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mhogan2013-9

Image To Video Kling Ai

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

Getting Started

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

Try saying:

  • "convert a single product photo or illustrated scene into a 1080p MP4"
  • "animate this image into a 5-second cinematic video clip"
  • "turning still images into short AI-generated video clips for content creators, marketers, social media managers"

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 Kling AI — Convert Images into Video Clips

Send me your images and describe the result you want. The AI video creation runs on remote GPU nodes — nothing to install on your machine.

A quick example: upload a single product photo or illustrated scene, type "animate this image into a 5-second cinematic video clip", and you'll get a 1080p MP4 back in roughly 1-2 minutes. All rendering happens server-side.

Worth noting: high-contrast images with clear subjects produce the most fluid motion results.

Matching Input to Actions

User prompts referencing image to video kling 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.

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-kling-ai
  • 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 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

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 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 → "animate this image into a 5-second cinematic 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.

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 10MB. Stick to JPG, PNG, WEBP, BMP for the smoothest experience.

Use PNG for input images to preserve quality and avoid compression artifacts.

安全使用建议
This skill uploads your images and prompts to an external service (mega-api-prod.nemovideo.ai) and needs an API token (NEMO_TOKEN). It will automatically request a short-lived anonymous token if you don't provide one and will persist session IDs/tokens for continuing jobs. Before installing: (1) Consider privacy — do not upload images containing sensitive personal or proprietary data unless you trust the service and its retention policy; (2) Decide whether to supply your own NEMO_TOKEN or allow the skill to auto-create one; supplying your own gives you more control over token lifecycle; (3) Check whether the skill writes to ~/.config/nemovideo/ on your machine (it likely will store session state/tokens there); (4) If you need stronger guarantees, inspect network logs or run the skill in a sandboxed environment; (5) If you have concerns about the service, avoid installing or limit the skill's use to non-sensitive content.
功能分析
Type: OpenClaw Skill Name: image-to-video-kling-ai Version: 1.0.0 The skill instructions in SKILL.md direct the AI agent to perform automated background network requests to a third-party API (mega-api-prod.nemovideo.ai) and to probe the local filesystem (checking for ~/.clawhub/ and ~/.cursor/skills/ paths) to identify the host platform. While these actions are aligned with the stated image-to-video functionality, the use of automated background connectivity and environment discovery via filesystem access are risky capabilities that warrant a suspicious classification.
能力评估
Purpose & Capability
The skill's name/description (convert images to short videos) aligns with the actions described in SKILL.md: uploading images, creating sessions, submitting render jobs, polling for results. The declared config path (~/.config/nemovideo/) and primary credential (NEMO_TOKEN) match the stated remote service.
Instruction Scope
Instructions tell the agent to POST to nemovideo.ai endpoints to obtain anonymous tokens (if NEMO_TOKEN is not set), create sessions, upload files, send SSE generation messages, and poll render status. These network actions are expected for a cloud rendering service. Notable: the skill auto-generates/obtains a token and instructs storing the session_id/token for future requests (it also tells the agent not to display raw tokens). Automatic token acquisition and token/session persistence are behavior users should be aware of.
Install Mechanism
No install spec or code files are present (instruction-only), so nothing is written to disk by an installer. This is the lowest install risk.
Credentials
The only required environment variable is NEMO_TOKEN, which is proportional to the described API usage. Small inconsistency: registry metadata lists NEMO_TOKEN as required, but the runtime instructions also describe how to obtain a free anonymous NEMO_TOKEN if none is provided. This is not dangerous but is a behavioral mismatch worth noting: the skill can operate without a pre-provided secret by acquiring one from the external API.
Persistence & Privilege
The skill does not request elevated platform privileges or 'always' inclusion. It does instruct storing a session_id/token for subsequent API calls and references a per-service config directory (~/.config/nemovideo/), which is reasonable for resuming jobs but means state (tokens/job IDs) may be persisted locally.
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-kling-ai
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /image-to-video-kling-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: Convert images into short AI-generated video clips with an easy workflow. - Upload JPG, PNG, WEBP, or BMP images (up to 10MB) and describe the animation you want; get a 1080p MP4 video in 1–2 minutes. - Automatic backend connection and authentication with 100 free credits for new users. - Supports prompt-driven edits, batch processing, and iterative video creation via cloud GPU nodes. - Handle video exports, credits, file uploads, and session state through simple commands. - Full error handling and actionable feedback for common issues like file size, format, or insufficient credits.
元数据
Slug image-to-video-kling-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 Kling Ai 是什么?

Skip the learning curve of professional editing software. Describe what you want — animate this image into a 5-second cinematic video clip — and get animated... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 70 次。

如何安装 Image To Video Kling Ai?

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

Image To Video Kling Ai 是免费的吗?

是的,Image To Video Kling 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 Kling Ai 支持哪些平台?

Image To Video Kling 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 Kling Ai?

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

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