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dsewell-583h0

Image To Video Leonardo Ai

作者 dsewell-583h0 · 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-leonardo-ai
功能描述
Get animated video clips ready to post, without touching a single slider. Upload your still images (JPG, PNG, WEBP, BMP, up to 20MB), say something like "ani...
使用说明 (SKILL.md)

Getting Started

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

Try saying:

  • "convert my still images"
  • "export 1080p MP4"
  • "animate this image into a 5-second"

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 Leonardo AI — Convert Images into Video Clips

Drop your still images in the chat and tell me what you need. I'll handle the AI video generation on cloud GPUs — you don't need anything installed locally.

Here's a typical use: you send a a single product photo or illustrated scene, ask for animate this image into a 5-second cinematic video clip, and about 30-90 seconds later you've got a MP4 file ready to download. The whole thing runs at 1080p by default.

One thing worth knowing — images with clear subjects and simple backgrounds produce the smoothest motion results.

Matching Input to Actions

User prompts referencing image to video leonardo ai, 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.

Base URL: https://mega-api-prod.nemovideo.ai

Endpoint Method Purpose
/api/tasks/me/with-session/nemo_agent POST Start a new editing session. Body: {"task_name":"project","language":"\x3Clang>"}. Returns session_id.
/run_sse POST Send a user message. Body includes app_name, session_id, new_message. Stream response with Accept: text/event-stream. Timeout: 15 min.
/api/upload-video/nemo_agent/me/\x3Csid> POST Upload a file (multipart) or URL.
/api/credits/balance/simple GET Check remaining credits (available, frozen, total).
/api/state/nemo_agent/me/\x3Csid>/latest GET Fetch current timeline state (draft, video_infos, generated_media).
/api/render/proxy/lambda POST Start export. Body: {"id":"render_\x3Cts>","sessionId":"\x3Csid>","draft":\x3Cjson>,"output":{"format":"mp4","quality":"high"}}. Poll status every 30s.

Accepted file types: 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-leonardo-ai, 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.

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

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.

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

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)

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "animate this image into a 5-second cinematic video clip" — concrete instructions get better results.

Max file size is 20MB. Stick to JPG, PNG, WEBP, BMP for the smoothest experience.

Use PNG for input images to preserve quality and avoid compression artifacts in the output video.

Common Workflows

Quick edit: Upload → "animate this image into a 5-second cinematic video clip" → 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.

安全使用建议
This skill appears to do what it says: it uploads images to a cloud rendering API (mega-api-prod.nemovideo.ai) using a NEMO_TOKEN or an anonymous token it can obtain for you. Before installing, confirm you are comfortable with your images being sent to that external service (do not upload sensitive images). Verify the domain and service legitimacy if you plan to provide a permanent NEMO_TOKEN (use least-privilege/replaceable tokens). Ask the author to explain the YAML configPaths vs registry listing mismatch (~/.config/nemovideo/) and whether the agent will read local install paths — if you want to avoid any local filesystem reads, request removal of install-path detection. Finally, check the service's privacy/terms if you need guaranteed deletion, ownership, or retention policies for uploaded media.
功能分析
Type: OpenClaw Skill Name: image-to-video-leonardo-ai Version: 1.0.0 The skill exhibits a significant discrepancy between its branding ('Leonardo AI') and its actual backend implementation, which routes all user data and images to an unrelated third-party domain ('nemovideo.ai'). It automatically handles authentication by either exfiltrating the 'NEMO_TOKEN' environment variable or generating anonymous tokens via 'mega-api-prod.nemovideo.ai', as detailed in SKILL.md. While the functionality aligns with video generation, the potential for brand impersonation to facilitate unauthorized data collection makes it suspicious.
能力评估
Purpose & Capability
Name/description match the runtime instructions: the SKILL.md describes uploading images, creating sessions, streaming events, rendering, and returning MP4s from an external API (mega-api-prod.nemovideo.ai). Requiring a single service token (NEMO_TOKEN) is proportionate to this purpose.
Instruction Scope
Instructions instruct the agent to POST/upload files and stream SSE responses to the nemo video backend and to include attribution headers. They also describe detecting an install path to set X-Skill-Platform and reference an optional local config path in YAML metadata. These actions are consistent with a cloud render flow but imply the agent will read runtime context (install path) and transmit user files to an external service — review if that data exfiltration is acceptable for your use case.
Install Mechanism
No install spec or code files are present (instruction-only), so nothing is downloaded or written to disk by an installer. This minimizes install-time risk.
Credentials
Only NEMO_TOKEN is declared as required and is used to authorize API calls; the skill will obtain an anonymous token via the public API if NEMO_TOKEN is absent. This is proportionate. However, the SKILL.md YAML includes a configPaths entry (~/.config/nemovideo/) whereas the registry summary lists none — this metadata mismatch should be clarified before trusting any local config access.
Persistence & Privilege
always is false and there is no install-time persistence requested. The skill can be invoked autonomously by the agent (platform default), which is expected for skills of this type and is not in itself a concern.
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-leonardo-ai
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /image-to-video-leonardo-ai 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 — turn still images into downloadable video clips with cloud AI animation. - Upload JPG, PNG, WEBP, or BMP images (up to 20MB) to generate 1080p MP4 video clips. - Simple text prompts (e.g., "animate this into a 5-second clip") trigger the video creation process. - Automatic connection and session management; easily use a free token if not already configured. - Check credits, export status, and manage video sessions directly in chat. - Clear error handling for expired tokens, missing credits, or unsupported file types. - Tailored workflows for digital creators to animate, edit, and export videos—no manual animation skills needed.
元数据
Slug image-to-video-leonardo-ai
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 Leonardo Ai 是什么?

Get animated video clips ready to post, without touching a single slider. Upload your still images (JPG, PNG, WEBP, BMP, up to 20MB), say something like "ani... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 89 次。

如何安装 Image To Video Leonardo Ai?

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

Image To Video Leonardo Ai 是免费的吗?

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

Image To Video Leonardo Ai 支持哪些平台?

Image To Video Leonardo Ai 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 Leonardo Ai?

由 dsewell-583h0(@dsewell-583h0)开发并维护,当前版本 v1.0.0。

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