Ffmpeg Mp4 Converter

Tired of wrestling with incompatible video formats, bloated files, or hours of manual conversion work? The ffmpeg-mp4-converter skill automates the entire pr...

Security Scan

The skill claims to be a cloud-backed FFmpeg converter and its requested NEMO_TOKEN credential and upload flows are coherent with that purpose, but several design choices and missing provenance (unknown backend/homepage, instructions to hide tokens, and filesystem probing) raise privacy and transparency concerns.

Purpose & Capability

The skill's name and description describe cloud-based video transcoding. Requiring a NEMO_TOKEN for a remote API and sending uploads to a transcoding backend is consistent with that purpose.

Instruction Scope

Runtime instructions tell the agent to upload local files or URLs to an external API, create sessions, and store a session_id — which is expected — but also direct the agent to: (1) write a UUID to ~/.config/ffmpeg-mp4-converter/client_id, (2) detect install path (~/.clawhub/, ~/.cursor/skills/) to set X-Skill-Platform, and (3) explicitly avoid showing raw API responses or token values to the user. The filesystem probing and the explicit hiding guidance broaden the agent's access and reduce transparency, increasing privacy/exfiltration risk.

Install Mechanism

No install spec and no code files (instruction-only). That minimizes on-disk code risk — nothing is downloaded or extracted by the skill itself.

Credentials

The skill only requests one credential (NEMO_TOKEN), which is proportionate to a cloud transcoding service. However, the SKILL.md also documents a flow to programmatically obtain an anonymous token and then treat it as NEMO_TOKEN; it is unclear whether the skill expects the platform to permanently set the env var or just hold the token in memory. The metadata claims NEMO_TOKEN is primary, which is reasonable but should be explicit about storage and lifetime to avoid unexpected persistence of credentials.

Persistence & Privilege

always:false and no install actions. The skill can be invoked autonomously (platform default), which increases blast radius if misused, but this is normal. The skill does write a client_id to ~/.config and may store session state locally; those writes are limited to its own config path.

What to consider before installing

This skill routes your video files and metadata to an external service (mega-api-prod.nemovideo.ai) and requires a NEMO_TOKEN. That is coherent for a cloud transcoder, but you should be cautious because: (1) the skill has no homepage or provenance information — you can’t verify the operator or privacy policy; (2) it instructs the agent to hide raw API responses and token values, which reduces transparency; (3) it probes local install/config paths to set attribution headers (reads ~/.clawhub/ or ~/.cursor/skills/), which is unrelated to video content and could leak environment details. Before installing, consider: - Do you trust the external service to handle your videos? Don’t send private/sensitive videos without reviewing the service’s privacy/terms. - Prefer using local ffmpeg if confidentiality is required; local conversion avoids external uploads. - Ask the skill author for provenance (homepage, owner identity, privacy policy), and for clarity on where tokens/session IDs are stored and for how long. - If you proceed, avoid setting a long-lived NEMO_TOKEN in a global environment variable; prefer ephemeral tokens limited to the session and inspect any saved files (~/.config/ffmpeg-mp4-converter/client_id, session storage). Additional information that would move this toward 'benign': a public homepage or GitHub repo, a clear privacy policy, and explicit statements about token storage and what metadata is sent with uploads.

Getting Started

Welcome! I'm your FFmpeg MP4 Converter assistant — ready to help you convert, compress, and reformat video files into MP4 with exactly the settings you need. Tell me what you're working with and I'll get it done.

Try saying:

• "Convert all MKV files in my downloads folder to MP4 using H.264 with a bitrate of 4000k"
• "I have a MOV file from my iPhone that's 2GB — can you convert it to MP4 and compress it under 500MB without losing too much quality?"
• "Batch convert a folder of WebM screen recordings to 1080p MP4 with AAC audio at 192kbps"

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 3.

1. Client identifier: Look for ~/.config/ffmpeg-mp4-converter/client_id. If missing, generate a UUID and save it there. This file stores only a UUID for rate-limit tracking.
2. Obtain a free token: POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with header X-Client-Id set to your identifier. The response data.token is your NEMO_TOKEN — 100 free credits, valid 7 days.
3. 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.

Convert Any Video to MP4 Without the Headache

Video format chaos is real. You've got footage from a drone in MKV, screen recordings in MOV, client files in AVI, and a deadline in two hours. Manually converting each file through a GUI tool is slow, inconsistent, and error-prone — especially when you need specific codecs, bitrates, or output resolutions.

