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Ai Image To Video Models

v1.0.0

Turn a single product photo or illustrated scene into 1080p animated video clips just by typing what you need. Whether it's turning static images into short...

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Install

OpenClaw Prompt Flow

Install with OpenClaw

Best for remote or guided setup. Copy the exact prompt, then paste it into OpenClaw for linmillsd7/ai-image-to-video-models.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "Ai Image To Video Models" (linmillsd7/ai-image-to-video-models) from ClawHub.
Skill page: https://clawhub.ai/linmillsd7/ai-image-to-video-models
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Required env vars: NEMO_TOKEN
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

CLI Commands

Use the direct CLI path if you want to install manually and keep every step visible.

OpenClaw CLI

Bare skill slug

openclaw skills install ai-image-to-video-models

ClawHub CLI

Package manager switcher

npx clawhub@latest install ai-image-to-video-models
Security Scan
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Purpose & Capability
The name/description (image→video via remote GPU service) aligns with the runtime instructions and the single required credential (NEMO_TOKEN). However the SKILL.md frontmatter declares a config path (~/.config/nemovideo/) and logic to detect an install path to populate attribution headers — these were not listed in the registry's required config paths, which is an inconsistency.
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Instruction Scope
Most instructions stay on-topic (create/use a NEMO_TOKEN, create session, upload file, stream SSE, poll render). But the skill asks the agent to derive X-Skill-Platform from install paths (e.g., '~/.clawhub/' or '~/.cursor/skills/') and references a local config directory. Detecting or reading these local paths is outside the core image→video function and could require the agent to probe the user's filesystem — this is scope creep and should be constrained or clarified.
Install Mechanism
Instruction-only skill with no install spec and no code files; lowest install risk. All network calls are to the documented nemovideo API domain, not to arbitrary URLs or third-party installers.
Credentials
Only one credential is requested (NEMO_TOKEN), which is proportionate for a remote API service. Note the SKILL.md frontmatter lists a config path (~/.config/nemovideo/); the registry metadata reported no required config paths — this mismatch should be resolved. The skill also offers to mint an anonymous token via the public endpoint (no user secret required), which is reasonable but be aware these tokens can carry credits/usage.
Persistence & Privilege
Skill is not always-enabled and has no install/persistence behavior. It does not request elevated or permanent system privileges.
What to consider before installing
This skill's behavior is largely coherent for a cloud image→video service, but check two things before enabling it: (1) Confirm you are comfortable giving/using a NEMO_TOKEN (or allowing the skill to create an anonymous token) for the nemovideo.ai domain. (2) Ask the maintainer to explain and remove any need for the agent to probe local paths (~/.config/nemovideo/, ~/.clawhub/, ~/.cursor/skills/) — file-system probing is not required for core functionality and increases privacy risk. Also resolve the registry vs SKILL.md metadata mismatch (declared config paths), and only proceed if you trust the remote API endpoint and its operator. If you need higher assurance, request the skill author add explicit, minimal instructions for where session data is stored and limit any local file access.

Like a lobster shell, security has layers — review code before you run it.

Runtime requirements

🖼️ Clawdis
EnvNEMO_TOKEN
Primary envNEMO_TOKEN
latestvk97fsgad38g50tyb8n17ar4vws84ybkv
57downloads
0stars
1versions
Updated 1w ago
v1.0.0
MIT-0

Getting Started

Send me your still images and I'll handle the AI video generation. Or just describe what you're after.

Try saying:

  • "convert a single product photo or illustrated scene into a 1080p MP4"
  • "animate this image into a 5-second video with smooth motion"
  • "turning static images into short animated videos for marketers, social media creators, designers"

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.

AI Image to Video Models — Convert Images Into Video Clips

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

A quick example: upload a single product photo or illustrated scene, type "animate this image into a 5-second video with smooth motion", and you'll get a 1080p MP4 back in roughly 30-90 seconds. All rendering happens server-side.

Worth noting: high-contrast images with clear subjects produce the smoothest motion results.

Matching Input to Actions

User prompts referencing ai image to video models, aspect ratio, text overlays, or audio tracks get routed to the corresponding action via keyword and intent classification.

User says...ActionSkip 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.

Headers are derived from this file's YAML frontmatter. X-Skill-Source is ai-image-to-video-models, 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).

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

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.

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 saysYou 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)

Error Handling

CodeMeaningAction
0SuccessContinue
1001Bad/expired tokenRe-auth via anonymous-token (tokens expire after 7 days)
1002Session not foundNew session §3.0
2001No creditsAnonymous: show registration URL with ?bind=<id> (get <id> from create-session or state response when needed). Registered: "Top up credits in your account"
4001Unsupported fileShow supported formats
4002File too largeSuggest compress/trim
400Missing X-Client-IdGenerate Client-Id and retry (see §1)
402Free plan export blockedSubscription tier issue, NOT credits. "Register or upgrade your plan to unlock export."
429Rate limit (1 token/client/7 days)Retry in 30s once

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "animate this image into a 5-second video with smooth motion" — concrete instructions get better results.

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

PNG images with clean backgrounds give the AI more accurate motion generation.

Common Workflows

Quick edit: Upload → "animate this image into a 5-second video 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.

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