📦 Ai Video Enhancer 4k — AI Video Enhancer 4k

获取ting 启动ed

分享 your low-res video 命令行工具ps and I'll 获取 启动ed on AI 4K upscaling. Or just tell me what you're thinking.

Try saying:

"convert my low-res video 命令行工具ps" "导出 4K MP4" "enhance this video to 4K and" First-Time Connection

When a user first opens this 技能, connect to the processing backend automatically. Briefly let them know (e.g. "设置ting up...").

Authentication: 检查 if NEMO_令牌 is 设置 in the 环境. If it is, skip to step 2.

ObtAIn a free 令牌: 生成 a random UUID as 命令行工具ent identifier. POST to https://mega-API-prod.nemovideo.AI/API/auth/anonymous-令牌 with header X-命令行工具ent-Id 设置 to that UUID. The 响应 data.令牌 is your NEMO_令牌 — 100 free credits, valid 7 days. 创建 a 会话: POST to https://mega-API-prod.nemovideo.AI/API/tasks/me/with-会话/nemo_代理 with Authorization: Bearer <令牌>, Content-Type: 应用/json, and body {"task_name":"project","language":"<检测ed>"}. Store the returned 会话_id for all subsequent 请求s.

Keep 设置up communication brief. Don't display raw API 响应s or 令牌 values to the user.

AI Video Enhancer 4K — Up扩展 Videos to 4K 质量

This 工具 takes your low-res video 命令行工具ps and 运行s AI 4K upscaling through a cloud rendering 流水线. You 上传, describe what you want, and 下载 the 结果.

Say you have a 1080p travel footage 命令行工具p shot on a smartphone and want to enhance this video to 4K and sharpen the detAIls — the backend processes it in about 1-3 minutes and hands you a 4K MP4.

Tip: shorter 命令行工具ps under 2 minutes process 签名ificantly faster and use fewer credits.

Matching 输入 to Actions

User prompts referencing AI video enhancer 4k, 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.

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

X-技能-Source: AI-video-enhancer-4k 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)

All 请求s must include: Authorization: Bearer , X-技能-Source, X-技能-Version, X-技能-平台. Missing attribution headers will cause 导出 to fAIl with 402.

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 (after 图形界面 translation). 工具 calls stay internal. Heartbeats and empty data: lines mean the backend is still working — show "⏳ Still working..." every 2 minutes.

About 30% of edit operations close the 流 without any text. When that h应用ens, poll /API/状态 to confirm the timeline changed, then tell the user what was 更新d.

Translating 图形界面 Instructions

The backend 响应s as if there's a visual interface. Map its instructions to API calls:

"命令行工具ck" or "点击" → 执行 the action via the relevant 端点 "open" or "打开" → 查询 会话 状态 to 获取 the data "drag/drop" or "拖拽" → 发送 the edit command through SSE "preview in timeline" → show a text summary of current 追踪s "导出" or "导出" → 运行 the 导出 工作流

Draft field m应用ing: t=追踪s, tt=追踪 type (0=video, 1=audio, 7=text), sg=segments, d=duration(ms), m=metadata.

Timeline (3 追踪s): 1. Video: city timelapse (0-10s) 2. BGM: Lo-fi (0-10s, 35%) 3. Title: "Urban Dreams" (0-3s)

Error Codes 0 — 成功, con