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tk8544-b

Image To Video Ltx

作者 tk8544-b · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install image-to-video-ltx
功能描述
Skip the learning curve of professional editing software. Describe what you want — animate this image into a 5-second video clip with smooth motion — and get...
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"

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 LTX — 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 landscape image, ask for animate this image into a 5-second video clip with smooth motion, 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 — clean, high-contrast images with clear subjects produce smoother motion output.

Matching Input to Actions

User prompts referencing image to video ltx, 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.

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

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

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.

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 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)

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 video clip with smooth motion" — concrete instructions get better results.

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

Use PNG for best image quality input to preserve detail in the generated video.

Common Workflows

Quick edit: Upload → "animate this image into a 5-second video clip with smooth motion" → 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 behaves like a normal cloud-rendering integration: it will call https://mega-api-prod.nemovideo.ai to obtain anonymous tokens (if you don't supply NEMO_TOKEN), create sessions, upload your images/audio, and return a downloadable MP4. Before installing: (1) Be comfortable that you will upload media to that external service — avoid sending sensitive/private images. (2) If you prefer control, provide your own NEMO_TOKEN rather than letting the skill obtain an anonymous token. (3) Note the skill may read its own file frontmatter and detect install paths for attribution headers; this is normal but means the agent will access small local metadata. (4) The metadata lists a config path (~/.config/nemovideo/) that the instructions don’t otherwise use — if you are concerned about any local config access, ask the skill author to clarify why that path is declared. Overall the skill appears internally coherent for its stated purpose.
功能分析
Type: OpenClaw Skill Name: image-to-video-ltx Version: 1.0.0 The skill is a functional integration for the NemoVideo AI image-to-video service, allowing users to animate images via the nemovideo.ai API. It includes automated session management, anonymous token acquisition, and clear instructions for handling SSE streams and file uploads. While it performs minor environment fingerprinting for platform attribution (e.g., checking if installed in .clawhub or .cursor), its behavior is entirely consistent with its stated purpose and lacks any indicators of malicious intent or data exfiltration.
能力评估
Purpose & Capability
Name/description match the behavior in SKILL.md. Requiring a NEMO_TOKEN and calling a nemo-video backend is coherent for an image→video rendering service.
Instruction Scope
Runtime instructions are limited to authenticating (using NEMO_TOKEN or acquiring an anonymous token), creating a session, uploading media, streaming SSE responses, polling render status, and returning download URLs. The skill asks the agent to read its own frontmatter and to detect the install path to set X-Skill-Platform — this requires local file/metadata access but is reasonable for attribution. No instructions request unrelated system files, credentials, or broad data collection.
Install Mechanism
Instruction-only skill (no install spec, no code files). Lowest-risk install posture — nothing will be written to disk by an installer step.
Credentials
Only one credential (NEMO_TOKEN) is required and is the primary credential for the backend. The skill will generate an anonymous token from the service if NEMO_TOKEN is absent. Metadata declares a config path (~/.config/nemovideo/) which is not explicitly used in the runtime steps — this is a small metadata inconsistency but not a dangerous mismatch.
Persistence & Privilege
The skill is not always-enabled and does not request persistent platform privileges. It stores session_id for ongoing renders (expected for queued jobs) and does not instruct modifying other skills or system-wide settings.
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-ltx
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /image-to-video-ltx 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 LTX. - Instantly convert still images (JPG, PNG, WEBP, BMP up to 20MB) into animated video clips using cloud GPU AI. - No editing software required: just upload your image and describe the animation you want. - Supports export to 1080p MP4 in 30–90 seconds, ideal for content creators and marketers. - Easy session and authentication setup: free 100-credit token available, managed automatically. - Batch processing, iterative edits, and timeline previews supported within each session. - Simple command mapping for exporting, checking credits, viewing status, and uploading files.
元数据
Slug image-to-video-ltx
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 Ltx 是什么?

Skip the learning curve of professional editing software. Describe what you want — animate this image into a 5-second video clip with smooth motion — and get... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 75 次。

如何安装 Image To Video Ltx?

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

Image To Video Ltx 是免费的吗?

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

Image To Video Ltx 支持哪些平台?

Image To Video Ltx 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 Ltx?

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

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