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mhogan2013-9

Video No Generator

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 video-no-generator
功能描述
Skip the learning curve of professional editing software. Describe what you want — remove the frame numbers or watermark numbers overlaid on my video — and g...
使用说明 (SKILL.md)

Getting Started

Share your video clips and I'll get started on video number removal. Or just tell me what you're thinking.

Try saying:

  • "remove my video clips"
  • "export 1080p MP4"
  • "remove the frame numbers or watermark"

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.

Video No Generator — Remove Numbers From Videos

Drop your video clips in the chat and tell me what you need. I'll handle the video number removal on cloud GPUs — you don't need anything installed locally.

Here's a typical use: you send a a 2-minute tutorial video with visible frame numbers, ask for remove the frame numbers or watermark numbers overlaid on my video, and about 30-60 seconds later you've got a MP4 file ready to download. The whole thing runs at 1080p by default.

One thing worth knowing — shorter clips with static number positions process significantly faster.

Matching Input to Actions

User prompts referencing video no generator, 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.

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

  • X-Skill-Source: video-no-generator
  • X-Skill-Version: from frontmatter version
  • X-Skill-Platform: detect from install path (~/.clawhub/clawhub, ~/.cursor/skills/cursor, else unknown)

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

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 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

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "remove the frame numbers or watermark numbers overlaid on my video" — concrete instructions get better results.

Max file size is 500MB. Stick to MP4, MOV, AVI, WebM for the smoothest experience.

Export as MP4 for widest compatibility.

Common Workflows

Quick edit: Upload → "remove the frame numbers or watermark numbers overlaid on my video" → Download MP4. Takes 30-60 seconds 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.

安全使用建议
Before installing, make sure you are comfortable sending selected videos to nemovideo.ai and using or creating a Nemo token. For sensitive content or paid accounts, verify the provider, monitor credits, and ask for confirmation before export or other credit-consuming actions.
功能分析
Type: OpenClaw Skill Name: video-no-generator Version: 1.0.0 The skill bundle is a functional integration for a video editing service hosted at mega-api-prod.nemovideo.ai. It provides structured instructions for an AI agent to manage authentication (including anonymous token acquisition), file uploads, and video processing tasks. While it requires access to a specific configuration path (~/.config/nemovideo/) and an environment variable (NEMO_TOKEN), these are consistent with its stated purpose of interfacing with the NemoVideo API, and no evidence of malicious intent or data exfiltration was found.
能力评估
Purpose & Capability
The stated purpose—removing numbers or overlays from videos—matches the cloud video upload, edit, and export workflow. The skill also documents broader video-editing actions such as export, audio, and timeline state, so users should understand it is not purely local number removal.
Instruction Scope
Instructions route user requests and some backend responses into API actions. This is purpose-aligned for a cloud editor, but exports and edits should remain tied to the user's explicit request.
Install Mechanism
There is no install script or local code to execute. The main provenance gap is that the registry lists the source as unknown and no homepage, while the skill depends on a remote API.
Credentials
Using NEMO_TOKEN and uploading media to the provider are proportionate for this cloud-rendering purpose. Users should avoid uploading sensitive videos unless they trust the provider.
Persistence & Privilege
No local persistence, background process, or privilege escalation is shown. The skill does create provider-side sessions and render jobs, and anonymous tokens are described as valid for 7 days.
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 video-no-generator
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /video-no-generator 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 Video No Generator v1.0.0. - Automatically removes overlaid frame numbers or watermark numbers from uploaded videos (MP4, MOV, AVI, WebM up to 500MB). - Simple upload and edit workflow, no manual editing needed; typical jobs complete in 30–60 seconds. - Seamless onboarding with automatic token retrieval and session setup. - Clear user prompts for uploading, editing, and exporting videos. - Supports batch and iterative editing with real-time cloud processing and status updates.
元数据
Slug video-no-generator
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

Video No Generator 是什么?

Skip the learning curve of professional editing software. Describe what you want — remove the frame numbers or watermark numbers overlaid on my video — and g... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 41 次。

如何安装 Video No Generator?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install video-no-generator」即可一键安装,无需额外配置。

Video No Generator 是免费的吗?

是的,Video No Generator 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Video No Generator 支持哪些平台?

Video No Generator 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 Video No Generator?

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

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