Video Audio Remover

Skip the learning curve of professional editing software. Describe what you want — remove all audio from this video and export it silent — and get silent MP4...

Security Scan

The skill's requirements and runtime instructions are consistent with a cloud-based video audio removal service that uses a single service token (NEMO_TOKEN) and API endpoints on nemovideo.ai; nothing in the SKILL.md asks for unrelated credentials or system-wide privileges.

Purpose & Capability

The name/description (remove audio, export silent MP4) match the SKILL.md instructions which call a nemo video processing API and upload video files for cloud processing. Requesting NEMO_TOKEN and using endpoints under mega-api-prod.nemovideo.ai is coherent with this purpose. Note: the SKILL.md frontmatter also references a config path (~/.config/nemovideo/) for attribution which is plausible for a client but is not reflected in the top-level registry metadata — an inconsistency to be aware of.

Instruction Scope

Instructions are explicit about connecting to the external API, uploading user video/audio files, creating sessions, polling status, and streaming SSEs — all expected for a cloud render workflow. The skill also instructs generating an anonymous token if NEMO_TOKEN is missing, saving session_id, and adding attribution headers (including detecting the install path to set X-Skill-Platform). Detecting install path and reading frontmatter for attribution implies the agent may inspect filesystem paths; this is understandable for attribution but worth noting as additional filesystem access.

Install Mechanism

This is an instruction-only skill with no install spec or code files; nothing is downloaded or written to disk by an installer. That is the lowest-risk install model.

Credentials

Only a single credential (NEMO_TOKEN) is declared as required and is appropriate for calling the described API. The SKILL.md also references a client UUID generation and uses session tokens that expire — expected for anonymous access. The frontmatter lists a config path (~/.config/nemovideo/) which the registry metadata did not list; this inconsistency is minor but should be clarified (the path would only be appropriate if it truly belongs to this service).

Persistence & Privilege

The skill is not always-on and uses normal autonomous invocation (disable-model-invocation is false), which is the platform default. It does not request elevation or modification of other skills or system-wide settings.

Assessment

This skill appears to do what it claims: it uploads your videos to a nemovideo.ai endpoint, removes audio server-side, and returns a download URL. Before installing, confirm you trust the destination (mega-api-prod.nemovideo.ai) because uploading video/audio sends potentially sensitive content off your device. Understand that the skill needs an active NEMO_TOKEN (or will obtain an anonymous token) — tokens grant access to the service, so treat them like credentials and avoid pasting them into untrusted UIs. Note the SKILL.md asks the agent to inspect install paths/frontmatter for attribution — if you prefer minimal filesystem access, ask the publisher to remove that behavior. If you want an offline alternative, consider local tools (e.g., ffmpeg) that remove audio without uploading files.

Getting Started

Share your video clips and I'll get started on AI audio removal. Or just tell me what you're thinking.

Try saying:

• "strip my video clips"
• "export 1080p MP4"
• "remove all audio from this video"

Automatic Setup

On first interaction, connect to the processing API before doing anything else. Show a brief status like "Setting things up...".

Token: If NEMO_TOKEN environment variable is already set, use it and skip to Session below.

Free token: Generate a UUID as client identifier, then POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with header X-Client-Id: <uuid>. The response field data.token becomes your NEMO_TOKEN (100 credits, 7-day expiry).

Session: POST to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer auth and body {"task_name":"project"}. Save session_id from the response.

Confirm to the user you're connected and ready. Don't print tokens or raw JSON.

Video Audio Remover — Remove Audio and Export Silent Videos

This tool takes your video clips and runs AI audio removal through a cloud rendering pipeline. You upload, describe what you want, and download the result.

Say you have a 2-minute interview video with background music and want to remove all audio from this video and export it silent — the backend processes it in about 20-40 seconds and hands you a 1080p MP4.

Tip: shorter clips process faster — split long videos before uploading.

Matching Input to Actions

User prompts referencing video audio remover, 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.

Skill attribution — read from this file's YAML frontmatter at runtime:

• X-Skill-Source: video-audio-remover
• X-Skill-Version: from frontmatter version
• X-Skill-Platform: detect from install path (~/.clawhub/ → clawhub, ~/.cursor/skills/ → cursor, else unknown)

Include Authorization: Bearer <NEMO_TOKEN> and all attribution headers on every request — omitting them triggers a 402 on export.

Error Handling

Code	Meaning	Action
0	Success	Continue
1001	Bad/expired token	Re-auth via anonymous-token (tokens expire after 7 days)
1002	Session not found	New session §3.0
2001	No credits	Anonymous: show registration URL with ?bind=<id> (get <id> from create-session or state response when needed). Registered: "Top up credits in your account"
4001	Unsupported file	Show supported formats
4002	File too large	Suggest compress/trim
400	Missing X-Client-Id	Generate Client-Id and retry (see §1)
402	Free plan export blocked	Subscription tier issue, NOT credits. "Register or upgrade your plan to unlock export."
429	Rate limit (1 token/client/7 days)	Retry in 30s once

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.

Translating GUI Instructions

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

• "click" or "点击" → execute the action via the relevant endpoint
• "open" or "打开" → query session state to get the data
• "drag/drop" or "拖拽" → send the edit command through SSE
• "preview in timeline" → show a text summary of current tracks
• "Export" or "导出" → run the export workflow

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)

Common Workflows

Quick edit: Upload → "remove all audio from this video and export it silent" → Download MP4. Takes 20-40 seconds 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.

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "remove all audio from this video and export it silent" — 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.