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Ai Video Leonardo

作者 mhogan2013-9 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install ai-video-leonardo
功能描述
Skip the learning curve of professional editing software. Describe what you want — animate this image into a cinematic 5-second video clip — and get AI-gener...
使用说明 (SKILL.md)

Getting Started

Got images or prompts to work with? Send it over and tell me what you need — I'll take care of the AI video generation.

Try saying:

  • "generate a landscape photo with a motion prompt into a 1080p MP4"
  • "animate this image into a cinematic 5-second video clip"
  • "generating short video clips from images using Leonardo AI for digital creators and artists"

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.

AI Video Leonardo — Generate Videos from Images

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

Say you have a landscape photo with a motion prompt and want to animate this image into a cinematic 5-second video clip — the backend processes it in about 1-2 minutes and hands you a 1080p MP4.

Tip: simpler image compositions produce smoother and more consistent motion results.

Matching Input to Actions

User prompts referencing ai video leonardo, 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 ai-video-leonardo, 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).

Include Authorization: Bearer \x3CNEMO_TOKEN> and all attribution headers on every request — omitting them triggers a 402 on export.

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)

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.

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 into a cinematic 5-second video clip" — concrete instructions get better results.

Max file size is 200MB. Stick to JPG, PNG, WebP, MP4 for the smoothest experience.

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

Common Workflows

Quick edit: Upload → "animate this image into a cinematic 5-second video clip" → Download MP4. Takes 1-2 minutes 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, make sure you are comfortable sending selected images, videos, audio, and prompts to mega-api-prod.nemovideo.ai. Use a dedicated NEMO_TOKEN or the anonymous token flow, protect the token, and ask for confirmation before exports if credit usage matters.
功能分析
Type: OpenClaw Skill Name: ai-video-leonardo Version: 1.0.0 The skill facilitates AI video generation by interfacing with the nemovideo.ai API. It contains detailed instructions for the agent to handle session management, file uploads, and token acquisition (including an automated anonymous token flow). While it requests environment variable access (NEMO_TOKEN) and performs network requests to mega-api-prod.nemovideo.ai, these actions are directly related to its stated functionality. No indicators of malicious intent, such as data exfiltration or unauthorized command execution, were identified in SKILL.md or _meta.json.
能力评估
Purpose & Capability
The stated purpose is AI video generation from user-provided images/prompts, and the visible instructions consistently route those inputs to a cloud render API for upload, editing, export, and download.
Instruction Scope
The skill instructs the agent to automatically connect to the provider API and translate some backend GUI-style responses into API actions; this is aligned with the video workflow but users should understand it may act within the provider session.
Install Mechanism
No install spec or code files are present, and the static scanner had no code to analyze.
Credentials
Use of NEMO_TOKEN, an anonymous token flow, and media uploads up to 200MB is proportionate for a cloud video-rendering service, but it means provider credentials and user media are involved.
Persistence & Privilege
The skill uses a provider session_id and references session state/render jobs, but the visible artifacts do not show local background persistence or privileged local execution.
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 ai-video-leonardo
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /ai-video-leonardo 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
AI Video Leonardo 1.0.0 — Initial Release - Instantly generate cinematic video clips from images or prompts with AI, no editing skills needed. - Supports JPG, PNG, WebP, and MP4 uploads up to 200MB; outputs in popular video formats (e.g., MP4). - Simple session and token setup with free credits available for new users. - User requests (e.g., generate, upload, export, check credits) routed and handled automatically. - Cloud GPU rendering delivers 1080p video clips in about 1-2 minutes. - Includes error handling, session management, and tips for getting the best results.
元数据
Slug ai-video-leonardo
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

Ai Video Leonardo 是什么?

Skip the learning curve of professional editing software. Describe what you want — animate this image into a cinematic 5-second video clip — and get AI-gener... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 32 次。

如何安装 Ai Video Leonardo?

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

Ai Video Leonardo 是免费的吗?

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

Ai Video Leonardo 支持哪些平台?

Ai Video Leonardo 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 Ai Video Leonardo?

由 mhogan2013-9(@mhogan2013-9)开发并维护,当前版本 v1.0.0。

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