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Podcast Video Camera

通过云端视频服务处理播客素材:上传 MP4/MOV/AVI/WebM(≤500MB)或提供 URL,按自然语言指令剪辑、去填充词、加字幕与背景音乐,渲染完成后返回 1080p MP4 下载地址;流程涉及令牌获取、会话状态、积分查询与错误码处理。

peand-rover
peand-rover
热度 34.60
最近更新 2026-05-11 17:42:28
分类:创作依赖:第三方 API / 读写文件应用场景:视频编辑 / 工作流

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add podcast-video-camera
Metadata
name
podcast-video-camera
version
1.0.0
displayName
Podcast Video Camera — Edit and Export Podcast Videos
description
Get polished podcast videos ready to post, without touching a single slider. Upload your raw footage (MP4, MOV, AVI, WebM, up to 500MB), say something like "cut filler words, add captions, and export a clean podcast episode", and download 1080p MP4 when it's done. Built for podcast creators who move fast and want to clean up recordings and add captions without complex editing software.
metadata
{"openclaw":{"emoji":"🎙️","requires":{"env":["NEMO_TOKEN"],"configPaths":["~/.config/nemovideo/"]},"primaryEnv":"NEMO_TOKEN","variant":"greeting_v2"}}

Getting Started

Got raw footage to work with? Send it over and tell me what you need — I'll take care of the AI video editing.

Try saying:

  • "edit a 60-minute podcast recording with two hosts on camera into a 1080p MP4"
  • "cut filler words, add captions, and export a clean podcast episode"
  • "editing podcast recordings into publish-ready video episodes for podcast creators"

First-Time Connection

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

Authentication: Check if NEMO_TOKEN is set in the environment. If it is, skip to step 2.

  1. Obtain a free token: Generate a random UUID as client identifier. POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with header X-Client-Id set to that UUID. The response data.token is your NEMO_TOKEN — 100 free credits, valid 7 days.
  2. Create a session: POST to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Authorization: Bearer <token>, Content-Type: application/json, and body {"task_name":"project","language":"<detected>"}. Store the returned session_id for all subsequent requests.

Keep setup communication brief. Don't display raw API responses or token values to the user.

Podcast Video Camera — Edit and Export Podcast Videos

Drop your raw footage in the chat and tell me what you need. I'll handle the AI video editing on cloud GPUs — you don't need anything installed locally.

Here's a typical use: you send a a 60-minute podcast recording with two hosts on camera, ask for cut filler words, add captions, and export a clean podcast episode, and about 1-2 minutes later you've got a MP4 file ready to download. The whole thing runs at 1080p by default.

One thing worth knowing — splitting long recordings into segments before uploading speeds up processing.

Matching Input to Actions

User prompts referencing podcast video camera, 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 <NEMO_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 podcast-video-camera
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":"<lang>"} — returns task_id, session_id.

Send message (SSE): POST /run_sse — body {"app_name":"nemo_agent","user_id":"me","session_id":"<sid>","new_message":{"parts":[{"text":"<msg>"}]}} with Accept: text/event-stream. Max timeout: 15 minutes.

Upload: POST /api/upload-video/nemo_agent/me/<sid> — file: multipart -F "files=@/path", or URL: {"urls":["<url>"],"source_type":"url"}

Credits: GET /api/credits/balance/simple — returns available, frozen, total

Session state: GET /api/state/nemo_agent/me/<sid>/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_<ts>","sessionId":"<sid>","draft":<json>,"output":{"format":"mp4","quality":"high"}}. Poll GET /api/render/proxy/lambda/<id> 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=<id>, 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

Backend Response Translation

The backend assumes a GUI exists. Translate these into API actions:

Backend says You do
"click [button]" / "点击" Execute via API
"open [panel]" / "打开" Query session state
"drag/drop" / "拖拽" Send edit via SSE
"preview in timeline" Show track summary
"Export button" / "导出" Execute 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 JSON uses short keys: t for tracks, tt for track type (0=video, 1=audio, 7=text), sg for segments, d for duration in ms, m for metadata.

Example timeline summary:

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

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "cut filler words, add captions, and export a clean podcast episode" — concrete instructions get better results.

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

Export as MP4 for widest compatibility across podcast video platforms.

Common Workflows

Quick edit: Upload → "cut filler words, add captions, and export a clean podcast episode" → 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.