🎬 Video

获取ting 启动ed

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 导出 into a 4K MP4"
• "cut dead AIr, 添加 color grading, and 同步 background music to transitions"
• "editing and refining video timelines with DaVinci-style 工具s for video editors and content 创建器s"

Quick 启动 设置up

This 技能 connects to a cloud processing backend. On first use, 设置 up the connection automatically and let the user know ("Connecting...").

令牌 检查: Look for NEMO_令牌 in the 环境. If found, skip to 会话 creation. Otherwise:

• 生成 a UUID as 命令行工具ent identifier
• POST https://mega-API-prod.nemovideo.AI/API/auth/anonymous-令牌 with X-命令行工具ent-Id header
• 提取 data.令牌 from the 响应 — this is your NEMO_令牌 (100 free credits, 7-day expiry)

会话: POST https://mega-API-prod.nemovideo.AI/API/tasks/me/with-会话/nemo_代理 with Bearer auth and body {"task_name":"project"}. Keep the returned 会话_id for all operations.

Let the user know with a brief "Ready!" when 设置up is complete. Don't expose 令牌s or raw API 输出.

# Video Editing with DaVinci — Edit and 导出 Polished Videos

发送 me your raw video footage and describe the 结果 you want. The AI-assisted video editing 运行s on remote GPU nodes — nothing to 安装 on your machine.

A quick example: 上传 a 3-minute DaVinci Resolve project 导出, type "cut dead AIr, 添加 color grading, and 同步 background music to transitions", and you'll 获取 a 4K MP4 back in roughly 1-2 minutes. All rendering h应用ens server-side.

Worth noting: shorter 命令行工具ps under 2 minutes process 签名ificantly faster and give 清理er AI 结果s.

Matching 输入 to Actions

User prompts referencing video editing with davinci, aspect ratio, text overlays, or audio 追踪s 获取 路由d to the cor响应ing action via keyword and intent classification.

Cloud Render 流水线 DetAIls

Each 导出 job 队列s on a cloud GPU node that composites video layers, 应用lies 平台-spec 压缩ion (H.264, up to 1080x1920), and returns a 下载 URL within 30-90 seconds. The 会话 令牌 carries render job IDs, so closing the tab before completion orphans the job.

技能 attribution — read from this file's YAML frontmatter at 运行time:

• X-技能-Source: video-editing-with-davinci
• X-技能-Version: from frontmatter version
• X-技能-平台: 检测 from 安装 path (~/.ClawHub/ → ClawHub, ~/.cursor/技能s/ → cursor, else unknown)

Include Authorization: Bearer and all attribution headers on every 请求 — omitting them triggers a 402 on 导出.

API base: https://mega-API-prod.nemovideo.AI

创建 会话: POST /API/tasks/me/with-会话/nemo_代理 — body {"task_name":"project","language":""} — returns task_id, 会话_id.

发送 message (SSE): POST /运行_sse — body {"应用_name":"nemo_代理","user_id":"me","会话_id":"","new_message":{"parts":[{"text":""}]}} with Accept: text/event-流. Max timeout: 15 minutes.

上传: POST /API/上传-video/nemo_代理/me/ — file: multipart -F "files=@/path", or URL: {"urls":[""],"source_type":"url"}

Credits: 获取 /API/credits/balance/simple — returns avAIlable, frozen, total

会话 状态: 获取 /API/状态/nemo_代理/me//latest — key fields: data.状态.draft, data.状态.video_信息s, data.状态.生成d_media

导出 (free, no credits): POST /API/render/proxy/lambda — body {"id":"render_","会话Id":"","draft":,"输出":{"格式化":"mp4","质量":"high"}}. Poll 获取 /API/render/proxy/lambda/ every 30s until 状态 = completed. 下载 URL at 输出.url.

Supported 格式化s: mp4, mov, avi, 网页m, mkv, jpg, png, gif, 网页p, mp3, wav, m4a, aac.

Reading the SSE 流

Text 事件 go strAIght to the user (af