← 返回 Skills 市场
linmillsd7

Image To Video In Free

作者 linmillsd7 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
60
总下载
0
收藏
0
当前安装
1
版本数
在 OpenClaw 中安装
/install image-to-video-in-free
功能描述
Turn three product photos in JPG format into 1080p animated video clips just by typing what you need. Whether it's converting still photos into shareable vid...
使用说明 (SKILL.md)

Getting Started

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

Try saying:

  • "convert my static images"
  • "export 1080p MP4"
  • "turn my photos into a short"

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 Free — Convert Images Into Video Clips

Drop your static 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 in JPG format, ask for turn my photos into a short video clip for free, 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 speeds up processing and keeps the video tight.

Matching Input to Actions

User prompts referencing image to video in free, 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.

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

  • X-Skill-Source: image-to-video-in-free
  • 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.

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)

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

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "turn my photos into a short video clip for free" — concrete instructions get better results.

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

Export as MP4 for widest compatibility.

Common Workflows

Quick edit: Upload → "turn my photos into a short video clip for free" → 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.

安全使用建议
This skill mostly does what it says (it talks to a Nemo video-rendering API and uses a NEMO_TOKEN), but before installing or providing credentials consider the following: 1) Metadata mismatch — the registry lists no config paths but the SKILL.md asks to use ~/.config/nemovideo/ and to probe ~/.clawhub/ and ~/.cursor/skills/ to set an X-Skill-Platform header. Ask the author why filesystem access is needed and where session/token data will be stored. 2) Token handling — the skill can generate and use anonymous tokens; confirm whether those tokens are stored persistently and where (disk vs memory). 3) Attribution headers — the skill requires adding local-install-path-derived headers to every request; this reveals local layout info to the remote API. 4) Domain verification — confirm mega-api-prod.nemovideo.ai is the legitimate service you expect and uses HTTPS/TLS. 5) Least privilege — prefer using a scoped, revocable API token or anonymous short-lived token rather than a long-lived secret in your environment. If you cannot verify the service or storage behavior, do not put sensitive credentials in NEMO_TOKEN and consider running this only in a restricted environment or asking the publisher for an explanation and an explicit privacy/persistence statement.
功能分析
Type: OpenClaw Skill Name: image-to-video-in-free Version: 1.0.0 The skill bundle provides instructions for an AI agent to interface with a third-party cloud service (nemovideo.ai) for image-to-video conversion. It handles authentication via environment variables or anonymous token generation and defines standard API interactions for file uploads, session management, and video rendering. No malicious patterns such as data exfiltration of sensitive local files, unauthorized execution, or deceptive prompt injections were found in SKILL.md or _meta.json.
能力评估
Purpose & Capability
The skill claims to convert images to videos and all runtime operations target a single external video-rendering API (mega-api-prod.nemovideo.ai) using a service token (NEMO_TOKEN). Requesting an API token for a cloud rendering service is coherent with the stated purpose.
Instruction Scope
The SKILL.md instructs the agent to (a) generate an anonymous token via the service API and treat it as NEMO_TOKEN, (b) create and keep a session_id, and (c) read the skill's YAML frontmatter and probe local paths to determine X-Skill-Platform. Probing install paths and reading/writing session/token state are outside a minimal 'image->video' description and expand the agent's filesystem and persistence scope. The instructions also require adding custom attribution headers on every request and state failed export if they are missing — an operational detail that forces the agent to collect and send local metadata.
Install Mechanism
No install step or external packages are specified; this is an instruction-only skill, which minimizes code-on-disk risk.
Credentials
The only declared credential is NEMO_TOKEN (primaryEnv), which is appropriate for a cloud-rendering integration. However, the SKILL.md frontmatter also references a config path (~/.config/nemovideo/) and asks the agent to detect install paths (~/.clawhub/, ~/.cursor/skills/), while the registry metadata showed no required config paths — a mismatch that increases surprise about what local data the skill will access.
Persistence & Privilege
The instructions tell the agent to 'save session_id' and treat an anonymously-created token as NEMO_TOKEN (100 credits, 7-day expiry). There is ambiguity about where and how those tokens/session IDs are stored (in-memory vs written under ~/.config/nemovideo/). The skill is not marked always:true, but the combination of required token handling, session persistence, and filesystem probing raises persistence/privacy concerns.
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-free
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /image-to-video-in-free 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 Free — Convert Images Into Video Clips. - Instantly turn up to three JPG product photos into 1080p MP4 video clips via chat. - No manual video editing: just upload images and describe your desired outcome. - Fully cloud-based with fast processing (30–60 seconds per export). - Automatic session and token setup included; 100 free credits on first use. - Supports export, credits check, balance, and progress/status queries. - Error handling for token expiry, missing credits, unsupported files, and more.
元数据
Slug image-to-video-in-free
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 Free 是什么?

Turn three product photos in JPG format into 1080p animated video clips just by typing what you need. Whether it's converting still photos into shareable vid... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 60 次。

如何安装 Image To Video In Free?

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

Image To Video In Free 是免费的吗?

是的,Image To Video In Free 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 Free 支持哪些平台?

Image To Video In Free 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 Free?

由 linmillsd7(@linmillsd7)开发并维护,当前版本 v1.0.0。

💬 留言讨论