Video Engineer
Purpose
Provides expertise in video processing, encoding, streaming, and infrastructure. Specializes in FFmpeg automation, adaptive streaming protocols, real-time communication, and building scalable video delivery systems.
When to Use
- Implementing video encoding and transcoding pipelines
- Setting up HLS or DASH streaming infrastructure
- Building WebRTC applications for real-time video
- Automating video processing with FFmpeg
- Optimizing video quality and compression
- Creating video thumbnails and previews
- Implementing video analytics and metadata extraction
- Building video player integrations
Quick Start
Invoke this skill when:
- Implementing video encoding and transcoding pipelines
- Setting up HLS or DASH streaming infrastructure
- Building WebRTC applications for real-time video
- Automating video processing with FFmpeg
- Optimizing video quality and compression
Do NOT invoke when:
- Building general web applications → use fullstack-developer
- Creating animated GIFs → use slack-gif-creator
- Media file analysis only → use multimodal-analysis
- Image processing without video → use appropriate skill
Decision Framework
Video Engineering Task?
├── On-Demand Streaming → HLS/DASH with adaptive bitrate
├── Live Streaming → Low-latency HLS or WebRTC
├── Real-Time Communication → WebRTC with STUN/TURN
├── Batch Processing → FFmpeg pipeline automation
├── Quality Optimization → Codec selection + encoding params
└── Video Analytics → Metadata extraction + scene detectionCore Workflows
1. Adaptive Streaming Setup
- Analyze source video specifications
- Define quality ladder (resolutions, bitrates)
- Configure encoder settings per quality level
- Generate HLS/DASH manifests
- Set up CDN for segment delivery
- Implement player with ABR support
- Monitor playback quality metrics
2. FFmpeg Processing Pipeline
- Define input sources and formats
- Build filter graph for transformations
- Configure encoding parameters
- Handle audio/video synchronization
- Implement error handling and retries
- Parallelize for throughput
- Validate output quality
3. WebRTC Implementation
- Set up signaling server
- Configure STUN/TURN servers
- Implement peer connection handling
- Manage media tracks and streams
- Handle network adaptation (simulcast, SVC)
- Implement recording if needed
- Monitor connection quality metrics
Best Practices
- Use hardware encoding (NVENC, QSV) when available for speed
- Implement adaptive bitrate for variable network conditions
- Pre-generate all quality levels for on-demand content
- Use appropriate codecs for use case (H.264 compatibility, H.265/AV1 efficiency)
- Set keyframe intervals appropriate for seeking and ABR switching
- Monitor and alert on encoding queue depth and latency
Anti-Patterns
- Single bitrate streaming → Always use adaptive bitrate
- Ignoring audio sync → Verify A/V alignment after processing
- Oversized segments → Keep HLS segments 2-10 seconds
- No error handling → FFmpeg can fail; implement retries
- Hardcoded paths → Parameterize for different environments