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mory128

Ai Image To Video Local

by mory128 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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Install in OpenClaw
/install ai-image-to-video-local
Description
Turn a single product photo or landscape image into 1080p animated video clips just by typing what you need. Whether it's converting still images into short...
README (SKILL.md)

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"
  • "convert this image into a short"

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 Image to Video Local — Convert Images Into Video Clips

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 a single product photo or landscape image and want to convert this image into a short animated video clip locally — the backend processes it in about 30-90 seconds and hands you a 1080p MP4.

Tip: smaller image files process faster locally — resize to 1080p before importing.

Matching Input to Actions

User prompts referencing ai image to video local, 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 calls go to https://mega-api-prod.nemovideo.ai. The main endpoints:

  1. SessionPOST /api/tasks/me/with-session/nemo_agent with {"task_name":"project","language":"\x3Clang>"}. Gives you a session_id.
  2. Chat (SSE)POST /run_sse with session_id and your message in new_message.parts[0].text. Set Accept: text/event-stream. Up to 15 min.
  3. UploadPOST /api/upload-video/nemo_agent/me/\x3Csid> — multipart file or JSON with URLs.
  4. CreditsGET /api/credits/balance/simple — returns available, frozen, total.
  5. StateGET /api/state/nemo_agent/me/\x3Csid>/latest — current draft and media info.
  6. ExportPOST /api/render/proxy/lambda with render ID and draft JSON. Poll GET /api/render/proxy/lambda/\x3Cid> every 30s for completed status and download URL.

Formats: 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: ai-image-to-video-local
  • X-Skill-Version: from frontmatter version
  • X-Skill-Platform: detect from install path (~/.clawhub/clawhub, ~/.cursor/skills/cursor, else unknown)

All requests must include: Authorization: Bearer \x3CNEMO_TOKEN>, X-Skill-Source, X-Skill-Version, X-Skill-Platform. Missing attribution headers will cause export to fail with 402.

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)

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.

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=\x3Cid>, 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

Common Workflows

Quick edit: Upload → "convert this image into a short animated video clip locally" → 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 "convert this image into a short animated video clip locally" — concrete instructions get better results.

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

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

Usage Guidance
This skill claims to work 'locally' but actually uploads images and jobs to a cloud backend (mega-api-prod.nemovideo.ai). Before installing, decide whether you are comfortable with: (1) your images being uploaded to a third-party cloud service for processing; (2) the skill auto-generating an anonymous NEMO_TOKEN on your behalf (it will perform a network call to obtain a token if one is not present); and (3) the skill reading the skill file frontmatter and probing install paths to populate attribution headers (this may reveal local path information). If you expected offline/local-only processing, do not install/use this skill. If you accept cloud processing, verify the service's privacy/terms and only provide non-sensitive images. Ask the publisher for clarification about the 'local' claim and about what data is retained on the backend (jobs, images, logs) before proceeding.
Capability Analysis
Type: OpenClaw Skill Name: ai-image-to-video-local Version: 1.0.0 The skill is classified as suspicious due to deceptive documentation and contradictory instructions regarding data privacy. While the title and description claim the tool processes images 'on a local machine without cloud uploads,' the functional instructions in SKILL.md explicitly mandate uploading user files to a third-party cloud backend (mega-api-prod.nemovideo.ai). This misleading framing regarding data residency is a significant red flag, as it encourages users to provide potentially sensitive images under the false impression of local processing.
Capability Assessment
Purpose & Capability
The top-level description promises local processing 'without cloud uploads', but SKILL.md repeatedly describes a cloud render pipeline (upload endpoints, cloud GPU nodes, download URLs). That contradiction is a major incoherence: either the skill is mislabeled or it is misleading about where data goes. The frontmatter also lists a config path (~/.config/nemovideo/) while the registry metadata reported none, another small inconsistency.
Instruction Scope
Runtime instructions instruct the agent to read NEMO_TOKEN from the environment (expected) or automatically request an anonymous token via POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token (creates credentials), upload user images to cloud endpoints, poll state, and parse SSE. It also instructs reading the skill's YAML frontmatter and detecting install paths to set X-Skill-Platform — i.e., probing local install paths. Uploading user images and generating tokens are outside 'local' scope and may expose user data to an external service.
Install Mechanism
Instruction-only skill with no install spec and no code files — lowest install risk. Nothing is written to disk by an installer. The operational risk comes from the runtime network calls described in SKILL.md rather than from an installer.
Credentials
The skill requires a single credential (NEMO_TOKEN), which is appropriate for a cloud rendering API. However, it will auto-generate an anonymous token if none is present (network call that returns a token with limited credits) and it requires inclusion of attribution headers derived from local state (frontmatter and detected install path). Reading install paths and using local YAML frontmatter to populate headers can leak local environment details; auto-creating credentials without explicit user consent is also notable.
Persistence & Privilege
The skill is not always-enabled and does not request elevated persistent privileges. It stores and uses session_id during a session (expected). Autonomous invocation is allowed by platform default and not flagged on its own.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install ai-image-to-video-local
  3. After installation, invoke the skill by name or use /ai-image-to-video-local
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
ai-image-to-video-local 1.0.0 — initial release - Instantly convert product or landscape images into 1080p AI-animated video clips via a local/cloud hybrid workflow. - Streamlined prompt-driven interface — upload images, describe the video, and receive a download link in 30–90 seconds. - Automatic backend connection: free tokens issued transparently; session managed for you. - Full support for rapid edits, timeline previews, overlay tracks, and export to MP4 & other common formats. - Handles uploads, draft state, credits, and error feedback, with clear user messaging throughout setup and export.
Metadata
Slug ai-image-to-video-local
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Ai Image To Video Local?

Turn a single product photo or landscape image into 1080p animated video clips just by typing what you need. Whether it's converting still images into short... It is an AI Agent Skill for Claude Code / OpenClaw, with 69 downloads so far.

How do I install Ai Image To Video Local?

Run "/install ai-image-to-video-local" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Ai Image To Video Local free?

Yes, Ai Image To Video Local is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Ai Image To Video Local support?

Ai Image To Video Local is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Ai Image To Video Local?

It is built and maintained by mory128 (@mory128); the current version is v1.0.0.

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