Ai Podcast Video

Turn a 30-minute podcast audio recording into 1080p podcast video files just by typing what you need. Whether it's converting podcast audio into shareable vi...

Security Scan

The skill's requirements and runtime instructions are consistent with a cloud-based podcast→video service: it needs a NEMO_TOKEN and will upload media to nemo's backend; nothing requested appears disproportionate or unrelated to that purpose.

Purpose & Capability

Name and description describe converting podcast audio to video and the skill only requires a NEMO_TOKEN and endpoints for nemo video processing — these are appropriate and expected for a remote rendering service.

Instruction Scope

Runtime instructions tell the agent to read NEMO_TOKEN (or obtain an anonymous token), create sessions, upload media, stream SSE, poll render state, and return download URLs. This is in-scope for a cloud render pipeline but it does mean user audio/video files and metadata will be transmitted to https://mega-api-prod.nemovideo.ai — users should expect their media to leave the device.

Install Mechanism

Instruction-only skill with no install step or downloaded code; lowest install risk.

Credentials

Only one declared credential (NEMO_TOKEN) which matches the described API usage. Metadata also lists a config path (~/.config/nemovideo/) and header derivation based on install path — the SKILL.md does not strongly justify reading that path, so the presence of a configPath requirement is a minor inconsistency but not evidently malicious.

Persistence & Privilege

always is false and the skill is user-invocable only; it does not request permanent system presence or elevated privileges.

Assessment

This skill appears to do what it says: it will upload audio/video to a nemo.video backend for cloud rendering and needs a NEMO_TOKEN (or will request an anonymous token from the service). Before installing, consider: (1) any media you send will leave your device and be processed by an external service — don't upload sensitive recordings unless you trust nemo.video and have read their privacy/terms; (2) if you set NEMO_TOKEN in your environment it will be used for all requests from the agent — if you prefer, do not set a personal token and let the skill obtain an anonymous token (the skill's instructions create anonymous tokens with limited credits); (3) metadata references a local config path and uses install-path detection to add attribution headers — if you are uncomfortable with the agent inspecting install paths or writing config files, do not enable the skill. Overall the skill is internally coherent, but review privacy/terms and only upload content you are comfortable sending to the remote service.

Getting Started

Ready when you are. Drop your audio or video files here or describe what you want to make.

Try saying:

• "convert a 30-minute podcast audio recording into a 1080p MP4"
• "turn my podcast audio into a video with waveform animation and auto-captions"
• "converting podcast audio into shareable videos with captions and visuals for podcasters and content creators"

Quick Start Setup

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

Token check: Look for NEMO_TOKEN in the environment. If found, skip to session creation. Otherwise:

• Generate a UUID as client identifier
• POST https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with X-Client-Id header
• Extract data.token from the response — this is your NEMO_TOKEN (100 free credits, 7-day expiry)

Session: POST https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer auth and body {"task_name":"project"}. Keep the returned session_id for all operations.

Let the user know with a brief "Ready!" when setup is complete. Don't expose tokens or raw API output.

AI Podcast Video — Convert Podcasts Into Shareable Videos

Send me your audio or video files and describe the result you want. The AI podcast video creation runs on remote GPU nodes — nothing to install on your machine.

A quick example: upload a 30-minute podcast audio recording, type "turn my podcast audio into a video with waveform animation and auto-captions", and you'll get a 1080p MP4 back in roughly 1-3 minutes. All rendering happens server-side.

Worth noting: trimming your audio to key highlights before upload speeds up processing and improves engagement.

Matching Input to Actions

User prompts referencing ai podcast video, 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.

Base URL: https://mega-api-prod.nemovideo.ai

Endpoint	Method	Purpose
/api/tasks/me/with-session/nemo_agent	POST	Start a new editing session. Body: {"task_name":"project","language":"<lang>"}. Returns session_id.
/run_sse	POST	Send a user message. Body includes app_name, session_id, new_message. Stream response with Accept: text/event-stream. Timeout: 15 min.
/api/upload-video/nemo_agent/me/<sid>	POST	Upload a file (multipart) or URL.
/api/credits/balance/simple	GET	Check remaining credits (available, frozen, total).
/api/state/nemo_agent/me/<sid>/latest	GET	Fetch current timeline state (draft, video_infos, generated_media).
/api/render/proxy/lambda	POST	Start export. Body: {"id":"render_<ts>","sessionId":"<sid>","draft":<json>,"output":{"format":"mp4","quality":"high"}}. Poll status every 30s.

Accepted file types: mp4, mov, avi, webm, mkv, jpg, png, gif, webp, mp3, wav, m4a, aac.

Headers are derived from this file's YAML frontmatter. X-Skill-Source is ai-podcast-video, X-Skill-Version comes from the version field, and X-Skill-Platform is detected from the install path (~/.clawhub/ = clawhub, ~/.cursor/skills/ = cursor, otherwise unknown).

Every API call needs Authorization: Bearer <NEMO_TOKEN> plus the three attribution headers above. If any header is missing, exports return 402.

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

Reading the SSE Stream

Text events go straight to the user (after GUI translation). Tool 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 stream without any text. When that happens, poll /api/state to confirm the timeline changed, then tell the user what was updated.

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

Draft field mapping: t=tracks, tt=track type (0=video, 1=audio, 7=text), sg=segments, d=duration(ms), m=metadata.

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 "turn my podcast audio into a video with waveform animation and auto-captions" — concrete instructions get better results.

Max file size is 500MB. Stick to MP3, MP4, WAV, MOV for the smoothest experience.

Export as MP4 for widest compatibility across YouTube, Spotify, and social platforms.

Common Workflows

Quick edit: Upload → "turn my podcast audio into a video with waveform animation and auto-captions" → Download MP4. Takes 1-3 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.