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Image To Video End Frame

作者 mory128 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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当前安装
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在 OpenClaw 中安装
/install image-to-video-end-frame
功能描述
generate images into image-ending video with this skill. Works with JPG, PNG, WEBP, HEIC files up to 200MB. marketers, social media creators, animators use i...
使用说明 (SKILL.md)

Getting Started

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

Try saying:

  • "generate my images"
  • "export 1080p MP4"
  • "generate a video that ends on"

Getting Connected

Before handling any user request, establish a connection to the backend API. Show a brief status like "Connecting...".

If NEMO_TOKEN is in the environment, use it directly and create a session. Otherwise, acquire a free starter token:

  • Generate a UUID as client identifier
  • POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with the X-Client-Id header
  • The response includes a token with 100 free credits valid for 7 days — use it as NEMO_TOKEN

Then create a session by POSTing to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer authorization and body {"task_name":"project","language":"en"}. The session_id in the response is needed for all following requests.

Tell the user you're ready. Keep the technical details out of the chat.

Image to Video End Frame — Generate Videos With Controlled End Frame

Send me your 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 product photo used as the final frame of a video, type "generate a video that ends on this image as the last frame", and you'll get a 1080p MP4 back in roughly 30-60 seconds. All rendering happens server-side.

Worth noting: high-contrast images with clear subjects work best as end frames for smooth transitions.

Matching Input to Actions

User prompts referencing image to video end frame, 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 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.

Skill attribution — read from this file's YAML frontmatter at runtime:

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

API base: https://mega-api-prod.nemovideo.ai

Create session: POST /api/tasks/me/with-session/nemo_agent — body {"task_name":"project","language":"\x3Clang>"} — returns task_id, session_id.

Send message (SSE): POST /run_sse — body {"app_name":"nemo_agent","user_id":"me","session_id":"\x3Csid>","new_message":{"parts":[{"text":"\x3Cmsg>"}]}} with Accept: text/event-stream. Max timeout: 15 minutes.

Upload: POST /api/upload-video/nemo_agent/me/\x3Csid> — file: multipart -F "files=@/path", or URL: {"urls":["\x3Curl>"],"source_type":"url"}

Credits: GET /api/credits/balance/simple — returns available, frozen, total

Session state: GET /api/state/nemo_agent/me/\x3Csid>/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_\x3Cts>","sessionId":"\x3Csid>","draft":\x3Cjson>,"output":{"format":"mp4","quality":"high"}}. Poll GET /api/render/proxy/lambda/\x3Cid> 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.

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=\x3Cid> (get \x3Cid> 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

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

SSE Event Handling

Event Action
Text response Apply GUI translation (§4), present to user
Tool call/result Process internally, don't forward
heartbeat / empty data: Keep waiting. Every 2 min: "⏳ Still working..."
Stream closes Process final response

~30% of editing operations return no text in the SSE stream. When this happens: poll session state to verify the edit was applied, then summarize changes to the user.

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)

Common Workflows

Quick edit: Upload → "generate a video that ends on this image as the last frame" → 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.

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "generate a video that ends on this image as the last frame" — 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.

安全使用建议
Install this skill only if you are comfortable with nemovideo.ai receiving your media and prompts for cloud rendering. Use a dedicated token if possible, avoid confidential images, and ask the agent to confirm before uploading or exporting files.
功能分析
Type: OpenClaw Skill Name: image-to-video-end-frame Version: 1.0.0 The skill is a functional integration for the NemoVideo AI video generation service (mega-api-prod.nemovideo.ai). It provides instructions for the agent to manage authentication, sessions, file uploads, and video rendering. While it includes logic for environment fingerprinting (detecting install paths like ~/.cursor/skills/) and uses Server-Sent Events (SSE) to receive instructions from a remote backend, these behaviors are consistent with its stated purpose of providing a cloud-based video editing service. No evidence of malicious intent, such as credential theft or unauthorized local execution, was found in SKILL.md or _meta.json.
能力评估
Purpose & Capability
The image-to-video purpose is coherent with the documented cloud GPU rendering workflow, but the workflow necessarily sends prompts and media to a third-party service.
Instruction Scope
The skill routes user intents to upload, SSE, state, credits, and export API actions, and it can translate backend GUI-style messages into API calls; this appears purpose-aligned but users may want explicit confirmation for uploads and exports.
Install Mechanism
No install script, binaries, or code files are present; the reviewed artifact is instruction-only.
Credentials
Use of NEMO_TOKEN, backend sessions, and remote file upload is proportionate for a cloud rendering skill, but it exposes user media and prompts to the provider.
Persistence & Privilege
The skill creates remote sessions and may use anonymous tokens valid for 7 days; no local background process, self-persistence, or elevated OS privilege 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-end-frame
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /image-to-video-end-frame 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 — generate videos from images with a controlled end frame. - Supports JPG, PNG, WEBP, HEIC uploads up to 200MB for video creation. - Session-based GPU video rendering; receive 1080p MP4 output in 30–90 seconds. - Automatic handling of authentication and session setup, including free credit tokens. - Workflow includes image upload, AI-powered animation toward an ending image, and easy export. - Built-in error handling for authentication, file limits, format issues, and export permissions. - Summarizes timeline edits and guides users with clear status messages throughout the process.
元数据
Slug image-to-video-end-frame
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 End Frame 是什么?

generate images into image-ending video with this skill. Works with JPG, PNG, WEBP, HEIC files up to 200MB. marketers, social media creators, animators use i... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 34 次。

如何安装 Image To Video End Frame?

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

Image To Video End Frame 是免费的吗?

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

Image To Video End Frame 支持哪些平台?

Image To Video End Frame 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 End Frame?

由 mory128(@mory128)开发并维护,当前版本 v1.0.0。

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