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Text To Video Automatic

作者 vynbosserman65 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
30
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0
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0
当前安装
1
版本数
在 OpenClaw 中安装
/install text-to-video-automatic
功能描述
Turn a 100-word product description paragraph into 1080p auto-generated videos just by typing what you need. Whether it's generating videos automatically fro...
使用说明 (SKILL.md)

Getting Started

Ready when you are. Drop your text prompts here or describe what you want to make.

Try saying:

  • "generate a 100-word product description paragraph into a 1080p MP4"
  • "turn this script into a 30-second video with visuals and music"
  • "generating videos automatically from written scripts or prompts for marketers"

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.

Text to Video Automatic — Generate Videos From Text Automatically

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

Say you have a 100-word product description paragraph and want to turn this script into a 30-second video with visuals and music — the backend processes it in about 1-2 minutes and hands you a 1080p MP4.

Tip: shorter, clearer text prompts produce more accurate and coherent video output.

Matching Input to Actions

User prompts referencing text to video automatic, 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.

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

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

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

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 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=\x3Cid>, 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

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 video with visuals and music" → Download MP4. Takes 1-2 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 video with visuals and music" — concrete instructions get better results.

Max file size is 200MB. Stick to TXT, DOCX, PDF, SRT for the smoothest experience.

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

安全使用建议
This skill is not showing evidence of malicious behavior in the provided artifacts. Before installing, understand that it relies on Nemovideo's cloud API, may create an anonymous token, and may upload your prompts or media files for rendering. Use it with non-sensitive content unless you have verified the provider's privacy, retention, and credit policies.
功能分析
Type: OpenClaw Skill Name: text-to-video-automatic Version: 1.0.0 The skill is classified as suspicious due to instructions in SKILL.md that direct the AI agent to map text-based commands received from a remote SSE stream (e.g., "click", "open", "drag/drop") directly into API actions. This creates a remote control vector where the backend server (mega-api-prod.nemovideo.ai) can influence the agent's tool execution. Additionally, the skill performs environment fingerprinting by checking local installation paths (~/.clawhub/ or ~/.cursor/skills/) to set attribution headers. While these features support the stated text-to-video functionality, the remote instruction mapping and automated token acquisition from an external endpoint represent a significant attack surface.
能力评估
Purpose & Capability
The described capabilities match the stated purpose: text prompts, uploads, cloud rendering, status checks, credits, and export. Users should still recognize that their content is processed by an external cloud backend.
Instruction Scope
The skill routes user intents to API operations and translates backend GUI-like responses into follow-up API calls. This is purpose-aligned, but actions can proceed automatically once the skill is invoked.
Install Mechanism
There is no install spec and no code to analyze, which reduces local execution risk. However, the registry source is unknown and no homepage is provided, so provenance cannot be verified from the artifacts.
Credentials
Use of NEMO_TOKEN or an automatically obtained anonymous token is proportionate for a cloud rendering API. The artifacts do not show unrelated credential access or token disclosure.
Persistence & Privilege
The skill keeps a cloud session_id for operations and notes that render jobs can be orphaned if interrupted. No hidden local persistence or background local process is shown.
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 text-to-video-automatic
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /text-to-video-automatic 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: Instantly generate 1080p videos from text prompts or scripts, including product descriptions, marketing content, and more. - Automatic cloud setup: Securely acquires credentials and session for you (100 free credits to start; no manual signup needed). - Seamless operations: Send text or upload scripts to generate videos—no timeline editing or export steps required. - Instant accessibility: Allows easy export and download of your finished videos in MP4 and other formats. - Status, credits, and workflow support: Built-in commands for checking balances, tracking session state, and managing your editing session. - Smart error handling and guidance: Informs you about file issues, credits, progress updates, and next steps automatically.
元数据
Slug text-to-video-automatic
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

Text To Video Automatic 是什么?

Turn a 100-word product description paragraph into 1080p auto-generated videos just by typing what you need. Whether it's generating videos automatically fro... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 30 次。

如何安装 Text To Video Automatic?

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

Text To Video Automatic 是免费的吗?

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

Text To Video Automatic 支持哪些平台?

Text To Video Automatic 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 Text To Video Automatic?

由 vynbosserman65(@vynbosserman65)开发并维护,当前版本 v1.0.0。

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