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Ai Video Editor Auto Cut

作者 tk8544-b · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install ai-video-editor-auto-cut
功能描述
Skip the learning curve of professional editing software. Describe what you want — automatically cut the silence, remove bad takes, and trim the clip to the...
使用说明 (SKILL.md)

Getting Started

Got raw video footage to work with? Send it over and tell me what you need — I'll take care of the AI auto cutting.

Try saying:

  • "edit a 10-minute unedited vlog recording into a 1080p MP4"
  • "automatically cut the silence, remove bad takes, and trim the clip to the best moments"
  • "automatically trimming long recordings into clean, tight videos for content creators and YouTubers"

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.

AI Video Editor Auto Cut — Auto Cut and Export Clean Videos

This tool takes your raw video footage and runs AI auto cutting through a cloud rendering pipeline. You upload, describe what you want, and download the result.

Say you have a 10-minute unedited vlog recording and want to automatically cut the silence, remove bad takes, and trim the clip to the best moments — the backend processes it in about 1-2 minutes and hands you a 1080p MP4.

Tip: shorter source clips under 5 minutes process significantly faster and yield more accurate cuts.

Matching Input to Actions

User prompts referencing ai video editor auto cut, 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.

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

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

  • X-Skill-Source: ai-video-editor-auto-cut
  • 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

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 → "automatically cut the silence, remove bad takes, and trim the clip to the best moments" → 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 "automatically cut the silence, remove bad takes, and trim the clip to the best moments" — 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 across platforms and devices.

安全使用建议
This skill appears benign for its stated purpose. Before installing, make sure you are comfortable sending selected videos or media URLs to the NemoVideo cloud backend and using or creating a NEMO_TOKEN for that service.
功能分析
Type: OpenClaw Skill Name: ai-video-editor-auto-cut Version: 1.0.0 The skill is a functional integration for a cloud-based video editing service (nemovideo.ai). It provides instructions for the agent to manage authentication, upload video files, and handle rendering tasks via the service's API. While it performs network requests and handles user-provided files, its behavior is transparently documented and strictly aligned with the stated purpose of automated video editing without evidence of malicious intent or unauthorized data access.
能力评估
Purpose & Capability
The cloud upload, AI editing, rendering, and export workflow matches the stated purpose of automatically cutting and exporting videos.
Instruction Scope
The instructions route user requests to specific NemoVideo API actions and include follow-up render polling; this appears scoped to the video-editing task.
Install Mechanism
There is no install spec or code to execute, but the registry lists the source as unknown and no homepage is provided, limiting provenance review.
Credentials
Use of NEMO_TOKEN and cloud API calls is expected for the service, but the token and uploaded files are sensitive and should be handled carefully.
Persistence & Privilege
The skill keeps a session_id for ongoing operations and says render jobs can be orphaned if the tab is closed; no hidden background persistence 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 ai-video-editor-auto-cut
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /ai-video-editor-auto-cut 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 AI Video Editor Auto Cut: - Automatically cuts silence, removes bad takes, and trims clips to highlights with a single prompt. - Supports video uploads (MP4, MOV, AVI, WebM) up to 500MB. - Fast cloud rendering — edited clips ready in 1–2 minutes. - Automatic session and API token management on first use. - Exports videos in high-quality MP4 (up to 1080p). - Ideal for creators seeking quick, hands-free edits.
元数据
Slug ai-video-editor-auto-cut
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

Ai Video Editor Auto Cut 是什么?

Skip the learning curve of professional editing software. Describe what you want — automatically cut the silence, remove bad takes, and trim the clip to the... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 34 次。

如何安装 Ai Video Editor Auto Cut?

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

Ai Video Editor Auto Cut 是免费的吗?

是的,Ai Video Editor Auto Cut 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Ai Video Editor Auto Cut 支持哪些平台?

Ai Video Editor Auto Cut 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 Ai Video Editor Auto Cut?

由 tk8544-b(@tk8544-b)开发并维护,当前版本 v1.0.0。

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