← 返回 Skills 市场
tk8544-b

Image To Video Kissing

作者 tk8544-b · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
40
总下载
0
收藏
0
当前安装
1
版本数
在 OpenClaw 中安装
/install image-to-video-kissing
功能描述
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 "...
使用说明 (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.

安全使用建议
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.
功能分析
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.
能力评估
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.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install image-to-video-kissing
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /image-to-video-kissing 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
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.
元数据
Slug image-to-video-kissing
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Image To Video Kissing 是什么?

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 "... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 40 次。

如何安装 Image To Video Kissing?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install image-to-video-kissing」即可一键安装,无需额外配置。

Image To Video Kissing 是免费的吗?

是的,Image To Video Kissing 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Image To Video Kissing 支持哪些平台?

Image To Video Kissing 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Image To Video Kissing?

由 tk8544-b(@tk8544-b)开发并维护,当前版本 v1.0.0。

💬 留言讨论