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francemichaell-15

Ai Image To Video Deevid

作者 francemichaell-15 · 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-image-to-video-deevid
功能描述
Get animated video clips ready to post, without touching a single slider. Upload your still images (JPG, PNG, WEBP, HEIC, up to 200MB), say something like "a...
使用说明 (SKILL.md)

Getting Started

Ready when you are. Drop your still images here or describe what you want to make.

Try saying:

  • "convert a single product photo or portrait image into a 1080p MP4"
  • "animate this image into a smooth 5-second video clip"
  • "turning static images into short animated videos for social media creators"

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.

AI Image to Video Deevid — Convert Images into Video Clips

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

Here's a typical use: you send a a single product photo or portrait image, ask for animate this image into a smooth 5-second video clip, and about 30-60 seconds later you've got a MP4 file ready to download. The whole thing runs at 1080p by default.

One thing worth knowing — high-contrast images with clear subjects animate more smoothly.

Matching Input to Actions

User prompts referencing ai image to video deevid, 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: ai-image-to-video-deevid
  • X-Skill-Version: from frontmatter version
  • X-Skill-Platform: detect from install path (~/.clawhub/clawhub, ~/.cursor/skills/cursor, else unknown)

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

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

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)

Common Workflows

Quick edit: Upload → "animate this image into a smooth 5-second video clip" → Download MP4. Takes 30-60 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 "animate this image into a smooth 5-second video clip" — concrete instructions get better results.

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

Export as MP4 for widest compatibility across social platforms.

安全使用建议
This skill appears to do what it says: it uploads images to a Nemovideo backend and returns rendered MP4s. Before installing, consider: - NEMO_TOKEN is used as a Bearer auth credential; only provide a token scoped for this service (avoid reusing high-privilege or long-lived secrets). If you don't supply one, the skill will request an anonymous token from https://mega-api-prod.nemovideo.ai which gives limited free credits. - Files you upload will be sent to an external service (mega-api-prod.nemovideo.ai) and processed on cloud GPUs — check privacy/storage/retention policies before sending sensitive images. - The skill will read its own YAML frontmatter and may probe typical install paths (~/.clawhub/, ~/.cursor/skills/) to set an X-Skill-Platform header; this is only for attribution but does involve checking local paths. If you are uncomfortable with that, avoid enabling the skill or run it in an environment that doesn't expose those paths. - There is a small metadata mismatch (SKILL.md lists a config path while registry metadata shows none); harmless but worth noting. If you trust nemovideo.ai (or plan to use the anonymous token flow) and are comfortable with uploading images to a third-party cloud renderer, this skill is coherent and reasonable to use. If you need stronger guarantees about data handling or token scope, verify the service's terms or avoid supplying a personal NEMO_TOKEN.
功能分析
Type: OpenClaw Skill Name: ai-image-to-video-deevid Version: 1.0.0 The skill is a legitimate integration for an AI image-to-video conversion service hosted at nemovideo.ai. It provides detailed instructions for the agent to manage API authentication (including an anonymous token fallback), session handling, and file uploads. The logic is consistent with the stated purpose, and there are no indicators of data exfiltration, malicious execution, or harmful prompt injection in SKILL.md or _meta.json.
能力评估
Purpose & Capability
The skill claims to convert images to short videos and its instructions only require a NEMO_TOKEN and calls to nemovideo.ai endpoints — these are coherent and expected for a cloud-rendering video service. Minor inconsistency: the SKILL.md frontmatter advertises a config path (~/.config/nemovideo/) while the registry metadata lists no required config paths; this is plausibly harmless but is an internal mismatch.
Instruction Scope
Instructions focus on establishing a session, uploading files, streaming SSE, and polling render status — all appropriate. They also instruct the agent to read the skill's YAML frontmatter (for version/source) and to detect install path (~/.clawhub/, ~/.cursor/skills/) to set an X-Skill-Platform header, which requires reading local install-path information; this is not strictly harmful but it does cause the agent to probe local paths for attribution purposes.
Install Mechanism
There is no install spec and no code files (instruction-only). Nothing is written to disk by an installer step — lowest-risk install mechanism.
Credentials
The only required environment variable is NEMO_TOKEN (declared as primaryEnv), which is proportionate for authenticating to the described API. The SKILL.md's additional mention of a config path is informational; no other unrelated secrets are requested.
Persistence & Privilege
The skill is not force-included (always: false) and requests no special persistent privileges or system-wide configuration changes. Autonomous invocation is allowed (platform default) but not combined with other high-risk requests.
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-image-to-video-deevid
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /ai-image-to-video-deevid 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 AI Image to Video Deevid — convert still images into animated video clips with cloud GPU rendering. - Upload static images (JPG, PNG, WEBP, HEIC, up to 200MB) and generate 1080p MP4 video clips via natural language instructions. - Automatic session and token management, including free 7-day starter credits for new users. - Seamless cloud rendering workflow with real-time status updates and error handling. - Workflow supports uploading, editing, previewing, exporting, and credit checking—all in chat. - Designed for fast, easy creation of animated videos for social media, no manual animation skills required.
元数据
Slug ai-image-to-video-deevid
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 Image To Video Deevid 是什么?

Get animated video clips ready to post, without touching a single slider. Upload your still images (JPG, PNG, WEBP, HEIC, up to 200MB), say something like "a... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 71 次。

如何安装 Ai Image To Video Deevid?

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

Ai Image To Video Deevid 是免费的吗?

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

Ai Image To Video Deevid 支持哪些平台?

Ai Image To Video Deevid 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 Image To Video Deevid?

由 francemichaell-15(@francemichaell-15)开发并维护,当前版本 v1.0.0。

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