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Jupiter Ai Text To Video

作者 mhogan2013-9 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install jupiter-ai-text-to-video
功能描述
Get AI generated videos ready to post, without touching a single slider. Upload your text prompts (TXT, DOCX, PDF, plain text, up to 500MB), say something li...
使用说明 (SKILL.md)

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"
  • "turn this script into a 30-second"

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.

Jupiter AI Text to Video — 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 two-sentence description of a sunset over a mountain lake, ask for turn this script into a 30-second cinematic video with background music, 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 and more specific prompts produce more accurate and consistent video output.

Matching Input to Actions

User prompts referencing jupiter ai text to video, 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 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.

Three attribution headers are required on every request and must match this file's frontmatter:

Header Value
X-Skill-Source jupiter-ai-text-to-video
X-Skill-Version frontmatter version
X-Skill-Platform auto-detect: clawhub / cursor / unknown from install path

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

Create session: POST /api/tasks/me/with-session/nemo_agent — body {"task_name":"project","language":"\x3Clang>"} — returns task_id, session_id.

Send message (SSE): POST /run_sse — body {"app_name":"nemo_agent","user_id":"me","session_id":"\x3Csid>","new_message":{"parts":[{"text":"\x3Cmsg>"}]}} with Accept: text/event-stream. Max timeout: 15 minutes.

Upload: POST /api/upload-video/nemo_agent/me/\x3Csid> — file: multipart -F "files=@/path", or URL: {"urls":["\x3Curl>"],"source_type":"url"}

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

Session state: GET /api/state/nemo_agent/me/\x3Csid>/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_\x3Cts>","sessionId":"\x3Csid>","draft":\x3Cjson>,"output":{"format":"mp4","quality":"high"}}. Poll GET /api/render/proxy/lambda/\x3Cid> 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.

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

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

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.

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 → "turn this script into a 30-second cinematic video with background music" → 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 "turn this script into a 30-second cinematic video with background music" — concrete instructions get better results.

Max file size is 500MB. Stick to TXT, DOCX, PDF, plain text for the smoothest experience.

Export as MP4 for widest compatibility across social platforms and devices.

安全使用建议
This skill appears to do what it says: it calls a nemo/nemovideo cloud API to turn text and uploaded files into rendered videos. Before installing, consider: (1) it will contact https://mega-api-prod.nemovideo.ai and may create an anonymous token automatically if you don't provide NEMO_TOKEN — only install if you trust that remote service; (2) uploading files reads content you provide (do not upload secrets or private documents); (3) verify whether you want the agent to automatically obtain ephemeral tokens on your behalf; (4) note the small metadata mismatch: SKILL.md references ~/.config/nemovideo/ while registry metadata reported no config paths — this is likely harmless but you may ask the publisher to clarify. If you have a real NEMO_TOKEN for an account, supply it only if you trust the service and understand that rendered media and inputs will be transmitted to their servers.
功能分析
Type: OpenClaw Skill Name: jupiter-ai-text-to-video Version: 1.0.0 The skill is a legitimate AI video generation tool designed to interface with the NemoVideo API (mega-api-prod.nemovideo.ai). It defines clear procedures for authentication (using NEMO_TOKEN or anonymous tokens), session management, and video rendering workflows. The instructions in SKILL.md are focused on task execution and API interaction, including explicit directions to protect tokens and handle errors gracefully, with no evidence of data exfiltration, malicious execution, or harmful prompt injection.
能力评估
Purpose & Capability
The skill claims to generate videos from text and its instructions only reference endpoints and actions that match that purpose (session creation, SSE messaging, upload, render, credits). Requiring a NEMO_TOKEN (primary credential) is appropriate for a cloud API service.
Instruction Scope
The SKILL.md directs the agent to call external APIs at mega-api-prod.nemovideo.ai, create anonymous tokens if NEMO_TOKEN is missing, create sessions, upload files (multipart or URL), stream SSE responses, and poll render status. These actions are within the stated purpose. Note: uploads reference local file paths (e.g., -F "files=@/path") which implies the agent may read files the user supplies or file paths given by the user — avoid sending sensitive files. The file also instructs auto-detection of an install path for X-Skill-Platform which may require inspecting the environment/install path.
Install Mechanism
Instruction-only skill with no install spec and no code files — nothing is written to disk by an installer. This is the lowest-risk install model.
Credentials
Only one credential is declared (NEMO_TOKEN), which is appropriate for a service API. The instructions also support generating an anonymous token via the anonymous-token endpoint when NEMO_TOKEN is absent — this is consistent but means the skill will reach out to the network to obtain ephemeral credentials if not provided. Minor metadata inconsistency: the registry metadata listed no required config paths, but the SKILL.md frontmatter references ~/.config/nemovideo/.
Persistence & Privilege
always is false and the skill is user-invocable; it does not request persistent system privileges or claim it will modify other skills or system-wide settings. Autonomous invocation is allowed (the platform default) but not combined with other high-risk indicators here.
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 jupiter-ai-text-to-video
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /jupiter-ai-text-to-video 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 Jupiter AI Text to Video. - Generate 1080p AI videos from text prompts (TXT, DOCX, PDF, plain text, up to 500MB) - Simple cloud setup: Automatic token handling and session creation - Supports exporting in multiple formats (MP4, MOV, AVI, WebM, etc.) with background music and effects - Workflow: Upload script → request edit → download finished video - Includes balance checking, timeline previews, batch file handling, and error feedback - Designed for fast and easy video creation by marketers and content creators—no special software required
元数据
Slug jupiter-ai-text-to-video
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

Jupiter Ai Text To Video 是什么?

Get AI generated videos ready to post, without touching a single slider. Upload your text prompts (TXT, DOCX, PDF, plain text, up to 500MB), say something li... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 73 次。

如何安装 Jupiter Ai Text To Video?

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

Jupiter Ai Text To Video 是免费的吗?

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

Jupiter Ai Text To Video 支持哪些平台?

Jupiter Ai Text To Video 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 Jupiter Ai Text To Video?

由 mhogan2013-9(@mhogan2013-9)开发并维护,当前版本 v1.0.0。

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