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vcarolxhberger

David Text Tovideo

作者 vcarolxhberger · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install david-text-tovideo
功能描述
Skip the learning curve of professional editing software. Describe what you want — turn this text into a 30-second video with visuals and voiceover — and get...
使用说明 (SKILL.md)

Getting Started

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

Try saying:

  • "convert my text prompts"
  • "export 1080p MP4"
  • "turn this text 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.

David Text to Video — Convert Text Into Generated Videos

Send me your text prompts and describe the result you want. The AI video creation runs on remote GPU nodes — nothing to install on your machine.

A quick example: upload a 150-word product description or script, type "turn this text into a 30-second video with visuals and voiceover", and you'll get a 1080p MP4 back in roughly 1-2 minutes. All rendering happens server-side.

Worth noting: shorter, clearer text produces more accurate and focused video output.

Matching Input to Actions

User prompts referencing david text tovideo, 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.

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

Header Value
X-Skill-Source david-text-tovideo
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

SSE Event Handling

Event Action
Text response Apply GUI translation (§4), present to user
Tool call/result Process internally, don't forward
heartbeat / empty data: Keep waiting. Every 2 min: "⏳ Still working..."
Stream closes Process 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.

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)

Common Workflows

Quick edit: Upload → "turn this text into a 30-second video with visuals and voiceover" → 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 text into a 30-second video with visuals and voiceover" — concrete instructions get better results.

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

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

安全使用建议
This skill connects to an external service (mega-api-prod.nemovideo.ai) to render videos and will send whatever text and files you upload to that service. Consider these points before installing: - Only provide non-sensitive material. Uploaded files and text will be transmitted to and processed by the vendor's backend. - Prefer supplying your own NEMO_TOKEN (if you have one) rather than allowing the skill to auto-create an anonymous token, so you retain control of the account and credits. - Ask the skill author for a homepage, privacy policy, and terms of service — the published metadata lacks a source URL. - Clarify the config-path discrepancy: SKILL.md references ~/.config/nemovideo/ while registry metadata showed none. Confirm whether the skill will read or write files in your home directory. - If you need strong guarantees about data handling (PII, copyrighted material), do not use this skill until you verify the vendor and their policies.
功能分析
Type: OpenClaw Skill Name: david-text-tovideo Version: 1.0.0 The skill provides a legitimate interface for a text-to-video generation service via the nemovideo.ai API. It handles authentication (including anonymous token acquisition), file uploads, and video rendering workflows as described. All identified network activity and environment variable usage (NEMO_TOKEN) are consistent with the stated purpose, and there is no evidence of data exfiltration, malicious execution, or harmful prompt injection in SKILL.md.
能力评估
Purpose & Capability
The skill claims to convert text into server‑rendered videos and the SKILL.md shows API endpoints, session flow, upload, render, and export operations that are coherent with that purpose. The declared primary credential (NEMO_TOKEN) is relevant to the stated functionality.
Instruction Scope
The runtime instructions tell the agent to perform network calls to a third‑party API (create session, SSE, upload, render) and to upload user files (multipart or by URL). If NEMO_TOKEN is missing the skill directs the agent to generate a client UUID and call an anonymous auth endpoint to obtain a token automatically. These instructions involve transmitting user content and creating/using credentials without an explicit, separate user sign‑in step; they also map GUI actions to automated API calls that may read local file paths if a user provides them. Users should be aware their uploaded content and any files referenced will be sent to an external service.
Install Mechanism
This is an instruction‑only skill with no install spec or code files — nothing is written to disk by an installer. That reduces installation risk.
Credentials
The only declared required environment variable is NEMO_TOKEN, which is appropriate. However, the SKILL.md frontmatter also references a config path (~/.config/nemovideo/) while the registry metadata listed no required config paths — this inconsistency should be clarified. Also, the skill will create/obtain an anonymous token if none exists, which is reasonable for convenience but has privacy/usage implications.
Persistence & Privilege
The skill is not set to always: true and has no install-time persistence. It can be invoked autonomously by the agent (the platform default), which increases reach but is expected for skills. It does not request elevated system-wide privileges.
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 david-text-tovideo
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /david-text-tovideo 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 — convert text into AI-generated videos in minutes, no editing skills required. - Upload TXT, DOCX, PDF, or pasted text (max 500MB) to auto-generate videos with visuals and voiceover. - Simple quickstart: automatic cloud setup with token generation and session handling. - Export high-quality videos (up to 1080p MP4) and track your project credits and session state. - Supports batch processing, timeline previews, and iterative editing via chat. - Handles error codes and user prompts for seamless cloud video creation.
元数据
Slug david-text-tovideo
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

David Text Tovideo 是什么?

Skip the learning curve of professional editing software. Describe what you want — turn this text into a 30-second video with visuals and voiceover — and get... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 66 次。

如何安装 David Text Tovideo?

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

David Text Tovideo 是免费的吗?

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

David Text Tovideo 支持哪些平台?

David Text Tovideo 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 David Text Tovideo?

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

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