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mhogan2013-9

Llm For Video Editing

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 llm-for-video-editing
功能描述
Turn a 3-minute unedited interview recording into 1080p AI-edited videos just by typing what you need. Whether it's using natural language prompts to cut, tr...
使用说明 (SKILL.md)

Getting Started

Share your raw video footage and I'll get started on AI-assisted video editing. Or just tell me what you're thinking.

Try saying:

  • "edit my raw video footage"
  • "export 1080p MP4"
  • "cut the filler words, add b-roll"

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.

LLM for Video Editing — Edit Videos With Text Prompts

Drop your raw video footage in the chat and tell me what you need. I'll handle the AI-assisted video editing on cloud GPUs — you don't need anything installed locally.

Here's a typical use: you send a a 3-minute unedited interview recording, ask for cut the filler words, add b-roll transitions, and write a short summary caption for each scene, and about 1-2 minutes later you've got a MP4 file ready to download. The whole thing runs at 1080p by default.

One thing worth knowing — shorter, focused prompts like 'remove silences and add fade transitions' produce more accurate edits than vague requests.

Matching Input to Actions

User prompts referencing llm for video editing, 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.

Base URL: https://mega-api-prod.nemovideo.ai

Endpoint Method Purpose
/api/tasks/me/with-session/nemo_agent POST Start a new editing session. Body: {"task_name":"project","language":"\x3Clang>"}. Returns session_id.
/run_sse POST Send a user message. Body includes app_name, session_id, new_message. Stream response with Accept: text/event-stream. Timeout: 15 min.
/api/upload-video/nemo_agent/me/\x3Csid> POST Upload a file (multipart) or URL.
/api/credits/balance/simple GET Check remaining credits (available, frozen, total).
/api/state/nemo_agent/me/\x3Csid>/latest GET Fetch current timeline state (draft, video_infos, generated_media).
/api/render/proxy/lambda POST Start export. Body: {"id":"render_\x3Cts>","sessionId":"\x3Csid>","draft":\x3Cjson>,"output":{"format":"mp4","quality":"high"}}. Poll status every 30s.

Accepted file types: 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: llm-for-video-editing
  • 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.

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

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.

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

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)

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "cut the filler words, add b-roll transitions, and write a short summary caption for each scene" — concrete instructions get better results.

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

Export as MP4 with H.264 codec for the best balance of quality and file size.

Common Workflows

Quick edit: Upload → "cut the filler words, add b-roll transitions, and write a short summary caption for each scene" → 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.

安全使用建议
This skill uploads whatever you send (video/audio) to nemovideo.ai and uses an API token (either one you provide as NEMO_TOKEN or a short-lived anonymous token it can obtain). Only upload footage you are comfortable sending to an external service. If privacy or billing matters, supply your own NEMO_TOKEN tied to an account you control rather than using anonymous tokens. Be aware of the minor metadata mismatch about a local config path (~/.config/nemovideo/) — ask the author whether the skill will read or write that directory if you are concerned about local files. Finally, review nemovideo.ai's privacy/retention policy before sending sensitive content.
功能分析
Type: OpenClaw Skill Name: llm-for-video-editing Version: 1.0.0 The skill provides a functional interface for a cloud-based video editing service hosted at nemovideo.ai. It includes standard procedures for authentication, session management, and file uploads. While it instructs the agent to perform basic environment detection (checking for ~/.clawhub or ~/.cursor paths) to set attribution headers, this behavior is documented and serves a clear operational purpose. No indicators of data exfiltration, malicious execution, or harmful prompt injection were found.
能力评估
Purpose & Capability
Name and description match behaviour in SKILL.md: the skill routes uploads and edit requests to a remote nemovideo.ai rendering backend. The declared primary credential (NEMO_TOKEN) is appropriate for an API-driven service. Note: the SKILL.md frontmatter lists a config path (~/.config/nemovideo/) while the registry metadata showed no required config paths — this is a minor metadata mismatch but consistent with a client that may also check local config.
Instruction Scope
Runtime instructions are scoped to starting sessions, uploading user-provided media, streaming SSE responses, polling render status, and exporting downloads. The skill explicitly posts files and metadata to nemovideo.ai endpoints and instructs reading its own frontmatter/install path to populate attribution headers. There are no instructions to read unrelated system files or secrets beyond NEMO_TOKEN.
Install Mechanism
Instruction-only skill with no install spec or downloaded code. This minimizes disk-write/remote-exec risk. All runtime actions are network calls to the documented API.
Credentials
Only one credential is required (NEMO_TOKEN), which is proportional to a cloud API. The SKILL.md also references a config path in its metadata (possible local config lookup) — acceptable but the registry metadata and SKILL.md disagree about config path requirements. Confirm whether the skill will read or write ~/.config/nemovideo/ if that matters for you.
Persistence & Privilege
Skill is not always-enabled and does not request elevated platform privileges. It may create ephemeral anonymous tokens via the API and keep session IDs in memory; there is no instruction 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 llm-for-video-editing
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /llm-for-video-editing 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
LLM for Video Editing, version 1.0.0 — Initial Release - Instantly edit raw video footage into 1080p clips with simple text prompts (e.g., "cut filler words, add b-roll") - Full cloud-based workflow, no local software required; setup automatically connects with a free trial token - Supports uploads in major video and audio formats (mp4, mov, avi, webm, etc.) up to 500MB - Natural language to action mapping: cut, trim, add music, overlay text, and more, all from chat - Real-time job progress via event streaming; robust error and credit handling with clear user feedback
元数据
Slug llm-for-video-editing
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

Llm For Video Editing 是什么?

Turn a 3-minute unedited interview recording into 1080p AI-edited videos just by typing what you need. Whether it's using natural language prompts to cut, tr... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 112 次。

如何安装 Llm For Video Editing?

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

Llm For Video Editing 是免费的吗?

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

Llm For Video Editing 支持哪些平台?

Llm For Video Editing 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 Llm For Video Editing?

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

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