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Ai Video Editor Davinci Resolve

作者 vcarolxhberger · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install ai-video-editor-davinci-resolve
功能描述
Get edited video files ready to post, without touching a single slider. Upload your raw video footage (MP4, MOV, AVI, MKV, up to 500MB), say something like "...
使用说明 (SKILL.md)

Getting Started

Ready when you are. Drop your raw video footage here or describe what you want to make.

Try saying:

  • "edit a 3-minute DaVinci Resolve project export into a 4K MP4"
  • "cut out silences, add transitions, and color grade the footage automatically"
  • "automating video edits for DaVinci Resolve users without manual timeline work for video editors and filmmakers"

Automatic Setup

On first interaction, connect to the processing API before doing anything else. Show a brief status like "Setting things up...".

Token: If NEMO_TOKEN environment variable is already set, use it and skip to Session below.

Free token: Generate a UUID as client identifier, then POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with header X-Client-Id: \x3Cuuid>. The response field data.token becomes your NEMO_TOKEN (100 credits, 7-day expiry).

Session: POST to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer auth and body {"task_name":"project"}. Save session_id from the response.

Confirm to the user you're connected and ready. Don't print tokens or raw JSON.

AI Video Editor DaVinci Resolve — Edit and Export Polished Videos

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

Say you have a 3-minute DaVinci Resolve project export and want to cut out silences, add transitions, and color grade the footage automatically — the backend processes it in about 1-2 minutes and hands you a 4K MP4.

Tip: export your DaVinci Resolve timeline as a flat MP4 before uploading for the smoothest processing.

Matching Input to Actions

User prompts referencing ai video editor davinci resolve, 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.

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

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

Header Value
X-Skill-Source ai-video-editor-davinci-resolve
X-Skill-Version frontmatter version
X-Skill-Platform auto-detect: clawhub / cursor / unknown from install path

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 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)

Common Workflows

Quick edit: Upload → "cut out silences, add transitions, and color grade the footage automatically" → 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 "cut out silences, add transitions, and color grade the footage automatically" — concrete instructions get better results.

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

H.264 codec gives the best balance of quality and file size for final exports.

安全使用建议
This skill calls an external, unverified API and asks you to upload video files and/or supply a NEMO_TOKEN. Before using it: (1) note the mismatch — the description promises 4K but the API docs within the skill limit output to ~1080p; don't assume 4K results. (2) Only upload non-sensitive test footage until you verify the service, privacy policy, and data retention. (3) Prefer creating and providing your own token (if you trust the service) rather than letting the agent mint one automatically. (4) Ask where tokens and session IDs are stored on your system (~/.config/nemovideo/ appears declared). (5) If you’re uncomfortable with the agent reading install paths or auto-detecting platform metadata, do not install or invoke the skill. If you want to proceed, verify the backend domain (mega-api-prod.nemovideo.ai) and ideally look for an authoritative homepage/terms or an official vendor before uploading real content.
功能分析
Type: OpenClaw Skill Name: ai-video-editor-davinci-resolve Version: 1.0.0 The skill is a functional integration for the nemovideo.ai cloud video editing service, providing instructions for an AI agent to automate video editing tasks like silence removal and color grading. It manages authentication via the NEMO_TOKEN environment variable or an anonymous token generation process, and it defines clear API interactions for session management, file uploads, and rendering. While it includes telemetry-like headers for platform attribution (X-Skill-Platform), its behavior is consistent with its stated purpose and lacks any indicators of malicious intent, unauthorized data access, or harmful execution.
能力评估
Purpose & Capability
The skill claims 'download 4K MP4' in the description, but the Cloud Render Pipeline section limits outputs to H.264 up to 1080x1920 — a direct capability mismatch. The name references 'DaVinci Resolve' yet the instructions only accept flat MP4 exports (no Resolve project integration). The declared config path (~/.config/nemovideo/) appears despite this being an instruction-only skill; it's unclear what will be stored there.
Instruction Scope
Runtime instructions tell the agent to: call an external API (mega-api-prod.nemovideo.ai) to mint anonymous tokens, create sessions, POST SSE messages, and upload local files (multipart form '@/path'). They also require adding three attribution headers and 'auto-detect' an install path to set X-Skill-Platform, which implies reading agent/install paths or environment — more filesystem/environment access than a minimal upload tool. The flow is otherwise consistent with a cloud-editing service, but the header/platform detection and ambiguous storage of session/token raise scope and privacy questions.
Install Mechanism
Instruction-only skill with no install spec or code to download; nothing will be written by an installer. This has the lowest install risk.
Credentials
Only one env var (NEMO_TOKEN) and a config path (~/.config/nemovideo/) are declared, which is proportionate for a cloud API client. However the skill will mint an anonymous token on demand via the external API if none is provided, and it’s unclear where tokens/sessions are persisted. The required attribution headers and suggested platform auto-detection imply the agent may read environment or filesystem metadata.
Persistence & Privilege
always is false and the skill is user-invocable; it does not request permanent inclusion or modification of other skills. Session tokens and render jobs live on the service side; closing the client may orphan jobs as documented. Autonomous invocation is allowed (the platform default) but is not combined here with elevated privileges.
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-davinci-resolve
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /ai-video-editor-davinci-resolve 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 for DaVinci Resolve — edit and export polished videos with AI automation. - Upload raw video footage and specify edit instructions for automatic, cloud-based processing. - Supports cutting silences, adding transitions, color grading, and exporting 4K MP4s. - No manual DaVinci Resolve workflow needed — cloud pipeline handles all edits and rendering. - Handles user authentication, session management, file uploads, and result exports through the API. - Multiple input formats supported; credits system in place for usage tracking.
元数据
Slug ai-video-editor-davinci-resolve
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 Davinci Resolve 是什么?

Get edited video files ready to post, without touching a single slider. Upload your raw video footage (MP4, MOV, AVI, MKV, up to 500MB), say something like "... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 75 次。

如何安装 Ai Video Editor Davinci Resolve?

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

Ai Video Editor Davinci Resolve 是免费的吗?

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

Ai Video Editor Davinci Resolve 支持哪些平台?

Ai Video Editor Davinci Resolve 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 Davinci Resolve?

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

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