mux-video
Mux Video infrastructure skill for designing, ingesting, transcoding/packaging, playback ID policy, live streaming, clipping, and observability with Mux Data. Use when architecting or operating Mux-based video pipelines, live workflows, playback security, or diagnosing playback issues.
Best use case
mux-video is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Mux Video infrastructure skill for designing, ingesting, transcoding/packaging, playback ID policy, live streaming, clipping, and observability with Mux Data. Use when architecting or operating Mux-based video pipelines, live workflows, playback security, or diagnosing playback issues.
Teams using mux-video should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/mux-video/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How mux-video Compares
| Feature / Agent | mux-video | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Mux Video infrastructure skill for designing, ingesting, transcoding/packaging, playback ID policy, live streaming, clipping, and observability with Mux Data. Use when architecting or operating Mux-based video pipelines, live workflows, playback security, or diagnosing playback issues.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Mux Video (Optimal) Skill Domain: Video Infrastructure & Delivery Primary Platform: Mux Target Level: Senior / Staff / Platform Architect Philosophy: Video is infrastructure. Reliability beats novelty. Analytics validate reality. --- ## 0. Prime Directive Mux Video exists to deliver video correctly, everywhere, under real-world conditions — not to feel fast in development. All decisions optimize for: - playback reliability - predictable latency - measurable experience - operational sanity --- ## 1. Canonical Mental Model ### What Mux Video Is - Managed video pipeline: ingest → transcode → package → distribute → secure - Abstracts FFmpeg complexity, CDN orchestration, ABR logic, and global delivery variance ### What Mux Video Is Not - A CMS - A player - A social platform - A monetization engine --- ## 2. Asset Model (Source of Truth) ### Assets - Canonical representation of media - Immutable once created - Represent media, not intent - Spawn many playback surfaces ### Design Rule - One asset → many experiences ### Asset Lifecycle - Ingest (upload or live record) - Transcode - Package (HLS / DASH) - Expose via Playback IDs - Observe via Mux Data --- ## 3. Control Planes (Separation of Concerns) Mux controls: - ingest stability - transcoding - packaging - global delivery You control: - identity - entitlements - playback authorization - business rules - monetization logic Failure to respect control planes causes: - security leaks - brittle playback - un-debuggable outages --- ## 4. Ingest Strategy (Critical) ### On-Demand Ingest - File upload (API or direct upload) - Deterministic quality - Preferred for premium content ### Live Ingest - RTMP only (by design) - Encoder quality determines everything downstream ### Live Rule - If the encoder is unstable, the stream is already lost ### Encoder Best Practices (Non-Negotiable) - Constant frame rate - GOP ≤ 2s (especially if clipping) - Stable bitrate ladder - Clean audio track --- ## 5. Encoding & Renditions Mux automatically: - Generates adaptive bitrate ladders - Selects codecs - Tunes for device compatibility ### Encoding Truth - Mux can’t fix a bad source — only distribute it efficiently --- ## 6. Playback IDs (Exposure Layer) ### Playback IDs Are Access Keys - Define who can watch, for how long, and under what policy - Do not modify the asset ### Playback Policies - `public` → open access - `signed` → controlled access ### Security Rule - Secure the Playback ID, not the asset --- ## 7. Playback Policy Decision Guide Use `public` when: - content is free or marketing - no revenue or rights risk exists - embedding is unrestricted Use `signed` when: - content is premium - playback must expire - access is user, geo, or entitlement based - clips have monetization value --- ## 8. Playback URLs & Delivery Mux delivers: - HLS (.m3u8) - DASH (.mpd) - Thumbnails - Storyboards Mux handles: - CDN selection - regional routing - device compatibility ### Latency Philosophy - On-demand → stability > speed - Live → latency is a tradeoff curve - There is no free low-latency lunch --- ## 9. Live Streaming (Operational Reality) Live is a distributed failure generator. Expect: - packet loss - dropped frames - network variance - device heterogeneity Mux mitigates — it does not eliminate. ### Live Best Practices - Always auto-record - Always monitor ingest - Always test encoder profiles - Never assume “it’ll be fine” --- ## 10. Live Latency Reality - Ultra-low latency increases failure sensitivity - Lower latency reduces buffer safety - Buffering is a reliability feature, not a bug Choose latency based on: - audience tolerance - interaction requirements - failure cost --- ## 11. Asset Clipping (First-Class Skill) ### Clipping Model Clips are derivative assets defined by: - source asset - start_time - end_time ### Rules - Source asset is immutable - Clips are disposable - Clips have independent analytics ### Why Clipping Matters - highlights - previews - modular content - monetization tiers - social repurposing ### Precision Constraint Clip accuracy depends on keyframe placement and encoder GOP size. Design accordingly. --- ## 12. Player Responsibility Boundary Mux delivers streams. The player renders video, reports telemetry, and controls UX. ### Rule - A bad player can sabotage a perfect pipeline --- ## 13. Observability Hook (Mux Data Dependency) Mux Video without Mux Data is a blind system. ### Requirement Every production playback must: - report sessions - surface QoE metrics No exceptions. --- ## 14. Observability Escalation Ladder 1. Playback failure rate increase 2. Startup time regression 3. Rebuffer ratio spike 4. Device or browser correlation 5. Region-specific anomalies 6. Ingest window correlation If you start debugging elsewhere, you’re guessing. --- ## 15. Operational Playbooks ### Playback Issues - Validate playback ID - Check startup time - Inspect error rates - Segment by device and browser - Correlate with ingest timing ### Live Stream Failure - Inspect encoder logs - Validate RTMP stability - Compare bitrate ladder output - Check regional impact - Fallback to recording --- ## 16. Anti-Patterns - Treating assets like content objects - Editing video “in Mux” - Ignoring encoder configuration - Using public playback IDs for premium content - Shipping unobserved video --- ## 17. Cost Reality Mux optimizes delivery cost. You control: - asset volume - clip proliferation - playback duration - entitlement abuse Unbounded playback equals silent spend. --- ## 18. Scaling Model Mux scales: - ingest - transcoding - delivery You scale: - auth - identity - entitlements - metadata - business logic Mux provides delivery truth. OpenClaw provides ownership, rights, access, and monetization intelligence. --- ## 19. Operational Fluency Signals You’ve mastered Mux Video when you can: - diagnose playback failures from metrics alone - design live streams for failure tolerance - atomize long-form content into clips at scale - secure playback without user friction - treat video as infrastructure, not media --- ## 20. Extension Points (Next Skills) - Mux Data (deep analytics) - Live highlight automation - Signed playback architectures - Clip-to-revenue attribution - AI-driven QoE optimization
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