The ffmpeg-mp4-converter skill solves this by giving you a conversational interface to one of the most powerful video processing engines in existence. Just describe what you need — the input format, desired quality, frame rate, audio settings — and the skill generates and executes the right FFmpeg command for your situation. No syntax memorization, no documentation diving.

Whether you're preparing videos for web delivery, archiving raw footage in a space-efficient format, or standardizing a library of mixed-format clips, this skill handles the heavy lifting. It supports single-file conversions and bulk jobs alike, with output options that match real-world publishing requirements across YouTube, Vimeo, mobile platforms, and broadcast workflows.

Routing Your Conversion Requests

When you submit a video file or URL, the skill parses your source format, target codec preferences, and any FFmpeg flags you specify, then routes the job to the appropriate transcoding pipeline based on container type and resolution.

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 Transcoding Backend Reference

All conversion jobs run on a distributed FFmpeg cloud backend that handles remuxing, re-encoding, and codec normalization — including H.264/AAC output, bitrate control, and keyframe alignment — without touching your local CPU. The API accepts multipart uploads or direct stream URLs and returns a signed download link to your finished MP4 upon job completion.

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

• X-Skill-Source: ffmpeg-mp4-converter
• X-Skill-Version: from frontmatter version
• X-Skill-Platform: detect from install path (~/.clawhub/ → clawhub, ~/.cursor/skills/ → cursor, else unknown)

All requests must include: Authorization: Bearer <NEMO_TOKEN>, X-Skill-Source, X-Skill-Version, X-Skill-Platform. Missing attribution headers will cause export to fail with 402.

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.

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.

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)

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

Troubleshooting Common Conversion Issues

If your converted MP4 has no audio, the source file likely uses an audio codec — like AC3 or DTS — that needs explicit transcoding. Specify -acodec aac in your request and the skill will handle the remapping automatically.

Green or corrupted frames often point to a codec mismatch during stream copy. If you're seeing visual artifacts, ask the skill to force full re-encoding rather than using stream copy mode — this is slower but produces a clean output every time.

Oversized output files usually mean the bitrate wasn't constrained. Tell the skill your target file size or maximum bitrate and it will calculate the appropriate -b:v and -maxrate flags accordingly.

If a conversion stalls or fails on a specific file, it may be a corrupted container. Ask the skill to run an integrity check using FFprobe before attempting conversion — this identifies broken keyframes or missing index data that would cause FFmpeg to hang mid-process.

Best Practices for High-Quality MP4 Output

Always specify your intended use case when making a conversion request. A video destined for YouTube has very different optimal settings than one being archived or sent via email. The skill will recommend appropriate codec profiles — like H.264 High Profile for streaming or Baseline Profile for maximum device compatibility — based on your target.

For the best balance of quality and file size, request CRF (Constant Rate Factor) encoding rather than fixed bitrate when quality consistency matters more than exact file size. A CRF value between 18 and 23 produces excellent results for most MP4 deliverables.

When batch converting, always test on a single representative file first. Confirm the output looks correct before running the full job — this avoids wasting time converting hundreds of files with a misconfigured setting.

Preserve your original files until you've verified the converted MP4 plays correctly on your target device or platform. Ask the skill to include a verification step that checks the output file's duration and stream metadata against the source before marking a job complete.

Real-World Use Cases for FFmpeg MP4 Converter

Content creators pulling raw footage off cameras often deal with formats like AVCHD or MXF that editing platforms and social media sites reject outright. This skill converts those files to H.264 MP4 — the universal format accepted by every major platform — while preserving the original resolution and frame rate.

Developers building media pipelines use this skill to normalize uploaded video content before storage or processing. Instead of writing custom FFmpeg wrapper scripts for every new format, they describe the transformation in plain language and get consistent, production-ready output.

Archivists and educators working with legacy video collections — think old AVI or RealMedia files — use the converter to migrate their libraries to MP4 containers with modern codecs, dramatically reducing storage requirements while maintaining playback compatibility across current devices.

Streaming teams use it to generate multiple MP4 variants from a single master file: a 1080p version for desktop, a 720p version for mobile, and a low-bitrate preview for thumbnails — all in one batch job.