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Create Video From Images

v1.0.0

turn images into compiled image video with this skill. Works with JPG, PNG, WEBP, HEIC files up to 200MB. marketers, social media creators use it for turning...

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Install

OpenClaw Prompt Flow

Install with OpenClaw

Best for remote or guided setup. Copy the exact prompt, then paste it into OpenClaw for mhogan2013-9/create-video-from-images.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "Create Video From Images" (mhogan2013-9/create-video-from-images) from ClawHub.
Skill page: https://clawhub.ai/mhogan2013-9/create-video-from-images
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Required env vars: NEMO_TOKEN
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

CLI Commands

Use the direct CLI path if you want to install manually and keep every step visible.

OpenClaw CLI

Bare skill slug

openclaw skills install create-video-from-images

ClawHub CLI

Package manager switcher

npx clawhub@latest install create-video-from-images
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OpenClawOpenClaw
Benign
medium confidence
Purpose & Capability
The skill is a cloud video-rendering client and only requests a service token (NEMO_TOKEN), which is appropriate for that purpose. One small inconsistency: the SKILL.md frontmatter declares a configPaths entry (~/.config/nemovideo/) while the registry metadata for this submission lists no required config paths.
Instruction Scope
The instructions direct the agent to automatically obtain an anonymous token from https://mega-api-prod.nemovideo.ai and to create and persist a session_id for subsequent API calls. That behavior matches a cloud-rendering workflow, but automatic backend connection on first open and generation/storage of tokens may be surprising to some users — the skill also asks the agent to read skill frontmatter and detect install path for attribution headers.
Install Mechanism
There is no install spec and no code files; this is instruction-only, which means nothing is written to disk by the skill itself during installation.
Credentials
Only a single service credential (NEMO_TOKEN) is declared as required, which is proportional. However, SKILL.md metadata references a config path (~/.config/nemovideo/) and the runtime asks the agent to detect install path (~/.clawhub, ~/.cursor), which would require reading filesystem locations — reasonable for attribution but worth noting because it reveals some environment info.
Persistence & Privilege
The skill is not marked always:true and does not request persistent platform-wide privileges. It does instruct retaining a session_id and using a token for API calls, which is standard for sessioned APIs.
Assessment
This skill appears to be what it claims: a cloud-based image→video client that needs a service token. Before installing, consider: (1) the skill will contact https://mega-api-prod.nemovideo.ai and may auto-generate an anonymous token on first use — confirm you trust that domain and its privacy policy; (2) uploads (your images and any audio) will be sent to the provider for processing, so do not send sensitive images unless you accept that; (3) the skill may read small environment details (its own frontmatter and install path, and SKILL.md mentions ~/.config/nemovideo/) — check where tokens/session IDs are stored if you care about persistence; (4) if you prefer explicit consent for network calls, ask the developer to disable automatic token creation and require a user-provided token. If any of the above is unacceptable, do not install or ask the publisher for more transparency about token storage and data retention.

Like a lobster shell, security has layers — review code before you run it.

Runtime requirements

🖼️ Clawdis
EnvNEMO_TOKEN
Primary envNEMO_TOKEN
latestvk97bck8cdnq53gb618wgtmr6x1854zb7
108downloads
0stars
1versions
Updated 1w ago
v1.0.0
MIT-0

Getting Started

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

Try saying:

  • "turn my images"
  • "export 1080p MP4"
  • "combine these photos into a 30-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 <token>, Content-Type: application/json, and body {"task_name":"project","language":"<detected>"}. 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.

Create Video From Images — Turn Photos Into MP4 Videos

This tool takes your images and runs AI video creation through a cloud rendering pipeline. You upload, describe what you want, and download the result.

Say you have five product photos in JPG format and want to combine these photos into a 30-second video with transitions and background music — the backend processes it in about 30-60 seconds and hands you a 1080p MP4.

Tip: using images with similar aspect ratios produces smoother transitions.

Matching Input to Actions

User prompts referencing create video from images, aspect ratio, text overlays, or audio tracks get routed to the corresponding action via keyword and intent classification.

User says...ActionSkip 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

EndpointMethodPurpose
/api/tasks/me/with-session/nemo_agentPOSTStart a new editing session. Body: {"task_name":"project","language":"<lang>"}. Returns session_id.
/run_ssePOSTSend 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/<sid>POSTUpload a file (multipart) or URL.
/api/credits/balance/simpleGETCheck remaining credits (available, frozen, total).
/api/state/nemo_agent/me/<sid>/latestGETFetch current timeline state (draft, video_infos, generated_media).
/api/render/proxy/lambdaPOSTStart export. Body: {"id":"render_<ts>","sessionId":"<sid>","draft":<json>,"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: create-video-from-images
  • 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 <NEMO_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=<id>, 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

EventAction
Text responseApply GUI translation (§4), present to user
Tool call/resultProcess internally, don't forward
heartbeat / empty data:Keep waiting. Every 2 min: "⏳ Still working..."
Stream closesProcess 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 saysYou 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 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 → "combine these photos into a 30-second video with transitions and background 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 "combine these photos into a 30-second video with transitions and background 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 platforms.

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