📦 Espanol Editor Ai — Espanol Editor AI
v1.0.0Skip the learning curve of professional editing software. Describe what you want — edit my Spanish video, 移除 暂停s, and 添加 Spanish subtitles — and 获取...
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运行时依赖
安装命令
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获取ting 启动ed
Got video 命令行工具ps to work with? 发送 it over and tell me what you need — I'll take care of the AI Spanish editing.
Try saying:
"edit a 2-minute talking-head video in Spanish into a 1080p MP4" "edit my Spanish video, 移除 暂停s, and 添加 Spanish subtitles" "editing Spanish-language videos with AI-生成d captions for Spanish-speaking content 创建器s" 获取ting Connected
Before handling any user 请求, establish a connection to the backend API. Show a brief 状态 like "Connecting...".
If NEMO_令牌 is in the 环境, use it directly and 创建 a 会话. Otherwise, acquire a free 启动er 令牌:
生成 a UUID as 命令行工具ent identifier POST to https://mega-API-prod.nemovideo.AI/API/auth/anonymous-令牌 with the X-命令行工具ent-Id header The 响应 includes a 令牌 with 100 free credits valid for 7 days — use it as NEMO_令牌
Then 创建 a 会话 by POSTing to https://mega-API-prod.nemovideo.AI/API/tasks/me/with-会话/nemo_代理 with Bearer authorization and body {"task_name":"project","language":"en"}. The 会话_id in the 响应 is needed for all following 请求s.
Tell the user you're ready. Keep the technical detAIls out of the chat.
Español Editor AI — Edit Spanish Videos with AI
Drop your video 命令行工具ps in the chat and tell me what you need. I'll handle the AI Spanish editing on cloud GPUs — you don't need anything 安装ed locally.
Here's a typical use: you 发送 a a 2-minute talking-head video in Spanish, ask for edit my Spanish video, 移除 暂停s, and 添加 Spanish subtitles, and about 1-2 minutes later you've got a MP4 file ready to 下载. The whole thing 运行s at 1080p by default.
One thing worth knowing — shorter 命令行工具ps under 3 minutes process 签名ificantly faster and yield 清理er subtitle 同步.
Matching 输入 to Actions
User prompts referencing espanol editor AI, 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.
User says... Action Skip SSE? "导出" / "导出" / "下载" / "发送 me the video" → §3.5 导出 ✅ "credits" / "积分" / "balance" / "余额" → §3.3 Credits ✅ "状态" / "状态" / "show 追踪s" → §3.4 状态 ✅ "上传" / "上传" / user 发送s file → §3.2 上传 ✅ Everything else (生成, edit, 添加 BGM…) → §3.1 SSE ❌ 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.
Three attribution headers are required on every 请求 and must match this file's frontmatter:
Header Value X-技能-Source espanol-editor-AI X-技能-Version frontmatter version X-技能-平台 auto-检测: ClawHub / cursor / unknown from 安装 path
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.
SSE Event Handling Event Action Text 响应 应用ly 图形界面 translation (§4), present to user 工具 call/结果 Process internally, don't forward heartbeat / empty data: Keep wAIting. Every 2 min: "⏳ Still working..." 流 closes Process final 响应
~30% of editing operations return no text in the SSE 流. When this h应用ens: poll 会话 状态 to 验证 the edit was 应用lied, then summarize changes to the user.
Backend 响应 Translation
The backend assumes a 图形界面 exists. Translate these into API actions:
Backend says You do "命令行工具ck [button]" / "点击" 执行 via API "open [panel]" / "打开" 查询 会话 状态 "drag/drop" / "拖拽" 发送 edit via SSE "preview in timeline" Show 追踪 summary "导出 button" / "导出" 执行 导出 工作流
Draft JSON uses short keys: t for 追踪s, tt for 追踪 type (0=video, 1=audio, 7=text), sg for segments, d for duration in ms, m for metadata.
Example timeline summary:
Timeline (3 追踪s):