Sora Ai Video Generation

v1.0.0

Cloud-based sora-ai-video-generation tool that handles generating short videos from text descriptions. Upload MP4, MOV, WebM, GIF files (up to 500MB), descri...

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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 susan4731-wilfordf/sora-ai-video-generation.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "Sora Ai Video Generation" (susan4731-wilfordf/sora-ai-video-generation) from ClawHub.
Skill page: https://clawhub.ai/susan4731-wilfordf/sora-ai-video-generation
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 sora-ai-video-generation

ClawHub CLI

Package manager switcher

npx clawhub@latest install sora-ai-video-generation
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Purpose & Capability
Name/description match the requested resource access: a single service credential (NEMO_TOKEN), optional config path (~/.config/nemovideo/) and API calls to a nemovideo.ai backend are exactly what a cloud video-generation skill would need.
Instruction Scope
SKILL.md gives detailed API call flow: look for NEMO_TOKEN, otherwise obtain an anonymous token by POSTing to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token, create/keep session_id, use SSE for generation, and upload local files via multipart. These instructions are within scope, but they do require network calls to the external domain and access to user-supplied files and some local paths (config and install-path detection). The skill explicitly tells the agent not to print raw tokens, which is good, but the instructions will transmit prompts and uploaded media to the third-party backend.
Install Mechanism
No install step or external code is fetched; this is an instruction-only skill, so nothing will be written to disk by an installer step.
Credentials
Only one environment variable is required (NEMO_TOKEN) and it is the declared primary credential. The skill will also create an anonymous token if none is present; no unrelated credentials are requested.
Persistence & Privilege
The skill keeps session_id and uses bearer tokens for API calls but does not request 'always: true' or other elevated platform privileges. It reads a declared config path (~/.config/nemovideo/) and inspects an install path pattern to set an attribution header; this involves reading user-home paths but does not appear to alter other skills or system-wide settings.
Assessment
This skill is coherent for cloud video generation, but it sends your prompts, session metadata, and any uploaded media to a third-party service at mega-api-prod.nemovideo.ai. Before installing or using it: (1) confirm you trust that domain/provider and review their privacy/security policies, (2) avoid uploading sensitive or private videos or PII, (3) if you prefer control, set a NEMO_TOKEN in your environment rather than relying on the skill's anonymous-token flow, and (4) be aware the skill may read ~/.config/nemovideo/ and inspect install-paths in your home directory to set headers (this is for attribution but does access your filesystem).

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

Runtime requirements

🎬 Clawdis
EnvNEMO_TOKEN
Primary envNEMO_TOKEN
latestvk97capn917wcb0nayzmhegg1xh84jr8c
96downloads
0stars
1versions
Updated 2w ago
v1.0.0
MIT-0

Getting Started

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

Try saying:

  • "generate my text prompts"
  • "export 1080p MP4"
  • "generate a 10-second cinematic video of"

Quick Start Setup

This skill connects to a cloud processing backend. On first use, set up the connection automatically and let the user know ("Connecting...").

Token check: Look for NEMO_TOKEN in the environment. If found, skip to session creation. Otherwise:

  • Generate a UUID as client identifier
  • POST https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with X-Client-Id header
  • Extract data.token from the response — this is your NEMO_TOKEN (100 free credits, 7-day expiry)

Session: POST https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer auth and body {"task_name":"project"}. Keep the returned session_id for all operations.

Let the user know with a brief "Ready!" when setup is complete. Don't expose tokens or raw API output.

Sora AI Video Generation — Generate Videos from Text Prompts

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

Here's a typical use: you send a a short text description like 'a dog running on a beach at sunset', ask for generate a 10-second cinematic video of a futuristic city at night with neon lights, and about 1-3 minutes later you've got a MP4 file ready to download. The whole thing runs at 1080p by default.

One thing worth knowing — shorter, more specific prompts tend to produce more accurate and consistent results.

Matching Input to Actions

User prompts referencing sora ai video generation, 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.

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

  • X-Skill-Source: sora-ai-video-generation
  • X-Skill-Version: from frontmatter version
  • X-Skill-Platform: detect from install path (~/.clawhub/clawhub, ~/.cursor/skills/cursor, else unknown)

Include Authorization: Bearer <NEMO_TOKEN> and all attribution headers on every request — omitting them triggers a 402 on export.

API base: https://mega-api-prod.nemovideo.ai

Create session: POST /api/tasks/me/with-session/nemo_agent — body {"task_name":"project","language":"<lang>"} — returns task_id, session_id.

Send message (SSE): POST /run_sse — body {"app_name":"nemo_agent","user_id":"me","session_id":"<sid>","new_message":{"parts":[{"text":"<msg>"}]}} with Accept: text/event-stream. Max timeout: 15 minutes.

Upload: POST /api/upload-video/nemo_agent/me/<sid> — file: multipart -F "files=@/path", or URL: {"urls":["<url>"],"source_type":"url"}

Credits: GET /api/credits/balance/simple — returns available, frozen, total

Session state: GET /api/state/nemo_agent/me/<sid>/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_<ts>","sessionId":"<sid>","draft":<json>,"output":{"format":"mp4","quality":"high"}}. Poll GET /api/render/proxy/lambda/<id> 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.

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.

Translating GUI Instructions

The backend responds as if there's a visual interface. Map its instructions to API calls:

  • "click" or "点击" → execute the action via the relevant endpoint
  • "open" or "打开" → query session state to get the data
  • "drag/drop" or "拖拽" → send the edit command through SSE
  • "preview in timeline" → show a text summary of current tracks
  • "Export" or "导出" → run the 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)

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

Common Workflows

Quick edit: Upload → "generate a 10-second cinematic video of a futuristic city at night with neon lights" → Download MP4. Takes 1-3 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 "generate a 10-second cinematic video of a futuristic city at night with neon lights" — concrete instructions get better results.

Max file size is 500MB. Stick to MP4, MOV, WebM, GIF for the smoothest experience.

Export as MP4 for widest compatibility across social platforms and video editors.

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