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Free Text To Video Llm

作者 vynbosserman65 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install free-text-to-video-llm
功能描述
Skip the learning curve of professional editing software. Describe what you want — generate a 30-second video of a futuristic city at dusk with cinematic lig...
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"
  • "generate a 30-second video of a"

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.

Free Text to Video LLM — Generate Videos From Text Prompts

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

A quick example: upload a two-sentence description of a sunset over a city skyline, type "generate a 30-second video of a futuristic city at dusk with cinematic lighting", and you'll get a 1080p MP4 back in roughly 1-3 minutes. All rendering happens server-side.

Worth noting: shorter and more specific prompts tend to produce more accurate and coherent video output.

Matching Input to Actions

User prompts referencing free text to video llm, 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 calls go to https://mega-api-prod.nemovideo.ai. The main endpoints:

  1. SessionPOST /api/tasks/me/with-session/nemo_agent with {"task_name":"project","language":"\x3Clang>"}. Gives you a session_id.
  2. Chat (SSE)POST /run_sse with session_id and your message in new_message.parts[0].text. Set Accept: text/event-stream. Up to 15 min.
  3. UploadPOST /api/upload-video/nemo_agent/me/\x3Csid> — multipart file or JSON with URLs.
  4. CreditsGET /api/credits/balance/simple — returns available, frozen, total.
  5. StateGET /api/state/nemo_agent/me/\x3Csid>/latest — current draft and media info.
  6. ExportPOST /api/render/proxy/lambda with render ID and draft JSON. Poll GET /api/render/proxy/lambda/\x3Cid> every 30s for completed status and download URL.

Formats: mp4, mov, avi, webm, mkv, jpg, png, gif, webp, mp3, wav, m4a, aac.

Headers are derived from this file's YAML frontmatter. X-Skill-Source is free-text-to-video-llm, X-Skill-Version comes from the version field, and X-Skill-Platform is detected from the install path (~/.clawhub/ = clawhub, ~/.cursor/skills/ = cursor, otherwise unknown).

Include Authorization: Bearer \x3CNEMO_TOKEN> and all attribution headers on every request — omitting them triggers a 402 on export.

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)

Backend Response Translation

The backend assumes a GUI exists. Translate these into API actions:

Backend says You 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

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.

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

Common Workflows

Quick edit: Upload → "generate a 30-second video of a futuristic city at dusk with cinematic lighting" → 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 30-second video of a futuristic city at dusk with cinematic lighting" — 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 platforms and devices.

安全使用建议
This skill sends your prompts and any uploaded files to a third‑party rendering service (mega-api-prod.nemovideo.ai). It will also mint an anonymous token automatically if you don't provide NEMO_TOKEN, which means the skill can make outbound network requests without you first supplying credentials. Before installing: 1) Confirm you trust the nemovideo domain and review its privacy/retention policy (your text/files and any metadata will be transmitted and processed remotely). 2) If you prefer control, provide your own NEMO_TOKEN rather than relying on anonymous token creation. 3) Ask the publisher to clarify the conflicting config-path metadata (~/.config/nemovideo/) and any local file reads. 4) Avoid uploading sensitive or proprietary content unless you are comfortable with third‑party processing.
功能分析
Type: OpenClaw Skill Name: free-text-to-video-llm Version: 1.0.0 The skill provides a functional interface for a text-to-video generation service hosted at nemovideo.ai. The instructions in SKILL.md guide the agent through standard API workflows, including anonymous token acquisition, session management, and file uploads to the service's backend. No evidence of data exfiltration, unauthorized access to sensitive local files, or malicious execution was found.
能力评估
Purpose & Capability
The declared purpose (generate videos from text) matches the runtime instructions: session creation, SSE chat, uploads, and export endpoints for a remote rendering service. However the SKILL.md frontmatter lists a config path (~/.config/nemovideo/) while the registry metadata reported no required config paths — a minor inconsistency in declared requirements.
Instruction Scope
Instructions explicitly require checking for NEMO_TOKEN and, if absent, POSTing to https://mega-api-prod.nemovideo.ai to obtain an anonymous token, then uploading user text/files (up to 500MB) and job data to that service. This is consistent with the feature but means user content and metadata will be transmitted to a third party. The skill also instructs detecting install path to set an attribution header (reads agent install path), which requires environment inspection beyond just the NEMO_TOKEN.
Install Mechanism
No install spec and no code files (instruction-only). That minimizes local disk/write risk; all execution is network calls described in SKILL.md.
Credentials
Only a single credential (NEMO_TOKEN) is required, which matches the cloud API integration. The skill will create an anonymous token automatically if none is present (network call). The earlier-mentioned config path in SKILL.md frontmatter is not otherwise referenced in instructions — this mismatch should be clarified.
Persistence & Privilege
always is false and the skill does not request permanent platform-wide privileges. It creates remote sessions on the third-party backend (normal for this service) but does not request to modify other skills or system-wide settings.
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 free-text-to-video-llm
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /free-text-to-video-llm 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 Free Text to Video LLM — generate videos from text prompts. - Instantly generate AI videos (up to 30 seconds) from text descriptions or uploaded documents. - Supports TXT, DOCX, PDF, and plain text files up to 500MB. - Cloud-powered: no local installation needed; auto-connects and handles authentication with 100 free credits to start. - Export videos in multiple formats including MP4, MOV, AVI, and more. - Built for easy workflows: upload, prompt, preview changes, and export videos in minutes.
元数据
Slug free-text-to-video-llm
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

Free Text To Video Llm 是什么?

Skip the learning curve of professional editing software. Describe what you want — generate a 30-second video of a futuristic city at dusk with cinematic lig... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 72 次。

如何安装 Free Text To Video Llm?

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

Free Text To Video Llm 是免费的吗?

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

Free Text To Video Llm 支持哪些平台?

Free Text To Video Llm 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 Free Text To Video Llm?

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

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