN (or will obtain a short‑lived anonymous token). Before installing/use: (1) Confirm you trust the remote service and its privacy policy — the skill will transmit your images to that API. (2) Prefer using an ephemeral or limited‑scope token rather than a sensitive long‑lived account token. (3) Be aware the skill may probe its install path to set an attribution header (reads certain home paths), which is minor but leaks which agent platform is used. (4) The skill source and homepage are unknown — if you need stronger assurance, request vendor info or a privacy/security statement before sending sensitive content.

Getting Started

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

Try saying:

• "convert my still images"
• "export 1080p MP4"
• "turn this image into a 5-second"

Automatic Setup

On first interaction, connect to the processing API before doing anything else. Show a brief status like "Setting things up...".

Token: If NEMO_TOKEN environment variable is already set, use it and skip to Session below.

Free token: Generate a UUID as client identifier, then POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with header X-Client-Id: <uuid>. The response field data.token becomes your NEMO_TOKEN (100 credits, 7-day expiry).

Session: POST to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer auth and body {"task_name":"project"}. Save session_id from the response.

Confirm to the user you're connected and ready. Don't print tokens or raw JSON.

Image to Video LLM — Convert Images Into Video Clips

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

Here's a typical use: you send a a single product photo or landscape image, ask for turn this image into a 5-second animated video clip with smooth motion, and about 30-90 seconds later you've got a MP4 file ready to download. The whole thing runs at 1080p by default.

One thing worth knowing — high-contrast images with clear subjects produce smoother, more realistic motion.

Matching Input to Actions

User prompts referencing image to video llm, 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: image-to-video-llm
• X-Skill-Version: from frontmatter version
• X-Skill-Platform: detect from install path (~/.clawhub/ → clawhub, ~/.cursor/skills/ → cursor, else unknown)

Include Authorization: Bearer <NEMO_TOKEN> and all attribution headers on every request — omitting them triggers a 402 on export.

Error Handling

Code	Meaning	Action
0	Success	Continue
1001	Bad/expired token	Re-auth via anonymous-token (tokens expire after 7 days)
1002	Session not found	New session §3.0
2001	No credits	Anonymous: show registration URL with ?bind=<id> (get <id> from create-session or state response when needed). Registered: "Top up credits in your account"
4001	Unsupported file	Show supported formats
4002	File too large	Suggest compress/trim
400	Missing X-Client-Id	Generate Client-Id and retry (see §1)
402	Free plan export blocked	Subscription tier issue, NOT credits. "Register or upgrade your plan to unlock export."
429	Rate limit (1 token/client/7 days)	Retry in 30s once

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.

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 → "turn this image into a 5-second animated video clip with smooth motion" → Download MP4. Takes 30-90 seconds 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 this image into a 5-second animated video clip with smooth motion" — concrete instructions get better results.

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

Use PNG for source images to preserve quality before AI processing.