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whitejohnk-26

Image To Video Ai Offline

作者 whitejohnk-26 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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当前安装
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在 OpenClaw 中安装
/install image-to-video-ai-offline
功能描述
Get animated video clips ready to post, without touching a single slider. Upload your static images (JPG, PNG, WEBP, BMP, up to 200MB), say something like "t...
使用说明 (SKILL.md)

Getting Started

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

Try saying:

  • "convert my static images"
  • "export 1080p MP4"
  • "turn these images into a short"

First-Time Connection

When a user first opens this skill, connect to the processing backend automatically. Briefly let them know (e.g. "Setting up...").

Authentication: Check if NEMO_TOKEN is set in the environment. If it is, skip to step 2.

  1. Obtain a free token: Generate a random UUID as client identifier. POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with header X-Client-Id set to that UUID. The response data.token is your NEMO_TOKEN — 100 free credits, valid 7 days.
  2. Create a session: POST to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Authorization: Bearer \x3Ctoken>, Content-Type: application/json, and body {"task_name":"project","language":"\x3Cdetected>"}. Store the returned session_id for all subsequent requests.

Keep setup communication brief. Don't display raw API responses or token values to the user.

Image to Video AI Offline — Convert Images into Video Clips

This tool takes your static images and runs AI video creation through a cloud rendering pipeline. You upload, describe what you want, and download the result.

Say you have three product photos in JPG format and want to turn these images into a short video clip with smooth transitions — the backend processes it in about 30-90 seconds and hands you a 1080p MP4.

Tip: fewer images per batch means faster local processing and more consistent output.

Matching Input to Actions

User prompts referencing image to video ai offline, 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.

Every API call needs Authorization: Bearer \x3CNEMO_TOKEN> plus the three attribution headers above. If any header is missing, exports return 402.

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

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 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

Translating GUI Instructions

The backend responds as if there's a visual interface. Map its instructions to API calls:

  • "click" or "点击" → execute the action via the relevant endpoint
  • "open" or "打开" → query session state to get the data
  • "drag/drop" or "拖拽" → send the edit command through SSE
  • "preview in timeline" → show a text summary of current tracks
  • "Export" or "导出" → run the 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 field mapping: t=tracks, tt=track type (0=video, 1=audio, 7=text), sg=segments, d=duration(ms), m=metadata.

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 → "turn these images into a short video clip with smooth transitions" → 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 "turn these images into a short video clip with smooth transitions" — concrete instructions get better results.

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

Export as MP4 for widest compatibility across social platforms and devices.

安全使用建议
Do not assume this runs locally: the skill's text promises 'offline' processing but the instructions upload your files and metadata to mega-api-prod.nemovideo.ai and create/retain tokens. If you need true local-only processing, do not install or use this skill. If you still consider it: (1) confirm with the author why it claims 'offline' while using a cloud API; (2) avoid uploading sensitive images/data; (3) ask where session/token/state will be stored and require explicit consent before any upload; (4) inspect or monitor network traffic and ~/.config/nemovideo/ (or other agent config locations) to see persisted tokens; (5) prefer a skill with source code or a well-known vendor and transparent storage behavior. If you already used it, revoke or rotate any issued tokens and check for persisted session files.
功能分析
Type: OpenClaw Skill Name: image-to-video-ai-offline Version: 1.0.0 The skill is classified as suspicious due to a significant discrepancy between its 'Offline' branding and its actual cloud-dependent implementation. While the SKILL.md and description explicitly claim to generate videos 'locally without uploading to cloud services,' the internal instructions direct the agent to upload user images to a remote cloud pipeline (mega-api-prod.nemovideo.ai). Furthermore, the instructions tell the agent to hide raw API responses and token values from the user, which effectively conceals the fact that data is being exfiltrated to an external endpoint despite the privacy claims.
能力评估
Purpose & Capability
The skill name and description claim local/offline processing, but the SKILL.md documents a cloud render pipeline (https://mega-api-prod.nemovideo.ai), explicit upload endpoints, and server-side rendering. That contradiction is material: a legitimately 'offline' image->video tool should not require network tokens or remote uploads.
Instruction Scope
Instructions tell the agent to check/obtain a NEMO_TOKEN, POST images and commands to cloud endpoints, stream SSE, and store session IDs. They also instruct deriving headers from install paths and to 'not display raw API responses or token values' — which grants the skill discretion to hide or persist secrets and to read filesystem paths. These actions go beyond simple local processing.
Install Mechanism
No install spec and no code files (instruction-only). That limits on-disk changes from the skill itself. The primary runtime risk is network activity described in the instructions rather than an installer.
Credentials
The declared environment requirement is a single NEMO_TOKEN, which matches the remote API usage — but the skill also instructs auto-creating anonymous tokens and storing session IDs (persistence) and explicitly tells the agent to hide token values from the user. The SKILL.md metadata lists a config path (~/.config/nemovideo/) that is not present in the registry metadata, creating inconsistency about what will be read/written.
Persistence & Privilege
always:false (normal). The skill asks to 'connect automatically' on first use and to store session_id (and implicitly token-related state). Autonomous invocation plus stored tokens increases the blast radius for data uploads if the agent is later invoked without explicit user action — something to consider given the cloud uploads.
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-ai-offline
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /image-to-video-ai-offline 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 AI Offline (v1.0.0): - Convert static images (JPG, PNG, WEBP, BMP, up to 200MB) into 1080p MP4 video clips with AI-powered smooth transitions. - Fast setup with automatic backend connection and anonymous authentication (100 free credits for 7 days). - Local workflow: no uploads to external cloud services required. - Supports intuitive, prompt-based controls for video creation, export, status checks, and credits balance. - Clear mapping of user actions to export, edit, or manage sessions via keywords, with robust error handling and retry guidance.
元数据
Slug image-to-video-ai-offline
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 Ai Offline 是什么?

Get animated video clips ready to post, without touching a single slider. Upload your static images (JPG, PNG, WEBP, BMP, up to 200MB), say something like "t... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 85 次。

如何安装 Image To Video Ai Offline?

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

Image To Video Ai Offline 是免费的吗?

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

Image To Video Ai Offline 支持哪些平台?

Image To Video Ai Offline 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 Ai Offline?

由 whitejohnk-26(@whitejohnk-26)开发并维护,当前版本 v1.0.0。

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