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Free Video Generation Github

作者 linmillsd7 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install free-video-generation-github
功能描述
generate text prompts into AI generated videos with this skill. Works with MP4, MOV, WebM, GIF files up to 500MB. developers use it for generating videos fro...
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 explainer video from"

Getting Connected

Before handling any user request, establish a connection to the backend API. Show a brief status like "Connecting...".

If NEMO_TOKEN is in the environment, use it directly and create a session. Otherwise, acquire a free starter token:

  • Generate a UUID as client identifier
  • POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with the X-Client-Id header
  • The response includes a token with 100 free credits valid for 7 days — use it as NEMO_TOKEN

Then create a session by POSTing to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer authorization and body {"task_name":"project","language":"en"}. The session_id in the response is needed for all following requests.

Tell the user you're ready. Keep the technical details out of the chat.

Free Video Generation GitHub — 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 short text description of a product demo scene, type "generate a 30-second explainer video from a text prompt about my app", and you'll get a 1080p MP4 back in roughly 1-2 minutes. All rendering happens server-side.

Worth noting: shorter prompts with clear scene descriptions produce more consistent results.

Matching Input to Actions

User prompts referencing free video generation github, 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.

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

Header Value
X-Skill-Source free-video-generation-github
X-Skill-Version frontmatter version
X-Skill-Platform auto-detect: clawhub / cursor / unknown from install path

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

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)

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

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.

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

Common Workflows

Quick edit: Upload → "generate a 30-second explainer video from a text prompt about my app" → 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 "generate a 30-second explainer video from a text prompt about my app" — 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.

安全使用建议
What to consider before installing: - The skill will call an external service (https://mega-api-prod.nemovideo.ai) to create sessions, stream edits, upload media, and export videos. Verify you trust that domain and service operator before allowing the skill to run. - It requires a NEMO_TOKEN. If you don't provide one, the skill will automatically request an anonymous token from the service and use it. Decide if you want the agent to obtain and hold tokens on your behalf. - SKILL.md frontmatter references a local config path (~/.config/nemovideo/) even though the registry metadata lists no config paths. Ask the publisher to confirm whether the skill will read/write that directory (e.g., to cache tokens or job IDs) and what it stores there. - The instructions explicitly say to "keep the technical details out of the chat," which means some network/auth actions may be hidden from the user. If you need auditability/transparency, request source or logs showing API calls and token usage, or avoid installing. - If you choose to proceed: provide a limited-scope token (if possible), test with non-sensitive content, and monitor outbound network activity. If you cannot verify the service owner or source code, treat the skill as untrusted and avoid using it with private or sensitive data.
功能分析
Type: OpenClaw Skill Name: free-video-generation-github Version: 1.0.0 The skill requires network access to an external API (mega-api-prod.nemovideo.ai) and file system access to '~/.config/nemovideo/', which are defined as risky capabilities. It provides detailed instructions for the AI agent to manage authentication tokens, sessions, and telemetry (X-Skill-Platform), and specifically directs the agent to hide technical details from the user. While these actions appear aligned with the stated video generation purpose, the combination of automated credential handling and environment inspection meets the threshold for a suspicious classification per the provided guidelines.
能力评估
Purpose & Capability
The skill's stated purpose (turn text prompts into videos) matches the documented API endpoints, auth flow, and required NEMO_TOKEN. However the SKILL.md frontmatter includes a config path (~/.config/nemovideo/) that the registry metadata did not list — this mismatch suggests either the registry metadata is incomplete or the skill expects to read/write a local config directory that wasn't declared.
Instruction Scope
Runtime instructions direct the agent to contact external endpoints at mega-api-prod.nemovideo.ai (session creation, SSE chat, uploads, exports) and to automatically obtain an anonymous token if NEMO_TOKEN is absent. The SKILL.md explicitly instructs the agent to "Keep the technical details out of the chat," which indicates network/auth activity may be hidden from the user. The actions described (uploading files, polling, long-lived SSE) are within the skill's purpose, but the deliberate instruction to omit technical details is an opacity/red‑flag.
Install Mechanism
This is an instruction-only skill with no install spec and no code files; nothing will be written to disk by an installer. That is the lowest-risk install mechanism. The only install-related oddity is the frontmatter's configPaths reference (suggesting a local config directory) despite no install/config requirements declared in the registry.
Credentials
Only NEMO_TOKEN is required, which is proportionate for a remote video service. However the skill will obtain an anonymous token from the external API if no token is provided, and the frontmatter mentions a local config path (~/.config/nemovideo/) which could be used to read or write tokens. The registry declared no config paths while the SKILL.md metadata did — this mismatch is a meaningful inconsistency about where credentials may be stored or read.
Persistence & Privilege
always is false and there is no install hook requesting permanent presence or modification of other skills. The skill does not request elevated platform privileges. It does create remote sessions on the vendor API, which is appropriate for its function and does not imply local persistence.
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-video-generation-github
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /free-video-generation-github 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 Video Generation GitHub skill. - Generate AI-powered videos from text prompts using cloud GPU backends. - Supports MP4, MOV, WebM, and GIF files up to 500MB. - Free tier: 100 credits for 7 days via quick anonymous-token flow (no registration required). - Exports videos in 1080p MP4 within 1-2 minutes; download available after render. - Includes workflows for upload, editing, previewing, and exporting videos. - Clear error messages, session handling, and multi-language prompt classification.
元数据
Slug free-video-generation-github
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 Video Generation Github 是什么?

generate text prompts into AI generated videos with this skill. Works with MP4, MOV, WebM, GIF files up to 500MB. developers use it for generating videos fro... 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 Video Generation Github?

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

Free Video Generation Github 是免费的吗?

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

Free Video Generation Github 支持哪些平台?

Free Video Generation Github 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 Video Generation Github?

由 linmillsd7(@linmillsd7)开发并维护,当前版本 v1.0.0。

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