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Image To Video In Canva

作者 peandrover adam · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install image-to-video-in-canva
功能描述
Turn three product photos or a single landscape image into 1080p animated image videos just by typing what you need. Whether it's converting static images in...
使用说明 (SKILL.md)

Getting Started

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

Try saying:

  • "convert my images"
  • "export 1080p MP4"
  • "turn my images into a slideshow"

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.

Image to Video in Canva — Convert Images into Shareable Videos

Drop your 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 three product photos or a single landscape image, ask for turn my images into a slideshow video with transitions and music, 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 — using fewer images with longer durations per slide produces smoother results.

Matching Input to Actions

User prompts referencing image to video in canva, 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 image-to-video-in-canva, 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).

Every API call needs Authorization: Bearer \x3CNEMO_TOKEN> plus the three attribution headers above. If any header is missing, exports return 402.

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

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.

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

Common Workflows

Quick edit: Upload → "turn my images into a slideshow video with transitions and music" → 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 "turn my images into a slideshow video with transitions and music" — 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 call an external service (mega-api-prod.nemovideo.ai) to render videos and will upload any images you drop into the chat—so your images and metadata will leave your machine/agent. The skill name references 'Canva' but it does not use Canva APIs; that is potentially misleading. Before installing or using it, consider: - Do you trust nemovideo.ai to receive and store your images? Check the service's privacy policy and retention practices. - Confirm where the agent will store the session_id / any generated tokens (the docs hint at ~/.config/nemovideo/ but the registry metadata omitted this). If you don't want persistent local tokens or files written, ask the developer to make storage explicit or to keep everything in-memory. - The skill will look at the agent install path to set an attribution header—if you have policy concerns about reading filesystem paths, request that behavior be removed or confined. - Because source and homepage are unknown, prefer minimal exposure: do not provide sensitive images or data, and consider creating an ephemeral/anonymous token or sandboxed agent instance for testing. If the developer can confirm (a) why 'Canva' is in the name, (b) exactly where tokens/session data are stored, and (c) that filesystem checks are read-only and limited, the inconsistencies would be resolved and my concern would lower.
功能分析
Type: OpenClaw Skill Name: image-to-video-in-canva Version: 1.0.0 The skill bundle is a legitimate integration for the nemovideo.ai service, allowing an AI agent to convert images to videos. It defines standard API workflows for authentication, session management, file uploads, and polling for render results. The instructions in SKILL.md are focused on the stated functionality and include appropriate security practices, such as advising the agent not to print sensitive tokens or raw JSON to the user. No indicators of data exfiltration, malicious execution, or harmful prompt injection were found.
能力评估
Purpose & Capability
The skill claims 'in Canva' in its name but the runtime exclusively targets an unrelated service (mega-api-prod.nemovideo.ai / NEMO_TOKEN). Requiring a NEMO_TOKEN and talking to nemovideo.ai is coherent for an 'image to video' service, but the 'Canva' branding in the name is misleading and may confuse users about what platform will actually be used. Metadata also references a config path (~/.config/nemovideo/) even though registry metadata listed no required config paths—an inconsistency.
Instruction Scope
Instructions require the agent to upload user images and other user-provided media to an external API (expected for the stated purpose) and to create/use bearer tokens. They also instruct deriving attribution headers by detecting the agent's install path (e.g., checking for ~/.clawhub/ or ~/.cursor/skills/), which implies reading filesystem state outside the skill's payload and may be surprising to users. The doc says to 'Save session_id' but doesn't specify where—this leaves ambiguous whether tokens/session data will be persisted to disk (metadata hints at ~/.config/nemovideo/). Transmitting user images and related metadata to a third party is normal for this functionality but is an important privacy consideration and should be explicit to users.
Install Mechanism
No install spec and no code files (instruction-only). This is the lower-risk model because nothing is written to disk by an installer, but runtime behavior still sends data to an external service.
Credentials
The skill requests a single credential (NEMO_TOKEN) which is proportional to calling the described API. However, metadata lists a config path (~/.config/nemovideo/) that was not declared in the registry's required config paths; the runtime also expects to be able to check install paths to derive headers. These imply potential read/write access to local config beyond the single env var and should be clarified.
Persistence & Privilege
The skill does not request 'always: true' and uses the normal autonomous-invocation model. There is no explicit instruction to modify other skills or system-wide settings. The only persistence ambiguity is where session tokens/session_id are saved (in-memory vs a local config path), which the SKILL.md does not specify.
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-in-canva
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /image-to-video-in-canva 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 in Canva — quickly turn images into animated videos. - Instantly convert up to 3 images into 1080p videos with no local setup required. - Drag and drop images or provide links, then describe your desired result to generate an animated video in seconds. - Includes robust session management, automatic free credit allocation, and cloud-based rendering (MP4 export, music, text overlays, and more). - Handles error cases (token expiry, file type/size limits, insufficient credits) with clear user messages and guidance. - Supports simple prompt-based editing for fast content creation and sharing.
元数据
Slug image-to-video-in-canva
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 In Canva 是什么?

Turn three product photos or a single landscape image into 1080p animated image videos just by typing what you need. Whether it's converting static images in... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 83 次。

如何安装 Image To Video In Canva?

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

Image To Video In Canva 是免费的吗?

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

Image To Video In Canva 支持哪些平台?

Image To Video In Canva 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 In Canva?

由 peandrover adam(@peand-rover)开发并维护,当前版本 v1.0.0。

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