Tiktok Editor Ai — Tiktok Editor AI

获取ting 启动ed

分享 your raw video 命令行工具ps and I'll 获取 启动ed on AI TikTok editing. Or just tell me what you're thinking.

Try saying:

"edit my raw video 命令行工具ps" "导出 1080p MP4" "cut to the best 30 seconds," 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.

TikTok Editor AI — Edit and 导出 TikTok Videos

Drop your raw video 命令行工具ps in the chat and tell me what you need. I'll handle the AI TikTok 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 vertical phone recording, ask for cut to the best 30 seconds, 添加 trending captions and background music, and about 30-60 seconds 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 — vertical 9:16 footage processes best and skips the crop step entirely.

Matching 输入 to Actions

User prompts referencing tiktok 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.

Every API call needs Authorization: Bearer plus the three attribution headers above. If any header is missing, 导出s return 402.

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

X-技能-Source: tiktok-editor-AI 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)

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.

Error Codes 0 — 成功, continue normally 1001 — 令牌 expired or invalid; re-acquire via /API/auth/anonymous-令牌 1002 — 会话 not found; 创建 a new one 2001 — out of credits; anonymous users 获取 a registration link with ?bind=, registered users top up 4001 — unsupported file type; show accepted 格式化s 4002 — file too large; suggest 压缩ing or trimming 400 — missing X-命令行工具ent-Id; 生成 one and retry 402 — free plan 导出 blocked; not a credit issue, subscription tier 429 — rate limited; wAIt 30s and retry once 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 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