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tk8544-b

Image To Video Kissing

by tk8544-b · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install image-to-video-kissing
Description
Get animated kissing clip ready to post, without touching a single slider. Upload your still images (JPG, PNG, WEBP, HEIC, up to 200MB), say something like "...
README (SKILL.md)

Getting Started

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

Try saying:

  • "animate my still images"
  • "export 1080p MP4"
  • "animate this image with a kissing"

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: \x3Cuuid>. 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 Kissing — Animate photos into video clips

Send me your still 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 single photo of two people facing each other, type "animate this image with a kissing motion between the two subjects", and you'll get a 1080p MP4 back in roughly 30-60 seconds. All rendering happens server-side.

Worth noting: images with clear subject faces and good lighting produce the most natural-looking animation.

Matching Input to Actions

User prompts referencing image to video kissing, 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.

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

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "animate this image with a kissing motion between the two subjects" — 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 across social platforms.

Common Workflows

Quick edit: Upload → "animate this image with a kissing motion between the two subjects" → Download MP4. Takes 30-60 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.

Usage Guidance
Before installing, be comfortable sending your uploaded images to the NemoVideo cloud API and using a service token for rendering credits. Do not upload images of people without consent, and avoid sharing sensitive photos or token values in chat or logs.
Capability Analysis
Type: OpenClaw Skill Name: image-to-video-kissing Version: 1.0.0 The skill is a functional wrapper for the 'nemovideo.ai' cloud service, designed to animate images into videos. It handles authentication via the NEMO_TOKEN environment variable or an anonymous token acquisition process, and manages media processing through standard REST API and SSE endpoints at mega-api-prod.nemovideo.ai. The instructions in SKILL.md are consistent with the stated purpose and do not contain evidence of data exfiltration, malicious execution, or harmful prompt injection.
Capability Assessment
Purpose & Capability
The described capability is coherent with the instructions: uploaded images are sent to a cloud rendering API to create 1080p video clips. Because the content may include identifiable people and romantic/kissing animation, users should consider consent and privacy before use.
Instruction Scope
The skill directs the agent to automatically create/connect a remote session and translate backend GUI-like responses into API actions. This is purpose-aligned for a hosted video editor, but users should be aware that actions such as upload, render, export, polling, and credit checks may happen through the remote service.
Install Mechanism
There is no install spec and no code files, so there is no local executable to inspect. The registry source is unknown and there is no homepage, which limits independent provenance review of the remote service.
Credentials
The skill uses a NEMO_TOKEN credential or creates an anonymous token for the NemoVideo API. It does not request broad local filesystem or OS access in the provided artifacts, and its network/API use is proportional to the cloud-rendering purpose.
Persistence & Privilege
The skill stores/uses a session_id for the remote render workflow and notes that closing the tab can orphan a job. No local background process, self-persistence, or privilege escalation is shown.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install image-to-video-kissing
  3. After installation, invoke the skill by name or use /image-to-video-kissing
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of "Image to Video Kissing": - Instantly animates still images of two people into realistic kissing motion video clips. - Simple workflow: upload JPG, PNG, WEBP, or HEIC (up to 200MB), describe the animation, and receive a 1080p MP4. - Automatic first-time setup: connects to cloud API with UUID-based token if needed. - Action commands for exporting, checking credits, status, uploading, and animation generation. - All processing occurs on remote GPU nodes; no local installations required. - Clear error codes, supported formats, and troubleshooting steps included.
Metadata
Slug image-to-video-kissing
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Image To Video Kissing?

Get animated kissing clip ready to post, without touching a single slider. Upload your still images (JPG, PNG, WEBP, HEIC, up to 200MB), say something like "... It is an AI Agent Skill for Claude Code / OpenClaw, with 40 downloads so far.

How do I install Image To Video Kissing?

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

Is Image To Video Kissing free?

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

Which platforms does Image To Video Kissing support?

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

Who created Image To Video Kissing?

It is built and maintained by tk8544-b (@tk8544-b); the current version is v1.0.0.

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